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Gone are the days when graphic design was solely focused on the obvious graphic elements of a product like its packaging and marketing materials. Today the impact of technology and the digital revolution is huge on how we communicate, the way we work, and even the way we socialize. Graphic design is no exception to this change. Technology plays a major role in the creation of digital work available in many fields. For example, portfolio design, presentations, signage, logos, websites, animations, and even architectural production have all traveled far since the dawn of the digital revolution. This graphic designing evolution has enabled brands with greater exposure online and enabled users with engaging and interesting graphics. Recently, we had a conversation with Iman Ahmed, an Adobe Certified Partner and Instructor and CompTIA Certified Technical Trainer on the current graphic design industry, various design tools, and how is the future. Iman has 19+ years of solid international experience in delivering the skills of various applications, from Architecture, Graphic design, Infographics, Motion Graphics, Photo Editing, Magazine and book design, video production, 3D Modelling. We also discussed her recently published book, Mastering Adobe InDesign 2020, a step-by-step guide to learn InDesign Framework, Workspace, Project setup, Master pages, Pages, Text, among other core features. It also explores new features in InDesign 2020 release and how are they useful to graphic designing professionals. Adobe InDesign, a preferred choice for designing text-heavy documents Adobe’s Creative Suite of tools offers graphic designers all kinds of solutions needed to create professional and engaging graphics. From photo editing to typography tools to sound design Adobe literally has everything covered for any type of design project. So how can one compare Adobe InDesign with other tools, specifically with Illustrator and Photoshop? Iman clarifies, “Adobe applications has no relation to the user's level of experience, it is all about the purpose. Adobe Photoshop is the best application for photo editing, while Adobe Illustrator is the best used for vector design, and Adobe InDesign is created for layout design, as it has tools and options that facilitate the layout design process.” Graphic designers use InDesign when they need to layout a multi-page, text-heavy piece. For example in print or digital, InDesign is used to layout text. It is a one-stop solution for designing a magazine, brochure or a booklet. Out of the three applications, InDesign has the most robust typesetting features available. It also integrates with Adobe Digital Publishing Solution, allowing designers to create fully interactive e-books, magazines, and other digital publications. Key features in Adobe InDesign 2020 version Adobe released InDesign 2020 version in November 4th this year. This release brings significant upgrades and changes as per Adobe InDesign user requests. With this release, InDesign tool now supports SVG file formats. Graphic designers will be able to use infinitely customizable fonts or variable fonts within InDesign. There is a more efficient way to place lines, or “rules,”  between columns of text. Additionally, it includes improvements to InDesign’s core performance and launch times for up to 25% faster. Iman says about the new release, “Adobe InDesign fixed a lot of bugs in this release, and offered an improvement to resolve document corruption. Opening a particular file, saving and closing it has become faster in this release. And text editing is also faster than earlier releases. There are a bunch of Text features which have been added to Adobe InDesign 2020 release, such as variable fonts and column rules, a new feature in spell check, plus five new languages - Thai, Burmese, Lao, Khmer, and Sinhalaare are supported in this release.” She further discussed why InDesign is considered to be a tool of choice for Multi-page projects, and how the Master page feature is one of the key features of InDesign. She says, “Master page is one of the most powerful features of Adobe InDesign, it acts as a template, as master page will host all common features that are needed to repeat in document pages, and any changes made in the master page, will reflect into all document pages that follow this particular master page. Hence, a professional designer has to be smart enough in designing the document, to decide how many master pages to use in one document.” You can read Chapter 6 of this book, Mastering Adobe InDesign 2020, to know more about Master page feature and how to create different Master pages for your document. Iman’s education in architecture designing is foundational to her graphics designing career Iman has studied architecture design, and she mentions that her undergraduate and postgraduate education in architecture has contributed to her success as an Engineering Applications’ Instructor and as a Graphic Design Instructor and designer. Iman talks about her graphic designing journey and shares this quote from Frank Lloyd Wright, “The mother of art is architecture. Without an architecture of our own we have no soul of our own civilization.” Iman takes pride and feels lucky to study the mother of all arts, which guided her strongly in her graphic designer career. She mentions that the steps she took can be common with everyone who feels passionate about learning graphic design. Undoubtedly architecture was the cornerstone in her pathway, but gaining knowledge of tools is important too to accomplish for a good designer. She says, “My graphic design path literally started when I used Adobe Photoshop for the first time in an architecture presentation. Then I felt more interested to learn more about Adobe Photoshop and photo editing. One year later, I started my career as an Adobe Photoshop instructor, and a graphic designer for the same training center, beside my work as an architect.” “I taught myself more about Photoshop, plus a bunch of applications using offline help, as YouTube was not available. For years, I kept searching for design ideas, learn new applications, teach what I learnt and practice a lot. Honestly, this is not enough, to be a professional graphic designer, I still need more.” On her inspiration to write Mastering Adobe InDesign CC 2020 Iman says, “As a trainer, it is the most joyful moment when you help others to learn and improve. For more than 19 years, I am teaching people from the Middle East, Europe, USA, Canada, Australia, Asia and Africa. Their nationalities are different, but their enthusiasm looks the same. My dream is to spread knowledge and help more and more people to learn. So for me writing a book about Adobe InDesign was a great chance to share my knowledge and experience to a broader audience. In my book I have shared 19 years of experience, it is not only about InDesign, but also about the design process. It helps both designers and non-designers to work more efficient with Adobe InDesign tool and makes them aware of the steps before implementing the design. It covers various exercises  and examples to enhance reader's skills, and sharpens the skills of intermediate and expert users.” The growth story of the graphic design industry is far from over According to the U.S. Bureau of Labor Statistics, the graphic design field is expected to grow by 3% from 2018 to 2028, which is slower than average. More graphic designers are jumping to be freelancers as compensation is a major issue in a full time job role. Companies pay lesser salaries at a junior and intermediate level roles in the graphic design department. Hence, designers prefer freelancing as they can sell their creatives and design templates to multiple clients with less effort. Additionally, there are perks to being your own boss. For instance, you get to set your own working hours and choose your own jobs. As freelancers are in millions all over the world, it gets difficult to track their number in the statistics while calculating the industry growth rate. And it becomes an outlier in showing the actual industry growth results thus it shows slower than the average. Iman shared her thoughts which are on similar lines, she says, “Competition is aggressive everywhere, the first reason for this competition is the unemployment phenomenon that we are facing in this decade. Globalization and platforms that offer freelancing designers to hire from everywhere negatively affects the field and market. As some designers are cheap, as per their economy and currency standards.” But on the other hand, working at a firm has its own perks. The company will be responsible for maintaining your work environment, purchasing equipment and software, and building a client base. And graphic designers will be more likely to work regular hours for a predictable paycheck. On this Iman says, “If you work hard on your portfolio, and know how to network well with others, definitely you will be hired by reputed organizations.” If you're not sure of what to start with, it's always a good idea to intern at a small or medium firm and gain experience in the industry. Then get to know your work style, and choose what fits best. Graphic designers ditching corporate culture for freelancing Out of more than 250,000 graphic designers in the U.S., almost 25% are self-employed. This number is expected to rise in the coming years due to millennials ditching the corporate culture for a freelance lifestyle. On this we asked Iman about a typical graphic designer career graph and what some pathways available are. We also asked if professionals require a degree to enter this field. Iman believes that ”graphic designer career path varies, and different routes can be the right pathway to the graphic design career. Your route depends on your target, would you like to be professional in logo design, branding, web design, packaging design, book and magazine design, some of them, all of them or even more!” Further she discussed major steps that a graphic designer needs to take to start their journey. Step 1: Learn graphic designing Iman recommends learning graphic design in a school which is specialized in graphic designing. She says, “learning applications is not enough, applications are only tools that will help you to finish your work in a smart and easy way.  But without design fundamentals and concrete foundations, a good design will not be achieved.” Step 2: Get inspired by others “Don't Reinvent The Wheel, learn from people with experience to save time,” says Iman. She suggests, “to watch others’ work, about the latest styles of design which will inspire and be another source of knowledge to learn from.” Step 3: Practice, practice, practice! Practice makes one perfect! Iman advises to “create more than one idea for a design and find every possible way to create a design that forms the message you need to communicate. You must sketch, re-sketch, refine your sketch, implement it and edit it, for an exclusively perfect design.” Step 4: Read more books Iman adds, “read more and more books about graphic design theory, history, elements of design, color theories and other books about designing. These books have in depth knowledge and a rich experience to develop designing skills.” Step 5: Selecting the right tool for designing Iman emphasises on being smart in selecting the tool for designing. She says, “you need to be aware, which application will help you in what task. For instance, one can create logos using Adobe Illustrator, then edit photos in Adobe Photoshop, and finally collect design elements smartly in Adobe InDesign.” On graphic designing future and what to expect next When it comes to the future of graphic design, the big thing on everyone’s mind is animation and VR. Digital media is rapidly becoming the future of graphic design. For more we asked Iman on what to expect next. She explains, “All the fields such as print media, web designs, animation and VFX are ways to form a message using visual communications’ tools, and send it to the target audience, and it depends on the field to field, there is a proper way to use, based on the purpose. Whereas in the design industry, we cannot predict what will happen tomorrow, but for sure, the competition in using visual communication tools, will always remain a major factor in the improvement of all design fields.” Get Iman’s book, Mastering Adobe InDesign 2020 today to start exploring the InDesign workspace, the different menus, and functions, along with gaining insights into planning and executing a design. You’ll also get hands-on with creating your first project, focusing on aspects such as working with text, images, and shapes. Author Bio Iman Ahmed is an Architect who loves Art and Design, she started practicing Graphic Design in 1999, and during her 20 years of experience as a graphic designer, she created a lot of designs and magazines using Adobe Photoshop, Illustrator and Indesign. Her passion for teaching was genuine and that drives her to work hard to be a special kind of trainers. She started her teaching career in 2004, she is an Adobe user since 1999 and an Adobe Certified Instructor since 2008. Iman is a CompTIA Certified Technical Trainer since 2008, and she had been interviewed by CompTIA in November 2016 as a model of a special kind of trainers, who studied and applied CompTIA ( CTT+) program in a special way that successfully polished her skills and teaching style. Iman is a classroom Instructor and an online trainer who delivers courses in the Middle East and UK. Following Capital One data breach, GitHub gets sued and AWS security questioned by a U.S. Senator British Airways set to face a record-breaking fine of £183m by the ICO over customer data breach US Customs and Border Protection reveal data breach that exposed thousands of traveler photos and license plate images

[dropcap]S[/dropcap]elf-service Business Intelligence is the buzzword everyone's talking about today. It gives modern business users the ability to find unique insights from their data without any hassle. Amidst a myriad of BI tools and platforms out there in the market, Microsoft’s Power BI has emerged as a powerful, all-encompassing BI solution - empowering users to tailor and manage Business Intelligence to suit their unique needs and scenarios. [author title="Brett Powell"]A Microsoft Power BI partner, and the founder and owner of Frontline Analytics LLC., a BI and analytics research and consulting firm. Brett has contributed to the design and development of Microsoft BI stack and Power BI solutions of diverse scale and complexity across the retail, manufacturing, financial, and services industries. He regularly blogs about the latest happenings in Microsoft BI and Power BI features at Insight Quest. He is also an organizer of the Boston BI User Group.[/author]   In this two part interview Brett talks about his new book, Microsoft Power BI Cookbook, and shares his insights and expertise in the area of BI and data analytics with a particular focus on Power BI. In part one, Brett shares his views on topics ranging from what it takes to be successful in the field of BI & data analytics to why he thinks Microsoft is going to lead the way in shaping the future of the BI landscape. In part two of the interview, he shares his expertise with us on the unique features that differentiate Power BI from other tools and platforms in the BI space. Key Takeaways Ease of deployment across multiple platforms, efficient data-driven insights, ease of use and support for a data-driven corporate culture are factors to consider while choosing a Business Intelligence solution for enterprises. Power BI leads in self-service BI because it’s the first Software-as-a-Service (SaaS) platform to offer ‘End User BI’ where anyone, not just a business analyst, can leverage powerful tools to obtain greater value from data. Microsoft Power BI has been identified as a leader in Gartner’s Magic Quadrant for BI and Analytics platforms, and provides a visually rich and easy to access interface that modern business users require. You can isolate report authoring from dataset development in Power BI, or quickly scale up or down a Power BI dataset as per your needs. Power BI is much more than just a tool for reports and dashboards. With a thorough understanding of the query and analytical engines of Power BI, users can customize more powerful and sustainable BI solutions. Part One Interview Excerpts - Power BI from a Bird’s Eye View On choosing the right BI solution for your enterprise needs What are some key criteria one must evaluate while choosing a BI solution for enterprises? How does Power BI fare against these criteria as compared with other leading solutions from IBM, Oracle and Qlikview? Enterprises require a platform which can be implemented on their terms and adapted to their evolving needs. For example, the platform must support on-premises, cloud, and hybrid deployments with seamless integration allowing organizations to both leverage on-premises assets as well as fully manage their cloud solution. Additionally, the platform must fully support both corporate business intelligence processes such as staged deployments across development and production environments as well as self-service tools which empower business teams to contribute to BI projects and a data driven corporate culture. Furthermore, enterprises must consider the commitment of the vendor to BI and analytics, the full cost of scaling and managing the solution, as well as the vendors’ vision for delivering emerging capabilities such as artificial intelligence and natural language. Microsoft Power BI has been identified as a leader in Gartner’s Magic Quadrant for BI and Analytics platforms based on both its currently ability to execute as well as its vision. Particularly now with Power BI Premium, the Power BI Report Server, and Power BI embedded offerings, Power BI truly offers organizations the ability to tailor and manage BI to their unique needs and scenarios. Power BI’s mobile application, available on all common platforms (iOS, Android) in addition to continued user experience improvements in the Power BI service provides a visually rich and common interface for the ‘anytime access’ that modern business users require. Additionally, since Power BI’s self-service authoring tool of Power BI Desktop shares the same engine as SQL Server Analysis Services, Power BI has a distinct advantage in enabling organizations to derive value from both self-service and corporate BI. The BI landscape is very competitive and other vendors such as Tableau and Qlikview have obtained significant market share. However, as organizations fully consider the features distinguishing the products in addition to the licensing structures and the integration with Microsoft Azure, Office 365, and common existing BI assets such as Excel and SQL Server Reporting Services and Analysis Services, they will (and are) increasingly concluding that Power BI provides a compelling value. On the future of BI and why Brett is betting on Microsoft to lead the way Self-service BI as a trend has become mainstream. How does Microsoft Power BI lead this trend? Where do you foresee the BI market heading next i.e., are there other trends we should watch out for?  Power BI leads in self-service BI because it’s the first software as a service (SaaS) platform to offer ‘End User BI’ in which anyone, not just a business analyst, can leverage powerful tools to obtain greater value from data. This ‘third wave’ of BI, as Microsoft suggests, further follows and supplements the first and second waves of BI in Corporate and self-service BI, respectively. For example, Power BI’s Q & A experience with natural language queries and integration with Cortana goes far beyond the traditional self-service process of an analyst finding field names and dragging and dropping items on a canvas to build a report. Additionally, an end user has the power of machine learning algorithms at their fingertips with features such as Quick Insights now built into Power BI Desktop. Furthermore, it’s critical to understand that Microsoft has a much larger vision for self-service BI than other vendors. Self-service BI is not exclusively the visualization layer over a corporate IT controlled data model – it’s also the ability for self-service solutions to be extended and migrated to corporate solutions as part of a complete BI strategy. Given their common underlying technologies, Microsoft is able to remove friction between corporate and self-service BI and allows organizations to manage modern, iterative BI project lifecycles.    On staying ahead of the curve in the data analytics & BI industry For someone just starting out in the data analytics and BI fields, what would your advice be? How can one keep up with the changes in this industry? I would focus on building a foundation in the areas which don’t change frequently such as math, statistics, and dimensional modeling. You don’t need to become a data scientist or a data warehouse architect to deliver great value to organizations but you do need to know the basic tools of storing and analysing data to answer business questions. To succeed in this industry over time you need to consistently invest in your skills in the areas and technologies relevant to your chosen path. You need to hold yourself accountable for becoming a better data professional and this can be accomplished by certification exams, authoring technical blogs, giving presentations, or simply taking notes from technical books and testing out tools and code on your machine. For hard skills I’d recommend standard SQL, relational database fundamentals, data warehouse architecture and dimensional model design, and at least a core knowledge of common data transformation processes and/or tools such as SQL Server Integration Services (SSIS) and SQL stored procedures. You’ll need to master an analytical language as well and for Microsoft BI projects that language is increasingly DAX. For soft skills, you need to move beyond simply looking for a list of requirements for your projects. You need to learn to become flexible and active – you need to become someone who offers ideas and looks to show value and consistently improve projects rather than just ‘deliver requirements’. You need to be able to have both a deeply technical conversation but also have a very practical conversation with business stakeholders. You need to able to build relationships with both business and IT. You don’t ever want to dominate or try to impress anyone but if you’re truly passionate about your work then this will be visible in how you speak about your projects and the positive energy you bring to work every day and to your ongoing personal development.   If you enjoyed this interview, check out Brett’s latest book, Microsoft Power BI Cookbook. In part two of the interview, Brett shares 5 Power BI features to watch out for, 7 reasons to choose Power BI to build enterprise solutions and more. Visit us tomorrow to read part two of the interview.

As software systems become more distributed, reliability and resiliency have become more and more important. This is one of the reasons why we've seen the emergence of chaos engineering - unreliability causes downtime which, in turn, also causes downtime. And downtime costs money. The impact of downtime is particularly significant for huge organizations that depend on the resilience and reliability of their platforms and applications. Take Uber - not only does the simplicity of the user experience hide its astonishing complexity, but it also has to ensure it can manage that complexity in a way that's reliable. A ride-hailing app couldn't be anywhere near as successful as Uber if it didn't work even if it had 1% downtime. Building resilient software is difficult But actually building resilient systems is difficult. We've recently seen how Uber uses distributed tracing to build more observable systems which can help improve reliability and resiliency in the last podcast episode with Yuri Shkuro but in this week's podcast we're diving even deeper into resiliency with Vilas Veeraraghavan, who's Director of Engineering at Walmart Labs. Vilas has experience at Netflix, the company where chaos engineering originated, but at Walmart, he's been playing a central role in bringing a more evolved version of chaos engineering - which Vilas calls resiliency engineering - to the organization. In this episode we discuss: Whether chaos engineering and resiliency engineering are for everyone Cultural challenges How to get buy-in Getting tooling right https://soundcloud.com/packt-podcasts/walmart-labs-director-of-engineering-vilas-veeraraghavan-on-chaos-engineering-resiliency   “You do not want to get up in the middle of the night get on the call with the VP of engineering and blurt out saying I have no idea what happened. Your answer should be I know exactly what happened because we have tested this exact scenario multiple times. We developed a recipe for it, and here is what we can do… that gives you as an engineer, the power to be able to stand up and say I know exactly what’s going on, I’ll fix it, don’t worry, we’re not going to cause an outage.”

Tableau is one of the leading BI tools used by data science and business intelligence professionals today. You can not only use it to create powerful data visualizations but also use it to extract actionable insights for quality decision making thanks to the plethora of tools and features it offers. We recently interviewed Joshua Milligan, a Tableau Zen Master and the author of the book, Learning Tableau. Joshua takes us on an insightful journey into Tableau explaining why it is the Google of data visualization. He tells us all about its current and future focus areas such as Geospatial analysis and automating workflows, the exciting new features and tools such as Hyper, Tableau Prep among other topics.  He also gives us a preview of things to come in his upcoming book. Author’s Bio Joshua Milligan, author of the bestselling book, Learning Tableau, has been with Teknion Data Solutions since 2004 and currently serves as a principal consultant.  With a strong background in software development and custom .NET solutions, he brings a blend of analytical and creative thinking to BI solutions. Joshua has been named Tableau Zen Master, the highest recognition of excellence from Tableau Software not once but thrice. In 2017, Joshua competed as one of three finalists in the prestigious Tableau Iron Viz competition. As a Tableau trainer, mentor, and leader in the online Tableau community, he is passionate about helping others gain insights from their data. His work has been featured multiple times on Tableau Public’s Viz of the Day and Tableau’s website. He also shares frequent Tableau (and Maestro) tips, tricks, and advice on his blog VizPainter.com. Key Takeaways Tableau is perfectly tailored for business intelligence professionals given its extensive list of offerings from data exploration to powerful data storytelling. The drag-and-drop interface allows you to understand data visually thus enabling anyone to perform and share self service data analytics with colleagues in seconds. Hyper is new in-memory data engine designed for powerful query analytical processing on complex datasets. Tableau Prep, a new data preparation tool released with Tableau 2018.1, allows users to easily combine, shape, analyze and clean the data for compelling analytics. Tableau 2018.1 is expected to bring new geospatial tools, enterprise enhancements to Tableau Server, and new extensions and plugins to create interactive dashboards. Tableau users can expect to see artificial intelligence and machine learning becoming major features in both Tableau and Tableau Prep - thus deriving insights based on users behavior across the enterprise. Full Interview There are many enterprise software for business intelligence, how does Tableau compare against the others? What are the main reasons for Tableau's popularity? Tableau's paradigm is what sets it apart from others. It's not just about creating a chart or dashboard. It's about truly having a conversation with the data: asking questions and seeing instant results as you drag and drop to get new answers that raise deeper questions and then iterating. Tableau allows for a flow of thought through the entire cycle of analytics from data exploration through analysis to data storytelling.  Once you understand this paradigm, you will flow with Tableau and do amazing things! There's a buzz in the developer's community that Tableau is the Google of data visualization. Can you list the top 3-5 features in Tableau 10.5 that are most appreciated by the community? How do you use Tableau in your day-to-day work? Tableau 10.5 introduced Hyper - a next-generation data engine that really lays a foundation for enterprise scaling as well as a host of exciting new features and Tableau 2018.1 builds on this foundation.  One of the most exciting new features is a completely new data preparation tool - Tableau Prep. Tableau Prep complements Tableau Desktop and allows users to very easily clean, shape, and integrate their data from multiple sources.  It’s intuitive and gives you a hands-on, instant feedback paradigm for data preparation in a similar way to what Tableau Desktop enables with data visualization. Tableau 2018.1 also includes new geospatial features that make all kinds of analytics possible.  I’m particularly excited about support for the geospatial data types and functions in SQL Server which have allowed me to dynamically draw distances and curves on maps.  Additionally, web authoring in Tableau Server is now at parity with Tableau Desktop. I use Tableau every day to help my clients see and understand their data and to make key decisions that drive new business, avoid risk, and find hidden opportunities.  Tableau Prep makes it easier to access the data I need and shape it according to the analysis I’ll be doing. Tableau offers a wide range of products to suit their users' needs. How does one choose the right product from their data analytics or visualization need? For example, what are the key differences between Tableau Desktop, Server and Public? Are there any plans for a unified product for the Tableau newbie in the near future? As a consultant at Teknion Data Solutions (a Tableau Gold Partner), I work with clients all the time to help them make the best decisions around which Tableau offering best meets their needs.  Tableau Desktop is the go-to authoring tool for designing visualizations and dashboards. Tableau Server, which can be hosted on premises or in the cloud, gives enterprises and organizations the ability to share and scale Tableau.  It is now at near parity with Tableau Desktop in terms of authoring. Tableau Online is the cloud-based, Tableau managed solution. Tableau Public allows for sharing public visualizations and dashboards with a world-wide audience. How good is Tableau for Self-Service Analytics / automating workflows? What are the key challenges and limitations? Tableau is amazing for this. Combined with the new data prep tool - Tableau Prep - Tableau really does offer users, across the spectrum (from business users to data scientists), the ability to quickly and easily perform self-service analytics. As with any tool, there are definitely cases which require some expertise to reach a solution. Pulling data from an API or web-based source or even sometimes structuring the data in just the right way for the desired analysis are examples that might require some know-how. But even there, Tableau has the tools that make it possible (for example, the web data connector) and partners (like Teknion Data Solutions) to help put it all together. In the third edition of Learning Tableau, I expand the scope of the book to show the full cycle of analytics from data prep and exploration to analysis and data storytelling. Expect updates on new features and concepts (such as the changes Hyper brings), a new chapter focused on Tableau Prep and strategies for shaping data to perform analytics, and new examples throughout that span multiple industries and common analytics questions. What is the development roadmap for Tableau 2018.1? Are we expecting major feature releases this year to overcome some of the common pain areas in business intelligence? I'm particularly excited about Tableau 2018.1. Tableau hasn't revealed everything yet, but things such as new geospatial tools and features, enterprise enhancements to Tableau Server, the new extensions API, new dashboard tools, and even a new visualization type or two look to be amazing! Tableau is working a lot in the geospatial domain coming up with new plugins/connectors and features. Can we expect Tableau to further strengthen their support for spatial data? What are the other areas/domains that Tableau is currently focused on? I couldn't say what the top 3-5 areas are - but you are absolutely correct that Tableau is really putting some emphasis on geospatial analytics.  I think the speed and power of the Hyper data engine makes a lot of things like this possible. Although I don't have any specific knowledge beyond what Tableau has publicly shared, I wouldn't be surprised to see some new predictive and statistical models and expansion of data preparation abilities. What's driving Tableau to Cloud? Can we expect more organizations adopting Tableau on Cloud? There has been a major shift to the cloud by organizations. The ability to manage, scale, ensure up-time, and save costs are driving this move and that in turn makes Tableau's cloud-based offerings very attractive. What does Tableau's future hold, according to you? For example, do you see machine learning and AI-powered analytics platform transformation? Or can we expect Tableau entering the IoT and IIoT domain? Tableau demonstrated a concept around NLQ at the Tableau Conference and has already started building in a few machine learning features. For example, Tableau now recommends joins based on what is  learns from behavior of users across the enterprise. Tableau Prep has been designed from the ground-up with machine learning in mind. I fully expect to see AI and machine learning become major features in both Tableau and Tableau Prep – but true to Tableau’s paradigm, they will complement the work of the analyst and allow for deeper insight without obscuring the role that humans play in reaching that insight.  I'm excited to see what is announced next! Give us a sneak peek into the book you are currently writing "Learning Tableau 2018.1, Third Edition", expected to be released in the 3rd Quarter this year. What should our readers get most excited about as they wait for this book? Although the foundational concepts behind learning Tableau remain the same, I'm excited about the new features that have been released or will be as I write.  Among these are a couple of game-changers such as the new geospatial features and the new data prep tool: Tableau Prep. In addition to updating the existing material, I'll definitely have a new chapter or two covering those topics! If you found this interview to be interesting, make sure you check out other insightful articles on business intelligence: Top 5 free Business Intelligence tools [Opinion] Tableau 2018.1 brings new features to help organizations easily scale analytics [News] Ride the third wave of BI with Microsoft Power BI [Interview - Part 1] Unlocking the secrets of Microsoft Power BI [Interview - Part 2] How Qlik Sense is driving self-service Business Intelligence [Interview]

UX designer has become a popular job role with tech recruiters, anxious to give roles a little extra sparkle and some additional sex appeal. But has UX become inflated as a term? Is its value being diluted? Although paying close attention to the experience of users can only be a good thing, are we doing a disservice to the discipline by treating it as a buzzword or a fad? If we pretend something's sexy, how serious can we really be about it? Whatever the problems with the uses and abuses of UX today, a landscape characterized by dark patterns and digital detox is one that's certainly not that comfortable for users. That means UX design is arguably more important than ever. What UX design is... and what it isn't To get to the heart of what UX design is, as well as what it isn't, we spoke to Will Grant (@wgx) a UX Designer who has experience working with a range of clients on products that have found their way into the lives of millions of users around the world. Will is the author of 101 UX Principles, a definitive design guide that explores key issues in the field.  In the podcast episode, we discussed: What UX is and isn't The UX process - what UX designers actually do The key skills a UX designer needs Originality v. templating Whether developers need to write code What conversational UI means for UX Can good design really save the world? Or should we quit the bullshit? Listen here: https://soundcloud.com/packt-podcasts/can-good-design-really-save-the-world-will-grant-on-the-importance-of-ux-in-2019 Read next: Will Grant’s 10 commandments for effective UX Design

Delivering Business Intelligence solutions to over 40000 customers worldwide, there is no doubt that Qlik has established a strong foothold in the analytics market for many years now. With the self-service capabilities of Qlik Sense, you can take better and more informed decisions than ever before. From simple data exploration to complex dashboarding and cloud-ready, multi-platform analytics, Qlik Sense gives you the power to find crucial, hidden insights from the depths of your data. We got some fascinating insights from our interview with two leading Qlik community members, Ganapati Hegde and Kaushik Solanki, on what Qlik Sense offers to its users and what the future looks like for the BI landscape. [box type="shadow" align="" class="" width=""] Ganapati Hegde Ganapati is an engineer by background and carries an overall IT experience of over 16 years. He is currently working with Predoole Analytics, an award-winning Qlik partner in India, in the presales role. He has worked on BI projects in several industry verticals and works closely with customers, helping them with their BI strategies. His experience in other aspects of IT, like application design and development, cloud computing, networking, and IT Security - helps him design perfect BI solutions. He also conducts workshops on various technologies to increase user awareness and drive their adoption. Kaushik Solanki Kaushik has been a Qlik MVP (Most Valuable Player) for the years 2016 and 2017 and has been working with the Qlik technology for more than 7 years now. An Information technology engineer by profession, he also holds a master’s degree in finance. Having started his career as a Qlik developer, Kaushik currently works with Predoole Analytics as the Qlik Project Delivery Manager and is also a certified QlikView administrator. An active member of Qlik community, his great understanding of project delivery - right from business requirement to final implementation, has helped many businesses take valuable business decisions.[/box] In this exciting interview, Ganapati and Kaushik take us through a compelling journey in self-service analytics, by talking about the rich features and functionalities offered by Qlik Sense. They also talk about their recently published book ‘Implementing Qlik Sense’ and what the readers can learn from it. Key Takeaways With many self-service and guided analytics features, Qlik Sense is perfectly tailored to business users Qlik Sense allows you to build customized BI solutions with an easy interface, good mobility, collaboration, focus on high performance and very good enterprise governance Built-in capabilities for creating its own warehouse, a strong ETL layer and a visualization layer for creating intuitive Business Intelligence solutions are some of the strengths of Qlik Sense With support for open APIs, the BI solutions built using Qlik Sense can be customized and integrated with other applications without any hassle. Qlik Sense is not a rival to Open Source technologies such as R and Python. Qlik Sense can be integrated with R or Python to perform effective predictive analytics ‘Implementing Qlik Sense’ allows you to upgrade your skill-set from a Qlik developer to a Qlik Consultant. The end goal of the book is to empower the readers to implement successful Business Intelligence solutions using Qlik Sense. Complete Interview There has been a significant rise in the adoption of Self-service Business Intelligence across many industries. What role do you think visualization plays in self-service BI? In a vast ocean of self-service tools, where do you think Qlik stands out from the others? As Qlik says visualization alone is not the answer. A strong backend engine is needed which is capable of strong data integration and associations. This then enables businesses to perform self-service and get answers to all their questions. Self-service plays an important role in the choice of visualization tools, as business users today no longer want to go to IT every time they need changes. Self service enable business users to quickly build their own visualization with simple drag and drop.   Qlik stands out from the rest in its capability to bring in multiple data sources, enabling users to easily answers questions. Its unique associative engine allows users to find hidden insights. The open API allows easy customization and integrations which is a must for enterprises. Data security and governance is one of the best in Qlik. What are the key differences between QlikView and Qlik Sense? What are the factors crucial to building powerful Business Intelligence solutions with Qlik Sense? QlikView and Qlik Sense are similar yet different. Both share the same engine. On one hand, QlikView is a developer’s delight with the options it offers, and on the other hand, Qlik Sense with its self-service is more suited for business users. Qlik Sense has better mobility and open API as compared to QlikView, making Qlik Sense more customizable and extensible. The beauty of Qlik Sense lies in its ability to help business get answers to their questions. It helps correlate the data between different data sources and making it very meaningful to users. Powerful data visualizations do not necessarily mean beautiful visualizations and Qlik Sense lays special emphasis on this. Finally what the users need is performance, easy interface, good mobility, collaboration and good enterprise governance - something which Qlik Sense provides. Ganapati, you have over 15 years of experience in IT, and have extensively worked in the BI domain for many years. Please tell us something about your journey. How does your daily schedule look like? I have been fortunate in my career to be able to work on multiple technologies ranging from programming, databases, information security, integrations and cloud solutions. All this knowledge is helping me propose the best solutions for my Qlik customers. It’s a pleasure helping customers in their analytical journey and working for a services company helps in meeting customers from multiple domains. The daily schedule involves doing Proof of Concepts/Demos for customers, designing optimum solutions on Qlik, and conducting requirement gathering workshops. It’s a pleasure facing new challenges every day and this helps me increase my knowledge base. Qlik open API opens up amazing new possibilities and lets me come up with out of the box solutions. Kaushik, you have been awarded the Qlik MVP for 2016 and 2017, and have experience of using Qlik's tools for over 7 years. Please tell us something about your journey in this field. How do you use the tool in your day to day work? I started my career by working with the Qlik technology. My hunger for learning Qlik made me addicted to the Qlik community. I learned lot many things from the community by asking questions and solving real-world problems of community members. This helped me to get awarded by Qlik as MVP for consecutively 2 years. MVP award motivated me to help Qlik customers and users and that is one of the reasons why I thought about writing a book on Qlik Sense. I have implemented Qlik not only for clients but also for my personal use cases. There are many ways in which Qlik helps me in my day-to-day work and makes my life much easier. It’s safe to say that I absolutely love Qlik. Your book 'Implementing Qlik Sense' is primarily divided into 4 sections - with each section catering to a specific need when it comes to building a solid BI solution. Could you please talk more about how you have structured the book, and why? BI projects are challenging, and it really hurts when a project doesn’t succeed. The purpose of the book is to enable Qlik Sense developers to get to implement successful Qlik Projects. There is often a lot of focus on development and thereby Qlik developers miss several other crucial factors which contribute to project success. To make the journey from a Qlik developer to a Qlik consultant the book is divided into 4 sections. The first section focuses on the initial preparation and intended to help consultant to get their groundwork done. The second section focuses on the execution of the project and intended to help consultants play a key role in rest of phases involving requirement gathering, architecture, design, development UAT. The third section is intended to make consultant familiar with some industry domains. This section is intended to help consultant in engaging better with business users and suggesting value-additions to project. The last section is to use the knowledge gained in the three sections and approaching a project with a case study which we come across routinely. Who is the primary target audience for this book? Are there any prerequisites they need to know before they start reading this book? The primary target audience is the Qlik Developers who are looking to progress in their career and are looking to wear the hat of a Qlik consultant.  The book is also for existing consultants who would like to sharpen their skills and use Qlik Sense more efficiently. The book will help them become trusted advisors to their clients. Those who are already familiar with some Qlik development will be able to get the most out of this book.   Qlik Sense is primarily an enterprise tool. With the rise of open source languages such as R and Python, why do you think people would still prefer enterprise tools for their data visualization? Qlik Sense is not a competition to R and Python but there are lots of synergies. The customer gets the best value when Qlik co-exists with R/Python and can leverage the capabilities of both Qlik and R/Python. Qlik Sense does not have the predictive capability which is easily fulfilled by R/Python. For the customer, the tight integration ensures he/she doesn’t have to leave the Qlik screen. There can be other use cases for using them jointly such as analyzing unstructured data and using machine learning. The reports and visualizations built using Qlik Sense can be viewed and ported across multiple platforms. Can you please share your views on this? How does it help the users? Qlik has opened all gates to integrate its reporting and visualization with most of the technologies through APIs. This has empowered customers to integrate Qlik with their existing portals and provide easy access to end users.  Qlik provides APIs for almost all its products, which makes Qlik the first choice for many CIOs because with those APIs they get a variety of options to integrate and automate their work. What are the other key functionalities of Qlik Sense that help the users build better BI solutions? Qlik Sense is not just a pure play data visualization tool. It has capabilities for creating its own warehouse, having an ETL layer and then of course there’s the visualization layer. For the customers, it’s all about getting all the relevant components required for their BI project in a single solution. Qlik is investing heavily in R&D and with its recent acquisitions and a strong portfolio, it is a complete solution enabling users to get all their use cases fulfilled. The open API has enabled opening newer avenues with custom visualizations, amazing concepts such as chatbots, augmented intelligence and much more. The core strength of strong data association, enterprise scalability, governance combined with all other aspects make Qlik one of the best in overall customer satisfaction. Do you foresee Qlik Sense competing strongly with major players such as Tableau and Power BI in the near future? Also, how do you think Qlik plans to tackle the rising popularity of the Open Source alternatives? Qlik has been classified as a Leader in the Gartner’s Magic Quadrant for several years now. We often come across Tableau and Microsoft Power BI as competition. We suggest our customers do a thorough evaluation and more often than not they choose Qlik for its features and the simplicity it offers. With recent acquisitions, Qlik Sense has now become an end-to-end solution for BI, covering uses cases ranging from report distributions, data-as-a-service, and geoanalytics as well. Open source alternatives have their own market and it makes more sense to leverage their capability rather than compete with them. An example, of course, is the strong integration of many BI tools with R or Python which makes life so much easier when it comes to finding useful insights from data. Lastly, what are the 3 key takeaways from your book 'Implementing Qlik Sense'? How will this book help the readers? The book is all about meeting your client’s expectations. The key takeaways are: Understand the role and  importance of Qlik consultant and why it’s crucial to be a trusted advisor to your clients Successfully navigating through all aspects which enable successful implementation of your Qlik BI Project. Focus on mitigating risks, driving adoption and avoiding common mistakes while using Qlik Sense. The book is ideal for Qlik developers who aspire to become Qlik consultants. The book uses simple language and gives examples to make the learning journey as simple as possible. It helps the consultants to give equal importance to certain phases of project development that often neglected. Ultimately, the book will enable Qlik consultants to deliver quality Qlik projects. If this interview has nudged you to explore Qlik Sense, make sure you check out our book Implementing Qlik Sense right away!

[dropcap]S[/dropcap]elf-service Business Intelligence is the buzzword everyone's talking about today. It gives modern business users the ability to find unique insights from their data without any hassle. Amidst a myriad of BI tools and platforms out there in the market, Microsoft’s Power BI has emerged as a powerful, all-encompassing BI solution - empowering users to tailor and manage Business Intelligence to suit their unique needs and scenarios. [author title="Brett Powell"]A Microsoft Power BI partner, and the founder and owner of Frontline Analytics LLC., a BI and analytics research and consulting firm. Brett has contributed to the design and development of Microsoft BI stack and Power BI solutions of diverse scale and complexity across the retail, manufacturing, financial, and services industries. He regularly blogs about the latest happenings in Microsoft BI and Power BI features at Insight Quest. He is also an organizer of the Boston BI User Group.[/author]   In this two part interview Brett talks about his new book, Microsoft Power BI Cookbook, and shares his insights and expertise in the area of BI and data analytics with a partciular focus on Power BI. In part one of the interview, Brett shared his views on topics ranging from what it takes to be successful in the field of BI & data analytics to why he thinks Microsoft is going to lead the way in shaping the future of the BI landscape. Today in part two, he shares his expertise with us on the unique features that differentiate Power BI from other tools and platforms in the BI space. Key Takeaways Ease of deployment across multiple platforms, efficient data-driven insights, ease of use and support for a data-driven corporate culture is what defines an ideal Business Intelligence solution for enterprises. Power BI leads in self-service BI because it’s the first Software as a Service (SaaS) platform to offer ‘End User BI’ in which anyone, not just a business analyst, can leverage powerful tools to obtain greater value from data. Microsoft Power BI has been identified as a leader in Gartner’s Magic Quadrant for BI and Analytics platforms, and provides a visually rich and easy to access interface that modern business users require. You can isolate report authoring from dataset development in Power BI, or quickly scale up or down a Power BI dataset as per your needs. Power BI is much more than just a tool for reports and dashboards. With a thorough understanding of the query and analytical engines of Power BI, users can customize more powerful and sustainable BI solutions. Part Two: Interview Excerpts - Power BI from a Worm’s Eye View How long have you been a Microsoft Power BI user? How have you been using Power BI on a day-to-day basis? What other tools do you generally end up using alongside Power BI for your work? I’ve been using Power BI from the beginning when it was merely an add-in for Excel 2010. Back then, there was no cloud service and Microsoft BI was significantly tethered to SharePoint but the fundamentals of the Tabular data modelling engine and programming language of DAX was available in Excel to build personal and team solutions. On a day-to-day basis I regularly work with Power BI datasets – that is, the analytical data models inside of Power BI Desktop files. I also work with Power BI report authoring and visualization features and with various data sources for Power BI such as SQL Server. From Learning to Mastering Power BI For someone just starting out using Power BI, what would your recommended learning plan be? For existing users, what does the road to mastering Microsoft Power BI look like? When you’re just starting out I’d recommend learning the essentials of the Power BI architecture and how the components (Power BI service, Power BI Desktop, On-Premises Data Gateway, Power BI Mobile, etc) work together. A sound knowledge on the differences between datasets, reports, and dashboards is essential and an understanding of app workspaces and apps is strongly recommended as this is the future of Power BI content management and distribution. In terms of a learning path you should consider what your role will be on Power BI projects – will you be administering Power BI, creating reports and dashboards, or building and managing datasets? Each of these roles has their own skills, technologies and processes to learn. For example, if you’re going to be designing datasets, a solid understanding of the DAX language and filter context is essential and knowledge of M queries and data access is very important as well. The road to mastering Power BI, in my view, involves a deep understanding of both the M and DAX languages in addition to knowledge of Power BI’s content management, delivery, and administration processes and features. You need to be able to contribute to the full lifecycle of Power BI projects and help guide the adoption of Power BI across an organization. The most difficult or ‘tricky’ aspect of Power BI is thinking of M and DAX functions and patterns in the context of DirectQuery and Import mode datasets. For example, certain code or design patterns which are perfectly appropriate for import models are not suitable for DirectQuery models. A deep understanding of the tradeoffs and use cases for DirectQuery versus default Import (in-memory) mode and the ability to design datasets accordingly is a top characteristic of a Power BI master. 5+ interesting things (you probably didn’t know) about Power BI What are some things that users may not have known about Power BI or what it could do? Can readers look forward to learning to do some of them from your upcoming book: Microsoft Power BI Cookbook? The great majority of learning tutorials and documentation on Power BI involves the graphical interfaces that help you get started with Power BI. Likewise, when most people think of Power BI they almost exclusively think of data visualizations in reports and dashboards – they don’t think of the data layer. While these features are great and professional Power BI developers can take advantage of them, the more powerful and sustainable Power BI solutions require some level of customization and can only be delivered via knowledge of the query and analytical engines of Power BI. Readers of the Power BI Cookbook can look forward to a broad mix of relatively simple to implement tips on usability such as providing an intuitive Fields list for users to more complex yet powerful examples of data transformations, embedded analytics, and dynamic filter behaviours such as with Row-level security models. Each chapter contains granular details on core Power BI features but also highlights synergies available by integrating features within a solution such as taking advantage of an M query expression, a SQL statement, or a DAX metric in the context of a report or dashboard. What are the 3 most striking features that make you love to work with Power BI? What are 3 aspects you would like improved? The most striking feature for me is the ability to isolate report authoring from dataset development. With Power BI you can easily implement a change to a dataset such as a new metric and many report authors can then leverage that change in their visualizations and dashboards as their reports are connected to the published version of the dataset in the Power BI service. A second striking feature is the ‘Query Folding’ of the M query engine. I can write or enhance an M query such that a SQL statement is generated to take advantage of the data source system’s query processing resources. A third striking feature is the ability to quickly scale up or down a Power BI dataset via the dedicated hardware available with Power BI Premium. With Power BI Premium, free users (users without a Pro License) are now able to access Power BI reports and dashboards. The three aspects I’d like to see improved include the following: Currently we don’t have IntelliSense and other common development features when writing M queries. Currently we don’t have display folders for Power BI datasets thus we have to work around this with larger, more complex datasets to maintain a simple user interface. Currently we don’t have Perspectives, a feature of SSAS, that would allow us to define a view of a Power BI dataset such that users don’t see other parts of a data model not relevant to their needs. Is the latest Microsoft Power BI update a significant improvement over the previous version? Any specific new features you’d like to highlight? Absolutely. The September update included a Drillthrough feature that, if configured correctly, enables users to quickly access the crucial details associated with values on their reports such as an individual vendor or a product. Additionally, there was a significant update to Report Themes which provides organizations with more control to define standard, consistent report formatting. Drillthrough is so important that an example of this feature was added to the Power BI Cookbook. Additionally, Power BI usage reporting including the identity of the individual user accessing Power BI content was recently released and this too was included in the Power BI Cookbook. Finally, I believe the new Ribbon Chart will be used extensively as a superior alternative to stacked column charts. Can you tell us a little about the new 'time storyteller custom visual' feature in Power BI? The Timeline Storyteller custom visual was developed by the Storytelling with Data group within Microsoft Research. Though it’s available for inclusion in Power BI reports via the Office Store like other custom visuals, it’s more like a storytelling design environment than a single visual given its extensive configuration options for timeline representations, scales, layouts, filtering and annotations. Like the inherent advantages of geospatial visuals, the linking of Visio diagrams with related Power BI datasets can intuitively call out bottlenecks and otherwise difficult-to-detect relationships within processes. 7 reasons to choose Power BI for building enterprise BI solutions Where does Power BI fall within Microsoft's mission to empower every person and every organization on the planet to achieve more of 1. Bringing people together 2. Living smarter 3. Friction free creativity 4. Fluid mobility? Power BI Desktop is available for free and is enhanced each month with features that empower the user to do more and which remove technical obstacles. Similarly, with no knowledge whatsoever of the underlying technology or solution, a business user can access a Power BI app on their phone or PC and easily view and interact with data relevant to their role. Importantly for business analysts and information workers, Power BI acknowledges the scarcity of BI and analytics resources (ie data scientists, BI developers) and thus provides both graphical interfaces as well as full programming capabilities right into Power BI Desktop. This makes it feasible and often painless to quickly create a working, valuable solution with relatively little experience with the product. We can expect Power BI to support 10GB (and then larger) datasets soon as well as improve its ‘data storytelling’ capabilities with a feature called Bookmarks. In effect, Bookmarks will allow Power BI reports to become like PowerPoint presentations with animation. Organizations will also have greater control over how they utilize the v-Cores they purchase as part of Power BI Premium. This will make scaling Power BI deployments easier and more flexible. I’m personally most interested in the incremental refresh feature identified on the Power BI Premium Roadmap. Currently an entire Power BI dataset (in import mode) is refreshed and this is a primary barrier to deploying larger Power BI datasets. Additionally (though not exclusively by any means), the ability to ‘write’ from Power BI to source applications is also a highly anticipated feature on the Power BI Roadmap. How does your book, Microsoft Power BI Cookbook, prepare its readers to be industry ready? What are the key takeaways for readers from this book? Power BI is built with proven, industry leading BI technologies and architectures such as in-memory, columnar compressed data stores and functional query and analytical programming languages. Readers of the Power BI Cookbook will likely be able to quickly deliver fresh solutions or propose ideas for enhancements to existing Power BI projects. Additionally, particularly for BI developers, the skills and techniques demonstrated in the Power BI Cookbook will generally be applicable across the Microsoft BI stack such as in SQL Server Analysis Services Tabular projects and the Power BI Report Server. A primary takeaway from this book is that Power BI is much more than a report authoring or visualization tool. The data transformation and modelling capabilities of Power BI, particularly combined with Power BI Premium capacity and licensing considerations, are robust and scalable. Readers will quickly learn that though certain Power BI features are available in Excel and though Excel can be an important part of Power BI solutions from a BI consumption standpoint, there are massive advantages of Power BI relative to Excel. Therefore, almost all PowerPivot and Power Query for Excel content can and should be migrated to Power BI Desktop. An additional takeaway is the breadth of project types and scenarios that Power BI can support. You can design a corporate BI solution with a Power BI dataset to support hundreds of users across multiple teams but you can also build a tightly focused solution such as monitoring system resources or documenting the contents of a dataset. If you enjoyed this interview, check out Brett’s latest book, Microsoft Power BI Cookbook. Also, read part one of the interview here to see how and where Power BI fits into the BI landscape and what it takes to stay successful in this industry.

Security is an important aspect for organizations and securing the devices that contain confidential data--personal or professional, is absolutely essential. Microsoft Enterprise Mobility + Security, an intelligent mobility management and security platform, offers a suite of services that helps in securing employee devices; thus, protecting and securing the organization. Our recent chat with Nate Chamberlain, a Business Analyst at DH Pace, Kansas, helped us understand more about the Microsoft 365 Enterprise Mobility + Security suite. Nate is also a Microsoft MVP in Office apps and services. His recently published book Microsoft 365 Mobility and Security – Exam Guide MS-101, helps users to plan, deploy, and manage Microsoft Office 365 services and gain the skills required to pass the MS-101 exam. In this interview, Nate also shares his favorite services from the suite, the importance of using Shadow IT in a controlled manner while ensuring security Cloud Apps, how the M365 Certified Enterprise Administrator Expert certification has helped him give a career boost, and much more. On the Microsoft Enterprise Mobility and Security suite Microsoft Enterprise Mobility and Security suite help professionals secure devices used within the enterprise and also helps in identifying breaches before they cause any major damage. The suite provides two offerings, Enterprise Mobility + Security E3 and E5. Talking about the popular Microsoft solutions in the suite, Nate said, “Azure Active Directory, Intune, and the Microsoft 365 Security & Compliance Center are big players in the overall EM+S suite. Taking the time to get to know each of them has the potential to significantly enhance your organization’s security.” Nate said one of his favorite features from the Microsoft 365 Enterprise Mobility + Security suite is, “the ability to be extremely granular in building conditional access policies. That, paired with the ability to utilize AI and zero-day security information in policies and practices, continually impresses me. It’ll be interesting to see where Endpoint Manager takes us.” On the topic of what features he would like to add in the suite in the future, he said, “the biggest improvement I would hope for currently is licensing simplification, and making sure admins are able to secure their organization and its users without breaking the budget.” On the new Microsoft Endpoint Manager and using ‘Shadow IT’ for Cloud App security Last month, Microsoft announced its new Endpoint Manager, a convergence of two of its popular tools, System Center Configuration Manager (ConfigMgr) and Microsoft Intune. Both ConfigMgr and Intune offer integrated cloud-powered management tools, and unique co-management options to provision, deploy, manage, and secure endpoints and applications across an organization. The Endpoint Manager offers end-to-end management solutions without the need for worrying too much about the complexity involved during migration, thus helping customers in a smooth cloud transition. According to Nate, “Microsoft Endpoint Manager takes a lot of the licensing guesswork out of building a secure solution for your organization.”  In addition to Intune and ConfigMgr, Microsoft Endpoint Manager includes the Device Management Admin Center (DMAC) and Desktop Analytics. Nate further adds that Microsoft Endpoint Manager includes nearly everything discussed in his exam prep book, Microsoft 365 Mobility and Security – Exam Guide MS-101--including Intune and ConfigMgr. Shadow IT and cloud app security In his book, Nate has written about controlling the use of ‘Shadow IT’ for Cloud App security. Shadow IT, also known as Stealth IT, is built and used without the knowledge of the IT or security group within the organization. We asked Nate for what processes is Shadow IT built-in the organizations. We also asked why Shadow IT is a threat and how organizations can minimize its usage. Clearing the clouds on Shadow IT, Nate explains, “Shadow IT is often a consequence of being too restrictive without providing alternative means of productivity and collaboration solutions. And sometimes, even if you provide alternatives or company-licensed tools, it’s the lack of ongoing education that failed to spread awareness and competency that led users to more familiar, comfortable means of accomplishing goals. When users need to accomplish something, they’ll find a way with or without the organization’s assistance.” He further adds, “It’s IT administrators’ responsibility to make sure productivity and collaboration solutions are provisioned and configured for secure, appropriate usage, and that education is provided to get users on board.” A few key takeaways from Nate’s book, an MS-101 exam guide, and his recommendations for further Microsoft 365 certifications According to Microsoft’s official website, the “Exam MS-101: Microsoft 365 Mobility and Security”, the skills measured in the exam includes, implementing modern device services, implementing Microsoft 365 security and threat management, and managing Microsoft 365 governance and compliance. These skills would help companies sieve through all the candidates among others who don’t know much about the suite. Talking about the key takeaways from his book, Nate says, “I hope readers find the content to be challenging, but accessible. The best takeaway I could hope for is that readers retain information that not only helps them in the exam but in their jobs. The whole point of taking exams and obtaining certifications is to demonstrate proficiency, knowledge, and skill. Ultimately, it’s practising those skills in the real world that matter - not the score on the exam. But the exam is absolutely a first step toward building confidence and career growth.” We also asked him what other certifications he would recommend next, to which Nate said, “Once readers pass MS-101, they should aim for passing MS-100 if they haven’t already. After that, they’re just one prerequisite certification away from becoming a Microsoft 365 Enterprise Administrator Expert.” On Nate’s journey as a Microsoft SharePoint Systems Engineer and beyond Nate worked as a SharePoint Systems Engineer at LMH Health, Kansas, and is currently a Business Analyst at DH Pace in Olathe, KS. He is also an M365 Certified Enterprise Administrator Expert. He shared why certifications are important for career growth. He says, “My certification certainly looks great on my resume to potential employers and I like to think it’s part of what made me competitive in pursuing my current role. Certifications are verified proof of skill and competency. It alleviates some risk a company would otherwise assume in hiring someone for highly technical work like we find in our industry.” He also shared about his journey and how learning SharePoint transformed his role. “My journey has been one of self-teaching, fueled by inspiring tech solutions coming out of Microsoft. I was once tasked to learn what I can about SharePoint, at the University of Kansas, and it turned into a SharePoint-specific role there. That opened doors for me which brought me to LMH Health and ultimately DH Pace.” He continues, “Somewhere along the way, I started sharing what I was learning via my blog, NateChamberlain.com, and by speaking at conferences around the country regularly. I also started a SharePoint user group, LSPUG, in Lawrence, KS. For these reasons and perhaps others, I was awarded the Microsoft MVP for Office Apps and Services.” Certifications are indeed verified proof of skill and competency. So go ahead and check out Nate’s book, Microsoft 365 Mobility and Security – Exam Guide MS-101, to get up to speed with planning, deploying, and managing Microsoft Office 365 services and gain the skills you need to pass the MS-101 exam. With this book, you’ll explore everything from mobile device management and compliance, through to data governance and auditing. By the end of this book, you’ll have learned to work with Microsoft 365 services and covered the concepts and techniques you need to know to pass the MS-101 exam. Written in a succinct way, you’ll explore chapter-wise self-assessment questions, exam tips and mock exams with answers. Microsoft technology evangelist Matthew Weston on how Microsoft PowerApps is democratizing app development [Interview] How PyTorch is bridging the gap between research and production at Facebook: PyTorch team at F8 conference SOLIDWORKS specialist Tayseer Almattar takes us into the world of 3D modeling using SOLIDWORKS 2020 [Interview]

Netflix, the global poster child for streamed TV and the use of Big Data to inform the programs they develop, has shown steady customer growth for several years now. Recently, the company revealed that it would be shutting down the user reviews which have been so prominent in their media catalogue interface for so long. In the background, media and telco are merging. AT&T, the telco which undertook the biggest deal in history recently, acquired Time and wants HBO to become like Netflix. Telia, a Finnish telecommunications company bought Bonnier Broadcasting in late July 2018. The video content landscape has changed a great deal in the last decade. Everyone in the entertainment game wants to move beyond broadcast TV and to use data to develop content their users will love and which will give their customer base more variety. This means they can look to data to charge higher subscription rates per user, experiment with tiered subscriptions, decide to localize global content, globalize local content and more. These changes raise two key questions. First, are we heading for a world in which AI and ML based algorithms drive what we watch on TV? And second, are the days of human recommendation being quietly replaced by machine recommendations over which the user has no control? [caption id="attachment_21726" align="aligncenter" width="1392"] As you know, Netflix is acquiring customers fast.[/caption] Source: Statista To get an insider’s view on the answer to those questions, I sat down with Matt Jones of OVO Mobile, one of Australia’s fastest growing telecommunications companies. OVO offer their customers a unique point of difference – streaming video sports content, included in a phone plan. OVO has bought the rights to a number of niche sports in Australia which weren’t previously available and now offer free OTA (Over the Air) digital content for fans of ‘unusual’ sports like Drag Racing or Gymnastics. OTA content is anything delivered to a user’s phone over a wireless network. In OVO’s case, the data used to transport the video content they provide to their users is free. That means customers don’t have to worry about paying more for mobile data so they can watch it – a key concern for users. OVO Mobile and Netflix are in very similar businesses – and Matt has a unique point of view about how Artificial Intelligence and Machine Learning will impact the world of telco and media. Key takeaways What’s changed our media consumption habits: the ubiquitous mobile internet, the always on and connected younger generation, better mobile hardware, improved network performance and capabilities, need for control over content choices. Digitization allows new features –some of which that people have proven to love - binge watching, screening out advert breaks and time shifting. The key to understanding the value of ML and AI is not in understanding the statistical or technical models that are used to enable it, it’s the way AI is used to improve the customer experience your digital customers are having with you. The use of AI in digital/app experience has changed in a way to personalize what users can see which old media could not offer. Content producers use the information they have on us, about the programs we watch, when we watch them and for how long we watch to Contribution of AI / ML towards the delivery of online media is endless in terms of personalisation, context awareness, notification management etc. Social acceptance of media delivered to users on mobile phones is what’s driving change A number of overlapping factors are driving changes in how we engage with content. Social acceptance of the internet and mobile access to it as a core part of life is one key enabler. From a technology perspective, things have changed too. Smartphones now have bigger, higher resolution screens than ever before – and they’re with us all the time. Jones believes this change is part of a cultural evolution in how we relate to technology. He says, “There has also been a generational shift which has taken place. Younger people are used to the small screen being the primary device. They’re all about control, seeking out their interests and consuming these, as opposed to previous generations which was used to mass content distribution from traditional channels like TV.” Other factors include network performance and capability which has improved dramatically in recent years. Data speeds have grown exponentially from 3G networks – launched less than 15 years ago, which could support stuttered low resolution video to 4G and 4.5G enabled networks. These can now support live streaming of High Definition TV. Mobile data allowances in plans and offers from some phone companies to provide some content ‘data free’ (as OVO does with theirs) have also driven uptake. Finally, people want convenience and digital offers that in a way people have never experienced before. Digitization allows new features –some of which that people have proven to love - binge watching, screening out advert breaks and time shifting. What part can AI / machine learning play in the delivery of media online? Artificial Intelligence (AI) is already part of 85% of our online interactions. Gartner suggest, it will be part of every product in the future. The key to understanding the value of ML and AI is not in understanding the statistical or technical models that are used to enable it, it’s the way AI is used to improve the customer experience your digital customers are having with you. When you find a new band in Spotify, when YouTube recommends a funny video you’ll like, when Amazon show you other products that you might like to consider alongside the one you just put in to your basket, that’s AI working to improve your experience. “Over The Top content is exploding. Content owners are going direct to consumer and providing fantastic experiences for their users. What’s changing is the use of AI in digital / app experiences to personalize what users see in ways old media never could.” Says Matt. Matt’s video content recommendation app, for example, ‘learns’ not just what you like to watch but also the times you are most likely to watch it. It then prompts users with a short video to entice them to watch. And the analytics available show just how effective it is. Matt’s app can be up to 5 times more successful at encouraging customers to watch his content, than those who don’t use it. “The list of ways that AI / ML contributes to the delivery of media online is endless. Personalisation, context awareness, notification management …. Endless” By offering users recommendations on content they’ll love, producers can now engage more customers for longer. Content producers use the information they have on us, about the programs we watch, when we watch them and for how long we watch to: Personalise at volume: Apps used to deliver content can personalise what’s shown first to users, based on a number of variables known about them, including the sort of context awareness that can be relatively easy to find on mobile devices. Ultimately, every AI customer experience improvement (including the examples that follow) are all designed to automate the process of providing something special to each individual that they uniquely want. Automation means that can be done at scale, with every customer treated uniquely. Notification management: AI that tracks the success of notifications and acknowledges, critically, when they are not helpful to the user, can be employed to alert users only about things they want to know. These AI solutions provide updates to users based on their preferences and avoid the provision of irrelevant information. Content discovery & Re- engagement: AI and ML can be used in the provision of recommendations as to what users could watch, which expose customers to content they would not otherwise find, but which they are likely to value. Better / more relevant advertising: Advertising which targets a legitimately held, real, customer need is actually useful to viewers. Better analytics for AI can assist in targeting micro segments with ads which contain information customers will value. Lattice, is a Business Insights tool provider. Their ‘Lattice Engine’ product combined information held in multiple cloud based locations and uses AI to automatically assign customers to a segment which suits them. Those data are then provided to a customer’s eCommerce site and other channel interactions, and used to offer content which will help them convert better. Developing better segments: Raw data on real customers can be gathered from digital content systems to inform Above The Line marketing in the real, non digital world. Big data analytics can now be used with accurate segmentation for local area marketing and to tie together digital and retail customer experiences. McKinsey suggest that 36% of companies are actively pursuing strategies, driven from their Big Data reserves. They advise their clients that Big Data can be used to better understand and grow Customer Lifetime Values. In the future - Deep linking for calls-to-action: Some digital content is provided in a form such that viewers can find out more information about an item on screen. Providing a way to deep link from a video screen in to a shopping cart prepopulated with something just seen on screen is an exciting possibility for the future. Cutting steps out of the buying process to make it easier for eCommerce users to transact from within content apps to buying a product they’ve seen on the screen is likely to become a big business. Deep linking raises the value of the content shown to the degree it raise the sales of the products included. Bringing it all together Jones believes those that invest big in AI and machine learning, and of them, those who find a way to draw out insights and act upon them, will be the ultimate victors. “The big winners are going to be the people who connect a fan with content they love and use AI and ML to deliver the best possible experience. It’s about using all the information you have about your users and acting on them.” Said Jones. That commercial incentive is already driving behavior. AI and ML drive already provide personalized content recommendations. Progressive content companies, including Matt’s, are already working on building AI in to every facet of every Digital experience you have. As to whether AI is entirely replacing social media influence, I don’t think that’s the case. The research says people are still 4 times more likely to watch a video if it is recommended to them by a friend. Reviews have always been important to presales on the internet and that applies to TV shows, too. People want to know what real users felt when they used a product. If they can’t get reviews from Netflix, they will simply open a new tab and google for reviews in that while they are thinking of how to find something to watch on Netflix. About Matt Jones, Matt is an industry disruptor, launching the first of its kind Media and Telco brand OVO Mobile in 2015, Matt is the driving force behind convergence of new media & telco – by bringing together Telecommunications with Media Rights and digital broadcast for mass distribution. OVO is a new type of Telco, delivering content that fans are passionate about, streamed live on their mobile or tablet UNLIMITED & data free. OVO has secured exclusive 3 year+ digital broadcast and distribution rights for a range of content owners including Supercars, World Superbikes, 400 Thunder Drag Series, Audi Australia Racing & Gymnastics Australia – with a combined Australian audience estimated at over 7 Million. OVO is a multi-award winner, including winning the Money Magazine Best of the Best Award 2017 for high usage, as well as featuring on A Current Affair, Sunrise, The Today Show, Channel 7 News, Channel 9 News and multiple radio shows for their world-first kids’ mobile phone plan with built-in cyber security protection. As OVO CEO, Matt was nominated for Start-Up Executive of the Year at the CEO Magazine Awards 2017 and was awarded runner-up. The Award recognises the achievements of leaders and professionals, and the contributions they have made to their companies across industry-specific categories. Matt holds a Bachelor of Arts (BA) from the University of Tasmania and regularly speaks at Telco, Sports Marketing and Media forums and events. Matt has held executive leadership roles at leading Telecommunications brands including Telstra (Head of Strategy – Operations), Optus, Vodafone, AAPT, Telecom New Zealand as well as global Management Consulting firms including BearingPoint. Matt lives on the northern beaches of Sydney with his wife Mel and daughters Charlotte and Lucy. How to earn $1m per year? Hint: Learn machine learning We must change how we think about AI, urge AI founding fathers Alarming ways governments are using surveillance tech to watch you

Charlotte Jee is a journalist specialising in technology and politics, currently working as editor for Techworld. Charlotte has founded, hosted and moderated a range of events including The Techies awards and her own ‘Women in Tech Speak Up’ event series. In 2017, Charlotte set up Jeneo with the mission to give women and underrepresented people a voice at Tech events. Jeneo helps companies source great individuals to speak at their events, and encourage event organisers to source panellists from a range of backgrounds. We spoke to Charlotte in a live Twitter Q&A to find out a bit more about Jeneo and Charlotte’s experience as a woman in the tech sector. Packt: Why did you decide to set up Jeneo? CJ: Good question! Basically, blame manels (aka all-male panels). I got tired of going to tech events and not seeing even one woman speaking. It's symptomatic of a wider issue with diversity within tech. And I resolved to start to do something about it! Packt: What have been your most interesting findings from the research you've carried out into women at tech events? CJ: Interestingly, the ratio of women to men speakers varies hugely across different events. However, promisingly, I've found that those who have worked on this specific issue have successfully upped their number of women speakers. The general finding was that from the top 60 London tech events, women comprise about a quarter of the speakers. The full research hasn't concluded just yet – so stay tuned. Packt: How has the industry changed since you started in Tech? Do you think it is getting easier for women to get into the Tech world? CJ: To be honest, I don't think that much progress has been made since I started working in tech. However, in the last year with scandals at Uber, Google and VC sexual harassment, the industry is starting to finally really focus on making itself more welcoming for women. Packt: What do you think is the best part of being a woman in the tech industry? CJ: I think the answer is the same regardless of your gender – the tech industry is at the forefront of the latest innovations within society and it is constantly changing, so you never get bored. Packt: What do you think is the worst part of being a woman in the tech industry? CJ: To be clear, I believe the majority of workplaces within tech are perfectly welcoming to women – however for the persistent minority that aren't, women can face all sorts of discrimination, both subtle and unsubtle. Packt: What advice would you give to a woman considering a career in the tech industry? CJ: Go for it! I honestly can't think of a better industry to work in. You can pretty much guarantee you'll be able to find work no matter what, so long as you keep your skills up to date. Packt: In what ways – positive or negative ­– do you think our education system influences the number of women in tech? CJ: Good question. I actually think that it's in education that this problem starts – from the experts I've spoken to, it seems like our education system too often seems to actively discourage women from pursuing careers in tech. Packt: What has been your biggest success in your tech career so far? CJ: I was really honoured when my boss promoted me to TechWorld News editor in 2015. Recently, it'd have to be the Women in Tech Speak Up event for 300 people I organised (single-handedly) in August 2017. Also, creating this list of 348 women working in tech in the UK which in many ways kicked this all off. Packt: What advice would you give to help tech companies to help increase their gender diversity? CJ: Way too much for one tweet here. Get senior buy-in, look carefully at your hiring process, be clear on culture/working practices, collect and publish data, provide flexible working (men want this too!) – if you want to more detail please get in touch with me. Packt: What do you think the future looks like for women working in tech? CJ: I feel more optimistic now than I have at any point. I think there is a huge amount of desire within the industry to be more inclusive. However, it's translating that goodwill into action – that's what I'll be focusing on. Packt: Are there any tech companies doing awesome things to increase diversity in their business? CJ: There's some impressive work from Monzo at the moment, who are highlighting the need for a diverse and inclusive team that represents their user base. There are plenty of others doing good work too, including Amazon UK. Packt: Are there any particular women in tech who have inspired you and who should we be following on Twitter? CJ:How long do you have? So many: @emercoleman @kitterati @ChiOnwurah @NAUREENK @annkempster @cathywhite10 @carrie_loves_ @JeniT @lily_dart @annashipman @yoditstanton That is truly just for starters. And of course, I have to add, the original woman in tech who inspired me is my Mum, Jane Jee (@janeajee) – she's CEO of RegTech startup Kompli-Global Compliance (@kompliglobal). Plus, obviously everyone on this list. Packt: Do you have any tips or advice for women to get on panels or bag speaking slots at tech events? CJ: Start small if possible, build up your conference – don't be afraid to put yourself out there! My research has found speaker submissions overwhelmingly come from men, get proactive and contact events you'd like to speak at. Thanks for chatting with us, Charlotte! Find out more about Jeneo here and follow Charlotte on Twitter: @CharlotteJee.

Analytics is slowly changing the face of the sports industry as we know it. Data-driven insights are being used to improve the team and individual performance, and to get that all-important edge over the competition. But what exactly is sports analytics? And how is it being used? What better way to get answers to these questions than asking an expert himself! [author title="Gaurav Sundararaman"]A Senior Stats Analyst at ESPN currently based in Bangalore, India. With over 10 years of experience in the field of analytics, Gaurav worked as a Research Analyst and a consultant in the initial phase of his career. He then ventured into sports analytics in 2012, and played a major role in the Analytics division of SportsMechanics India Pvt. Ltd. where he was the Analytics Consultant for the T20 World Cup winning West Indies team in 2016.[/author]   In this interview, Gaurav takes us through the current landscape of sports analytics, and talks about how analytics is empowering better decision-making in sports. Key Takeaways Sports analytics pertains to finding actionable, useful insights from sports data which teams can use to gain competitive advantage over the opposition Instincts backed by data make on and off-field decisions more powerful and accurate Rise of IoT and wearable technology has boosted sports analytics. With more data available for analysis, insights can be unique and very helpful Analytics is being used in sports right from improving player performance to optimizing ticket prices and understanding fan sentiments Knowledge of  tools for data collection, analysis and visualization such as R, Python and Tableau is essential for a sports analyst Thorough understanding of the sport, up to date skillset and strong communication with players and management are equally important factors to perform efficient analytics Adoption of analytics within sports has been slow, but steady. More and more teams are now realizing the benefits of sports analytics and are adopting an analytics-based strategy Complete Interview Analytics today is finding widespread applications in almost every industry today - how has the sports industry changed over the years? What role is analytics playing in this transformation? The sports industry has been relatively late in adopting analytics. That said, the use of analytics in sports has also varied geographically. In the west, analytics plays a big role in helping teams, as well as individual athletes, take up decisions. Better infrastructure and a quick adoption of the latest trends in technology is an important factor here. Also, investment in sports starts from a very young age in the west, which also makes a huge difference.  In contrast, many countries in Asia are still lagging behind when it comes to adopting analytics, and still leverage on traditional techniques to solve problems. A combination of analytics with traditional knowledge from experience would go a long way in helping teams, players and businesses succeed. Previously the sports industry was a very close community. Now with the advent of analytics, the industry has managed to expand its horizon. We witness more non-sportsmen playing a major part in the decision making. They understand the dynamics of the sports business and how to use data-driven insights to influence the same. Many major teams across different sports such as Football (Soccer), Cricket, American Football, Basketball and more have realized the value of data and analytics. How are they using it? What advantages does analytics offer to them? One thing I firmly believe is that analytics can’t replace skills or can’t guarantee wins. What it can do is ensure there is logic towards certain plans and decisions. Instincts backed by data make the decisions more powerful. I always tell the coaches or players – Go with your gut and instincts as Plan A. If it does not work out your fall back could be Plan B based on trends and patterns derived from data. It turns out to be a win-win for both. Analytics offers a neutral perspective which sometimes players or coaches may not realize. Each sport has a unique way of applying analytics to make decisions and obviously, as analysts, we need to understand the context and map the relevant data. As far as using the analytics is concerned, the goals are pretty straightforward - be the best, beat the opponents and aim for sustained success. Analytics helps you achieve each of these objectives. The rise of IoT and wearable technology over the last few years has been incredible. How has it affected sports, and sports analytics, in particular? It is great to see that many companies are investing in such technologies. It is important to identify where wearables and IoT can be used in sport and where it can cause maximum impact. These devices allow in-game monitoring of players, their performance, and their current physical state. Also, I believe more than on-field, these technologies would be very useful in engaging fans as well. Data derived from these devices could be used in broadcasting as well as providing a good experience for fans in the stadiums. This will encourage more and more people to watch games in stadiums and not in the comfort of their homes. We have already seen a beginning with a few stadiums around the world leveraging technology (IoT). The Mercedes Benz stadium (home of Atlanta Falcons) has a high tech stadium powered by IBM. Sacramento is building a state-of-the-art facility for the Sacramento Kings. This is just the start, and it will only get better with time. How does one become a sports analyst? Are there any particular courses/certifications that one needs to complete in order to become one? Can you share with us your journey in sports analytics? To be honest there are no professional courses yet in India to become an Analyst. There are a couple of colleges which have just started offering Sports Analytics as a course in their Post-Graduation Program. However, there are a few companies (Sports Mechanics and Kadamba Technologies in Chennai) that offer jobs that can enable you to become a Sports Analyst if you are really good.  If you are a freelancer then my advice would be to ensure you brand yourself well and showcase your knowledge through social media platforms and get a breakthrough via contacts. Post my MBA, Sports Mechanics (a leader in this space), a company based in Chennai were looking for someone to work to start their data practice. I was just lucky to be at the right place at the right time. I worked for 4 years there and was able to learn a lot about the industry and what works and what does not. Being a small company, I was lucky to don multiple hats and work on different projects across the value chain. I moved and joined the lovely team Of ESPNCricinfo where I work for their stats team. What are the tools and frameworks that you use for your day to day tasks? How do they make your work easier? There are no specific tools or frameworks. It depends on the enterprise you are working for. Usually, they are proprietary tools of the company. Most of these tools are used either to collect, mine or visualize data. Interpreting the information and presenting it in a manner in which users understand is important and that is where certain applications or frameworks are used. However to be ready for the future it would be good to be skilled on tools that support data collection, analysis and visualization namely R, Python and Tableau, to name a few. Do sports analysts have to interact with players and the coaching staff directly? How do you communicate your insights and findings with the relevant stakeholders? Yes, they have to interact with players and management directly. If not, the impact will be minimal. Communicating insights is very important in this industry. Too much analysis could lead to paralysis. We need to identify what exactly each player or coach is looking for, based on their game and try to provide them the information in a crisp manner which helps them make decisions on and off the field. For each stakeholder the magnitude of the information provided is different. For the coach and management, the insights can be in detail while for the players we need to keep it short and to the point. The insights you generate must not only be limited to enhancing the performance of a team on the field but much more than that. Could you give us some examples? Insights can vary. For the management, it could deal with how to maximise the revenue or save some money in an auction. For coaches, it could help them know about his team’s as well as the opposition’s strengths and weaknesses from a different perspective. For captains, data could help in identifying some key strategies on the field. For example, in Cricket, it could help the captain determine which bowler to bring on to which opposition batsmen, or where to place the fielders. Off the field, one area where analytics could play a big role would be in grassroots development and tracking of an athlete from an early age to ensure he is prepared for the biggest stage. Monitoring performance, improving physical attributes by following a specific regimen, assessing injury record and designing specific training programs, etc. are some ways in which this could be done. What are some of the other challenges that you face in your day to day work? Growth in this industry can be slow sometimes. You need to be very patient, work hard and ensure you follow the sport very closely. There are not many analytical obstacles as such, but understanding the requirements and what exactly the data needs are can be quite a challenge. Despite all the buzz, there are quite a few sports teams and organizations who are still reluctant to adopt an analytics-based strategy – why do you think is that the case? What needs to change? The reason for the slow adoption could be the lack of successful case studies and the awareness. In most sports when so many decisions are taken on the field sometimes the players' ability and skill seems far more superior to anything else. As more instances of successful execution of data-based trends come up, we are likely to see more teams adopting the data-based strategy. Like I mentioned earlier, analytics needs to be used to make the coach and captain take the most logical and informed decisions. Decision-makers need to be aware of the way it is used and how much impact it can cause.  This awareness is vital towards increasing the adoption of analytics in sports. Where do you see sports analytics in the next 5-10 years? Today in sports many decisions are taken on gut feeling, and I believe there should be a balance. That is where analytics can help. In sports like Cricket, only around 30% of the data is used and there is more emphasis given to video. Meanwhile, if we look at Soccer or Basketball, the usage of data and video analytics is close to 60-70% of its potential. Through awareness and trying out new plans based on data, we can increase usage of analytics in cricket to 60-70 % in the next few years. Despite the current shortcomings, It is fair to say that there is a progressive and positive change at the grassroots level across the world. Data-based coaching and access to technology are slowly being made available to teams as well as budding sportsmen/women. Another positive is that the investment in the sports industry is growing steadily. I am confident that in a couple of years, we will see more job opportunities in sports. Maybe in five years, the entire ecosystem would be more structured and professional. We would witness analytics playing a much bigger role in helping stakeholders make informed decisions, as data-based insights become even more crucial. Lastly, what advice do you have for aspiring sports analysts? My only advice would be - Be passionate, build a strong network of people around you, and constantly be on the lookout for opportunities. Also, it is important to keep updating your skill-set in terms of the tools and techniques needed to perform efficient and faster analytics. Newer and better tools keep coming up very quickly, which make your work easier and faster. Be on the lookout for such tools! One also needs to identify their own niche based on their strengths and try to build on that. The industry is on the cusp of growth and as budding analysts, we need to be prepared to take off when the industry matures. Build your brand and talk to more people in the industry - figure out what you want to do to keep yourself in the best position to grow with the industry.

SAP, the market-leading enterprise software, recently became the first European technology company to create an AI ethics advisory panel where they announced seven guiding principles for AI development. These guidelines revolve around recognizing AI’s significant impact on people and society. Also, last week, at the Microsoft Ignite conference, SAP, in collaboration with Microsoft and Adobe announced the Open Data Initiative. This initiative aims to help companies to better govern their data and support privacy and security initiatives. For SAP, this initiative will further bring advancements to its SAP C/4HANA and S/4HANA platforms. All of these actions emphasize SAP’s focus on transforming itself into a responsible data use company. We recently interviewed Rehan Zaidi, a senior SAP ABAP consultant. Rehan became one of the youngest authors on SAP worldwide when he was published in the prestigious SAP Professional Journal in the year 2001. He has written a number of books, and over 20 articles and professional papers for the SAP Professional Journal USA and HR Expert USA, part of the prestigious sapexperts.com library. Following are some of his views on the SAP community and products and how the SAP suite can benefit people including budding professionals, developers, and business professionals. Key takeaways SAP HANA was introduced to accelerate jobs 200 times faster while maintaining the efficiency. The introduction of SAP Leonardo brought in the next wave of AI, machine learning, and blockchain services via the SAP cloud platform and other standalone projects. Experienced ABAP developers should look forward to getting certified in one of the newest technologies such as HANA, and Fiori. SAP ERP Central Component (SAP ECC) is the on-premises version of SAP, and it is usually implemented in medium and large-sized companies. For smaller companies, SAP offers its Business One ERP platform. SAP Fiori is a line of SAP apps meant to address criticisms of SAP's user experience and UI complexity. Q.1. SAP is one of the most widely used ERP tools. How has it evolved over the past few years from the traditional on-premise model to keep up with the cloud-based trends? Yes. Let me cover the main points. SAP started in 1973 as a company and the first product SAP R/98 was launched. In 1979, SAP launched the R/2 design. It had most of the typical processes such as accounting, manufacturing processes, supply chain logistics, and human resources. Then came R/3  that brought the more efficient three-tier (Application server -  Database and the presentation (GUI)) architecture, with more new modules and functionalities added. It was a smart system fully configurable by functional consultants. This was further enhanced with Netweaver capability that brought the integration of the internet and SOA capability.  SAP introduced the ECC 5 and subsequently the ECC 6 Release. Mobility was later added that lets mobile applications running on devices to access the business processes in SAP and execute them. Both display and updation of SAP data was possible. HANA system was then introduced. It is very fast and efficient - allows you to do 200 times faster jobs than before Cloud systems then became available that let customers connect to SAP Cloud Platform via their on-premise systems and then get access to services such as Mobile Service for app protection, Mobile Service for SAP Fiori, among others. SAP Leonardo was finally introduced, as a way of bringing in next-gen AI, machine learning and blockchain services via standalone projects and the SAP cloud platform. Q.2. Being a Senior ABAP Programming Analyst, how does your typical day look like? Ahh. Well, a typical day! No two days are the same for us. Each morning we find ourselves confronting a problem whose solution is to be devised. A different problem every day- followed by a unique solution. We spend hours and hours finding issues in custom developed programs. We learn about making custom programs run faster. We get requirements of a wide variety of users. They may be in the Human Resource, Materials Management, Sales and Distribution or Finance, and so on. This requirement may be pertaining to an entirely new report or a dialog program having a set of screens. We even do Fiori ( using Javascript based library) applications that may be accessible from the PC or a mobile device. I even get requirements of teaching junior or trainee SAP developers on a wide variety of technologies. Q.3. Can you tell us about the learning curve for SAP? There are different job profiles related to SAP which range from executives to consultants and managers. How do each of them learn or update themselves on SAP? Yes, this is a very important question. A simple answer to this question is that “there is no end to learning and at any stage, learning is never enough,” no matter to which field within SAP you belong to. Things are constantly changing. The more you read and the more you work, you feel that there is a lot to be done. You need to constantly update yourself and learn about new technologies. There is plenty of material available on the internet. I usually refer to the Official SAP website for newer courses available. They even tell you for which background (managers, developers) the courses are relevant to. I also go to open.sap.com for new courses. Whether they are consultants (functional and technical), or managers, all of them need to keep themselves up-to-date. They must take new courses and learn about innovation in their technology. For example, HR must now study and try to learn about Successfactors. Even integration of SAP HANA with other software might be an interesting topic of today. There are Fiori and HANA related courses for Basis consultants and the corresponding tracks for developers. Some knowhow of newer technologies is also important for managers and executives, since your decisions may need to be adapted based on the underlying technologies running in your systems. You should know the pros and cons of all technologies in order to make the correct move for your business. Q.4. Many believe an SAP certification improves their chances of getting jobs at competitive salaries. How important are certifications? Which SAP certifications should a buddying developer look forward to obtain? When I did my Certification in October 2000, I used to think that Certifications are not important. But now I have realized, yes, it makes a difference.  Well, certifications are definitely a plus point. They enhance your CV and allow you to have an edge over those who are not certified.  I found some jobs adverts that specifically mention that certification will be required or will be advantageous. However, they are only useful when you have at least 4 years of experience. For a fresh graduate, a certification might not be very useful. A useful SAP consultant/developer is a combination of solid base/foundation of knowledge along with a touch of experience. I suggest all my juniors to go for Certifications in order to strengthen concepts, which include: C_C4C30_1711 - SAP Certified Development Associate – SAP Hybris Cloud for Customer C_CP_11 - SAP Certified Development Associate - SAP Cloud Platform C_FIORDEV_20 - SAP Certified Development Associate - SAP Fiori Application Developer C_HANADEV_13 - SAP Certified Development Associate - SAP HANA C_SMPNHB_30 - SAP Certified Development Associate - SAP Mobile Platform Application Development (SMP 3.0) C_TAW12_750 - SAP Certified Development Associate - ABAP with SAP NetWeaver 7.50 E_HANAAW_12 - SAP Certified Development Specialist - ABAP for SAP HANA For experienced ABAP developers, I suggest getting certified on the newest technologies such as HANA, and Fiori. They may help you get a project quicker and/or at a better rate than others. Q.5. The present buzz is around AI, machine learning, IoT, Big data, and many other emerging technologies. SAP Leonardo works on making it easy to create frameworks for harnessing the latest tech. What are your thoughts on SAP Leonardo? Leonardo is SAP’s response to an AI platform. It should be an important part of SAP’s offerings, mostly built on the SAP cloud platform. SAP has relaunched Leonardo as a digital innovation system. As I understand it, Leonardo allows customers to take advantage of artificial intelligence (AI), machine learning, advanced analytics and blockchain on their company’s data. SAP gives customers an efficient way of using these technologies to solve business issues. It allows you to build a system which, in conjunction with machine learning, searches for results that can be combined with SAP transactions. The benefit with SAP Leonardo is that all the company’s data is available right in the SAP system. Using Leonardo, you have access to all human resources data and any other module data residing in the ERP system. Any company from any industry can make use of Leonardo; it works equally well for retailers, food and beverage companies and medical industries, for organizations working in retail, manufacturing and automotive. An approach that works for one company in a given industry can be applied to other companies in that industry. Suppose a company operates sensors. They can link the sensor data with the data in their SAP systems and even link that with other data, and they can then use the Leonardo capabilities to solve problems or optimize performance. When a problem for one company in an industry is solved, a similar solution may be applied to the entire industry. Yes, in my opinion, Leonardo has a bright future and should be successful. For more information about Leonardo success stories, I encourage readers to check out SAP Leonardo Internet of Things Portfolio & Success Stories. Q. 6. You are currently writing a book on ABAP Objects and Design Patterns expected to be published by the end of 2018. What was your motivation behind writing it? Can you tell us more about ABAP objects? What should readers expect from this book? ABAP and ABAP Objects has gone tremendous changes since some time both on the features (and capability) as well as the syntax. It is the most unsung topic of today. It has been there for quite long but most developers are not aware of it or are not comfortable enough to use them in their day to day work. ABAP is a vast community with developers working in a variety of functional areas. The concepts covered in the book will be generic, allowing the learner to apply them to his or her particular area. This book will cover ABAP objects (the object-oriented extension of the SAP language ABAP) in the latest release of SAP NetWeaver 7.5 and explain the newest advancements. It will start with the programming of objects in general and the basics of ABAP language the developer needs to know to get started. The book will cover the most important topics needed on everyday support jobs and for succeeding in projects. The book will be goal-directed, not a collection of theoretical topics. It won’t just touch on the surface of ABAP objects, but will go in depth from building the basic foundation (e.g., classes and objects created locally and globally) to the intermediary areas (e.g., ALV programming, method chaining, polymorphism, simple and nested interfaces), and then finally into the advanced topics (e.g., shared memory, persistent Objects). The best practices for making better programs via ABAP objects will be shown at the end. No long stories, no boring theory, only pure technical concepts followed by simple examples using coding pertaining to football players. Everything will be presented in a clear, interesting manner, and readers will learn tips and tricks they can apply immediately. Learners, students, new SAP programmers and SAP developers with some experience can use this as an alternative to expensive training books. The book will also save reader’s time searching the internet for help writing new programs. Knowing ABAP objects is key for ABAP developers these days to move forward. Starting from simple ALV reporting requirements, or defining and catching exceptional situations that may occur in a program or even the enhancement technology of BAdIs that lets you enhance standard SAP applications require sound ABAP Objects understanding. In addition, Web Dynpro application development, the Business Object Processing Framework, and even OData service creation to expose data that can be used by Fiori apps all demand solid knowledge of ABAP objects. How to perform predictive forecasting in SAP Analytics Cloud Popular Data sources and models in SAP Analytics Cloud Understanding Text Search and Hierarchies in SAP HANA  

IBM’s SPSS Modeler provides a powerful, versatile workbench that allows you to build efficient and accurate predictive models in no time. What else separates IBM SPSS Modeler from other enterprise analytics tools out there today? To know just that, we talk to arguably two of the most popular members of the SPSS community. [box type="shadow" align="" class="" width=""] Keith McCormick Keith is a career-long practitioner of predictive analytics and data science, has been engaged in statistical modeling, data mining, and mentoring others in this area for more than 20 years. He is also a consultant, an established author, and a speaker. Although his consulting work is not restricted to any one tool, his writing and speaking have made him particularly well known in the IBM SPSS Statistics and IBM SPSS Modeler communities. Jesus Salcedo Jesus is an independent statistical consultant and has been using SPSS products for over 20 years. With a Ph.D., in Psychometrics from Fordham University, he is a former SPSS Curriculum Team Lead and Senior Education Specialist, and has developed numerous SPSS learning courses and trained thousands of users.[/box] In this interview with Packt, Keith and Jesus give us more insights on the Modeler as a tool, the different functionalities it offers, and how to get the most out of it for all your data mining and analytics needs. Key Interview Takeaways IBM SPSS Modeler is easy to get started with but can be a tricky tool to master Knowing your business, your dataset and what algorithms you are going to apply are some key factors to consider before building your analytics solution with SPSS Modeler SPSS Modeler’s scripting language is Python, and the tool has support for running R code IBM SPSS Modeler Essentials helps you effectively learn data mining and analytics, with a focus on working with data than on coding Full Interview Predictive Analytics has garnered a lot of attention of late, and adopting an analytics-based strategy has become the norm for many businesses. Why do you think this is the case?   Jesus: I think this is happening because everyone wants to make better-informed decisions.  Additionally, predictive analytics brings the added benefit of discovering new relationships that you were previously not aware of. Keith: That’s true, but it’s even more exciting when the models are deployed and are potentially driving automated decisions. With over 40 years of combined experience in this field, you are master consultants and trainers, with an unrivaled expertise when it comes to using the IBM SPSS products. Please share with us the story of your journey in this field. Our readers would also love to know how your day-to-day schedule looks like.   Jesus: When I was in college, I had no idea what I wanted to be. I took courses in many areas, however I avoided statistics because I thought it would be a waste of time, after all, what else is there to learn other than calculating a mean and plugging it into fancy formulas (as a kid I loved baseball, so I was very familiar with how to calculate various baseball statistics). Anyway, I took my first statistics course (where I learned SPSS) since it was a requirement, and I loved it. Soon after I became a teaching assistant for more advanced statistics courses and I eventually earned my Ph.D. in Psychometrics, all the while doing statistical consulting on the side. After graduate school, my first job was as an education consultant for SPSS (where I met Keith). I worked at SPSS (and later IBM) for seven years, at first focusing on training customers on statistics and data-mining, and then later on developing course materials for our trainings. In 2013 Keith invited me to join him as an IBM partner, so we both trained customers and developed a lot of new and exciting material in both book and video formats. Currently, I work as an independent statistical and data-mining consultant and my daily projects range from analyzing data for customers, training customers so they can analyze their own data, or creating books and videos on statistics and data mining. Keith: Our careers have lots of similarities. My current day to day is similar too. Lately, about 1/3rd of my year is lecturing and curriculum development for organizations like TDWI (Transforming Data with Intelligence), The Modeling Agency, and UC Irvine Extension. The majority of my work is in predictive analytics consulting. I especially enjoy projects where I’m brought in early and can help with strategy and planning. Then, the coach and mentor take over a team until they are self-sufficient. Sometimes building the team is even more exciting than the first project because I know that they will be able to do many more projects in the future. There is a plethora of predictive analytics tools used today - for desktop and enterprises. IBM SPSS Modeler is one such tool. What advantages does SPSS Modeler have over the others, in your opinion? Keith: One of our good friends who co-authored the IBM SPSS Modeler Cookbook made an interesting comment about this at a conference. He is unique in that he has done one-day seminars using several different software tools. As you know, it is difficult to present data mining in just one day. He said that only with Modeler he is able to spend some time on each of the CRISP-DM phases of a case study in a day. I think he feels this way because it’s among the easiest options to use. We agree. While powerful, and while it takes a whole career to master everything, it is easy to get started. Are there any prerequisites for using SPSS Modeler? How steep is the learning curve in order to start using the tool effectively? Keith: Well, the first thing I want to mention is that there are no prerequisites for our PACKT video IBM SPSS Modeler Essentials. In that, we assume that you are starting from scratch. For the tool in general, there aren’t any specific requisites as such, however knowing your data, and what insights you are looking for always helps. Jesus: Once you are back at the office, in order to be successful on a data mining project or efficiently utilize the tool, you’ll need to know your business, your data, and the modeling algorithm you are using. Keith: The other question that we get all the time is how much statistics and machine learning do you have to know. Our advice is to start with one or maybe two algorithms and learn them well. Try to stick to algorithms that you know. In our PACKT course, we mostly focus on just Decision Trees, which one of the easiest to learn. What do you think are the 3 key takeaways from your course - IBM SPSS Modeler Essentials? The 3 key takeaways from this course, we feel are: Start slow. Don’t pressure yourself to learn everything all at once. There are dozens of “nodes” in Modeler. We introduce the most important ones so start there. Be brilliant in the basics. Get comfortable with the software environment. We recommend the bests ways to organize your work. Don’t rush to Modeling. Remember the Cross Industry Standard Process for Data Mining (CRISP-DM), which we cover in the video. Use it to make sure that you proceed systematically and don’t skip critical steps. IBM recently announced that SPSS Modeler would be available freely for educational usage. How can one make the most of this opportunity? Jesus: A large portion of the work that we have done over the past few years has been to train people on how to analyze data. Professors are in a unique position to expose more students to data mining since we teach only those students whose work requires this type of training, whereas professors can expose a much larger group of people to data mining. IBM offers several programs that support professors, students, and faculty; for more information visit: https://www-01.ibm.com/software/analytics/spss/academic/ Keith: When seeking out a university class, whether it be classroom or online, ask them if they use Modeler or if they allow you to complete your homework assignments in Modeler. We recognize that R based classes are very popular now, but you potentially won’t learn as much about Data Mining. Sometimes too much of the class is spent on coding so you learn R, but learn less about analytics. You want to spend most of the class time actively working with data and producing results. With the rise of open source languages such as R and Python and their applications in predictive analytics, how do you foresee enterprise tools like SPSS Modeler competing with them? Keith: Perhaps surprisingly, we don’t think Modeler does compete with R or Python. A lot of folks don’t know that Python is Modeler’s scripting language. Now, that is an advanced feature, and we don’t cover it in the Essentials video, but learning Python actually increases your knowledge of Modeler. And Modeler supports running R code right in a Modeler stream by using the R nodes. So Modeler power users (or future power users) should keep learning R on their to-do list. If you prefer not to use code, you can produce powerful results without learning either by just using Modeler straight out of the box. So, it really is all up to you. If this interview has sparked your interest in learning more about IBM SPSS Modeler, make sure you check out our video course IBM SPSS Modeler Essentials right away!

Sebastian Raschka is a machine learning expert. He is currently a researcher at Michigan State University, where he is working on computational biology. But he is also the author of Python Machine Learning, the most popular book ever published by Packt. It's a book that has helped to define the field, breaking it out of the purely theoretical and showing readers how machine learning algorithms can be applied to everyday problems. Python Machine Learning was published in 2015, but Sebastian is back with a brand new edition, updated and improved for 2017, working alongside his colleague Vahid Mirjalili. We were lucky enough to catch Sebastian in between his research and working on the new edition to ask him a few questions about what's new in the second edition of Python Machine Learning, and to get his assessment of what the key challenges and opportunities in data science are today. What's the most interesting takeaway from your book? Sebastian Raschka: In my opinion, the key take away from my book is that machine learning can be useful in almost every problem domain. I cover a lot of different subfields of machine learning in my book: classification, regression analysis, clustering, feature extraction, dimensionality reduction, and so forth. By providing hands-on examples for each one of those topics, my hope is that people can find inspiration for applying these fundamental techniques to drive their research or industrial applications. Also, by using well-developed and maintained open source software, makes machine learning very accessible to a broad audience of experienced programmers as well as people who are new to programming. And introducing the basic mathematics behind machine learning, we can appreciate machine learning being more than just black box algorithms, giving readers an intuition of the capabilities but also limitations of machine learning, and how to apply those algorithms wisely. What's new in the second edition? SR: As time and the software world moved on after the first edition was released in September 2015, we decided to replace the introduction to deep learning via Theano. No worries, we didn't remove it! But it got a substantial overhaul and is now based on TensorFlow, which has become a major player in my research toolbox since its open source release by Google in November 2015. Along with the new introduction to deep learning using TensorFlow, the biggest additions to this new edition are three brand new chapters focussing on deep learning applications: A more detailed overview of the TensorFlow mechanics, an introduction to convolutional neural networks for image classification, and an introduction to recurrent neural networks for natural language processing. Of course, and in a similar vein as the rest of the book, these new chapters do not only provide readers with practical instructions and examples but also introduce the fundamental mathematics behind those concepts, which are an essential building block for understanding how deep learning works. What do you think is the most exciting trend in data science and machine learning? SR: One interesting trend in data science and machine learning is the development of libraries that make machine learning even more accessible. Popular examples include TPOT and AutoML/auto-sklearn. Or, in other words, libraries that further automate the building of machine learning pipelines. While such tools do not aim to replace experts in the field, they may be able to make machine learning even more accessible to an even broader audience of non-programmers. However, being to interpret the outcomes of predictive modeling tasks and being to evaluate the results appropriately will always require a certain amount of knowledge. Thus, I see those tools not as replacements but rather as assistants for data scientists, to automate tedious tasks such as hyperparameter tuning. Another interesting trend is the continued development of novel deep learning architectures and the large progress in deep learning research overall. We've seen many interesting ideas from generative adversarial neural networks (GANs), densely connected neural networks (DenseNets), and  ladder networks. Large profress has been made in this field thanks to those new ideas and the continued improvements of deep learning libraries (and our computing infrastructure) that accelerate the implementation of research ideas and the development of these technologies in industrial applications. How has the industry changed since you first started working? SR: Over the years, I have noticed that more and more companies embrace open source, i.e., by sharing parts of their tool chain in GitHub, which is great. Also, data science and open source related conferences keep growing, which means more and more people are not only getting interested in data science but also consider working together, for example, as open source contributors in their free time, which is nice. Another thing I noticed is that as deep learning becomes more and more popular, there seems to be an urge to apply deep learning to problems even if it doesn't necessarily make sense -- i.e., the urge to use deep learning just for the sake of using deep learning. Overall, the positive thing is that people get excited about new and creative approaches to problem-solving, which can drive the field forward. Also, I noticed that more and more people from other domains become more familiar with the techniques used in statistical modeling (thanks to "data science") and machine learning. This is nice, since good communication in collaborations and teams is important, and a given, common knowledge about the basics makes this communication indeed a bit easier. What advice would you give to someone who wants to become a data scientist? SR: I recommend starting with a practical, introductory book or course to get a brief overview of the field and the different techniques that exist. A selection of concrete examples would be beneficial for understanding the big picture and what data science and machine learning is capable of. Next, I would start a passion project while trying to apply the newly learned techniques from statistics and machine learning to address and answer interesting questions related to this project. While working on an exciting project, I think the practitioner will naturally become motivated to read through the more advanced material and improve their skill. What are the biggest misunderstandings and misconceptions people have about machine learning today? Well, there's this whole debate on AI turning evil. As far as I can tell, the fear mongering is mostly driven by journalists who don't work in the field and are apparently looking for catchy headlines. Anyway, let me not iterate over this topic as readers can find plenty of information (from both viewpoints) in the news and all over the internet. To say it with one of the earlier comments, Andrew Ng's famous quote: “I don’t work on preventing AI from turning evil for the same reason that I don’t work on combating overpopulation on the planet Mars." What's so great about Python? Why do you think it's used in data science and beyond? SR: It is hard to tell which came first: Python becoming a popular language so that many people developed all the great open-source libraries for scientific computing, data science, and machine learning or Python becoming so popular due to the availability of these open-source libraries. One thing is obvious though: Python is a very versatile language that is easy to learn and easy to use. While most algorithms for scientific computing are not implemented in pure Python, Python is an excellent language for interacting with very efficient implementations in Fortran, C/C++, and other languages under the hood. This, calling code from computationally efficient low-level languages but also providing users with a very natural and intuitive programming interface, is probably one of the big reasons behind Python's rise to popularity as a lingua franca in the data science and machine learning community. What tools, frameworks and libraries do you think people should be paying attention to? There are many interesting libraries being developed for Python. As a data scientist or machine learning practitioner, I'd especially want to highlight the well-maintained tools from Python core scientific stack: -       NumPy and SciPy as efficient libraries for working with data arrays and scientific computing -       Pandas to read in and manipulate data in a convenient data frame format -       matplotlib for data visualization (and seaborn for additional plotting capabilities and more specialized plots) -       scikit-learn for general machine learning There are many, many more libraries that I find useful in my project. For example, Dask is an excellent library for working with data frames that are too large to fit into memory and to parallelize computations across multiple processors. Or take TensorFlow, Keras, and PyTorch, which are all excellent libraries for implementing deep learning models. What does the future look like for Python? In my opinion, Python's future looks very bright! For example, Python has just been ranked as top 1 programming language by IEEE Spectrum as of July 2017. While I mainly speak of Python from the data science/machine learning perspective, I heard from many people in other domains that they appreciate Python as a versatile language and its rich ecosystem of libraries. Of course, Python may not be the best tool for every problem, it is very well regarded as a "productive" language for programmers who want to "get things done." Also, while the availability of plenty of libraries is one of the strengths of Python, I must also highlight that most packages that have been developed are still being exceptionally well maintained, and new features and improvements to the core data science and machine learning libraries are being added on a daily basis. For instance, the NumPy project, which has been around since 2006, just received a $645,000 grant to further support its continued developed as a core library for scientific computing in Python. At this point, I also want to thank all the developers of Python and its open source libraries that have made Python to what it is today. It's an immensely useful tool to me, and as Python user, I also hope you will consider getting involved in open source -- every contribution is useful and appreciated, small documentation fixes, bug fixes in the code, new features, or entirely new libraries. Again, and with big thanks to the awesome community around it,  I think Python's future looks very bright.

IntroductionIn today’s digital landscape, scalability isn’t just a buzzword—it’s a crucial determinant of success. As the complexity and user base of applications grow, so do the challenges in designing systems that can efficiently handle massive loads. This ongoing challenge of scalability was a key inspiration for my recent book, “System Design Guide for Software Professionals: Build scalable solutions – from fundamental concepts to cracking top tech company interviews” The Scalability Crisis Consider a scenario where a startup’s web application goes viral, resulting in a massive influx of users. This should be a cause for celebration, but instead, it becomes a nightmare as the application starts to slow down significantly. According to a 2024 report by Ably, nearly 85% of companies that experience sudden user growth face significant performance issues due to scalability challenges. The root cause often lies in early design decisions, where the rush to market overshadows the need to build for scale. The building Blocks Approach Over the years, I've found that the "building blocks" approach to system design is crucial for building scalable systems. This method leverages established patterns and components to improve scalability. Here are some of the key building blocks discussed in my book: Distributed Caching: A report from Ahex shows that implementing distributed caching systems like Redis or Memcached can reduce database load by up to 60%, significantly speeding up read operations. Load Balancing: Modern load balancers are more than just traffic directors; they are intelligent systems that optimize resource utilization. A 2024 NGINX report revealed that effective load balancing can improve server efficiency by 40%, enhancing performance during peak loads. Database Sharding: As data grows, a single database becomes a bottleneck. Sharding allows horizontal scaling, and companies that implemented it have seen up to a 5x increase in database throughput, as noted in a Google Cloud study. Message Queues: Asynchronous processing with message queues like Kafka or RabbitMQ can decouple system components and manage traffic spikes. A Gartner report found that this can lead to a 30% reduction in latency during peak usage times. Content Delivery Networks (CDNs): For global applications, CDNs are essential. According to Cloudflare, CDNs can reduce load times by 50-70% for users across different regions, significantly improving user experience. Real-World Application: Scaling a Hypothetical E-commerce Platform Consider an e-commerce platform initially designed as a monolithic application with a single database. This setup worked well for the first 100,000 users, but performance issues began to surface as the user base grew to a million. Approach: Microservices Architecture: Decomposing the monolith into microservices allows independent scaling of each component. Amazon famously adopted this approach, enabling it to handle billions of requests daily. Distributed Caching: Implementing a distributed cache reduced database queries by 70%, as seen in an Akamai case study. Database Sharding: Sharding the database improved query performance by 80%, according to data from MongoDB. Message Queues: Using message queues for resource-intensive tasks led to a 25% reduction in system load, as per RabbitMQ's benchmarks. CDN Deployment: Deploying a global CDN reduced page load times from 3.5 seconds to under 1 second, similar to the optimizations reported by Shopify. Example Metrics: Before optimization: The average page load time was 3.5 seconds, with 30% of requests exceeding 5 seconds during peak hours. After optimization: Reduced to 800ms, with 99% of requests completing under 2 seconds, even during Black Friday. Database query volume: Reduced by 65% through effective caching strategies. Infrastructure costs: Reduced by 40% while handling 5x more daily active users. The AI/ML Twist: Scaling GenAI Infrastructure Scaling infrastructure for Generative AI (GenAI) presents unique challenges. For instance, consider a startup offering a GenAI service for content creation. Initially, 10 high-end GPUs served 1,000 daily users, processing about 1 million tokens daily. However, rapid growth led to the processing of 500 million tokens per day for 100,000 users. Challenges: GPU Scaling: GPU scaling requires managing expensive, specialized hardware. A BCG report notes that effective GPU utilization can save companies up to 50% in infrastructure costs. Token Economy: The varying token loads in GenAI apps pose significant challenges. Stanford University says token loads can vary dramatically, complicating resource prediction. Cost Management: Cloud GPU instances can cost over $10,000/month. AWS reports that optimized GPU management strategies can reduce costs by 30%. Latency Expectations: Users expect near-instant responses. A study by OpenAI found that sub-second latencies are critical for real-time applications. Solutions: Dynamic GPU Allocation: Implementing dynamic GPU allocation can reduce idle times and costs, as observed by Google Cloud. Request Batching: Grouping user requests can improve GPU throughput by 20%, according to Azure AI. Model Optimization: Techniques like quantization and pruning can reduce model size by 70% and increase inference speed by 50%, as highlighted in MIT’s research. Tiered Service Levels: Offering different response time guarantees can optimize resource allocation, as shown by Microsoft Azure. Distributed Inference: Splitting models across GPUs or using CPU inference can reduce GPU load by 40%, based on Google AI's findings. Example Metrics: Cost per 1000 tokens: Reduced from $0.05 to $0.015 through optimized GPU management. p99 Latency: Improved from 5 seconds to 1.2 seconds. Infrastructure scaling: Handled 1 billion daily tokens with only a 20x increase in costs, compared to the 100x increase projected by traditional scaling methods. Beyond Technology: The Human Factor While technology is critical, fostering a culture of scalability is equally important. A Harvard Business Review article emphasized that companies prioritizing scalable culture from the start are 50% more likely to sustain growth without operational roadblocks. Strategies: Encourage developers to consider scalability from the outset. Invest in monitoring and observability tools to detect issues early. Regularly conduct load tests and capacity planning. Adopt a DevOps culture to break down silos between development and operations. The Road Ahead As we move forward, innovations in edge computing, serverless architectures, and large-scale machine learning will continue to push the boundaries of scalability. However, the foundational principles of scalable system design—modularity, redundancy, and efficient resource utilization—remain vital. By mastering these principles, you can build systems that grow and adapt to an ever-changing digital landscape, whether you’re scaling a web application or pioneering generative AI technologies. Remember, scalability is not a destination but a journey, and having the right building blocks makes all the difference.