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How-To Tutorials - Business Intelligence

7 Articles
article-image-mastering-performance-tuning-with-dax-studio-and-vertipaq-analyzer
Thomas LeBlanc, Bhavik Merchant
03 Dec 2024
15 min read
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Mastering Performance Tuning with DAX Studio and VertiPaq Analyzer

Thomas LeBlanc, Bhavik Merchant
03 Dec 2024
15 min read
This article is an excerpt from the book, "Microsoft Power BI Performance Best Practices - Second Edition", by Thomas LeBlanc, Bhavik Merchant. Overcome common challenges in data management, visualization, and security with this updated edition of Microsoft Power BI Performance Best Practices, and ramp-up your Power BI solutions, achieve faster insights, and drive better business outcomes.IntroductionOptimizing performance and storage in Power BI and Analysis Services can be a complex task. However, tools like DAX Studio and VertiPaq Analyzer simplify this process by providing insightful metrics and performance-tuning capabilities. This article explores how to leverage these tools to analyze semantic models, identify performance bottlenecks, and optimize DAX queries. We'll discuss key features such as viewing model metrics, capturing and analyzing query traces, and testing optimizations using DAX Studio's query editor.Tuning with DAX Studio and VertiPaq AnalyserDAX Studio, as the name implies, is a tool centered on DAX queries. It provides a simple yet intuitive interface with powerful features to browse and query Analysis Services semantic models. We will cover querying later in this section. For now, let’s look deeper into semantic models.The Analysis Services engine has supported dynamic management views (DMVs) for over a decade. These views refer to SQL-like queries that can be executed on Analysis Services to return information about semantic model objects and operations.VertiPaq Analyzer is a utility that uses publicly documented DMVs to display essential information about which structures exist inside the semantic model and how much space they occupy. It started life as a standalone utility, published as a Power Pivot for an Excel workbook, and still exists in that form today. In this chapter, we will refer to its more recent incarnation as a built-in feature of DAX Studio 3.0.11.It is interesting to note that VertiPaq is the original name given to the compressed column store engine within Analysis Services (Verti referring to columns and Paq referring to compression).Analyzing model size with VertiPaq AnalyzerVertiPaq Analyzer is built into DAX Studio as the View Metrics features, found in the Advanced tab of the toolbar. You simply click the icon to have DAX Studio run the DMVs for you and display statistics in a tabular form. This is shown in the following figure:Figure 6.8 – Using View Metrics to generate VertiPaq Analyzer statsYou can switch to the Summary tab of the VertiPaq Analyzer pane to get an idea of the overall total size of the model along with other summary statistics, as shown in the following figure:Figure 6.9 – Summary tab of VertiPaq AnalyzerThe Total Size metric provided in the previous figure will often be larger than the size of the semantic model on disk (as a .pbix file or Analysis Services .abf backup). This is because there are additional structures required when the model is loaded into memory, which is particularly true of Import mode semantic models.In Chapter 2, Exploring Power BI Architecture and Configuration, we learned about Power BI’s compressed column storage engine. The DMV statistics provided by VertiPaq Analyzer let us see just how compressible columns are and how much space they are taking up. It also allows us to observe other objects, such as relationships.The Columns tab is a great way to see whether you have any columns that are very large relative to others or the entire dataset. The following figure shows the columns view for the same model we saw in Figure 6.9. You can see how from 238 columns, a single column called SalesOrderNumber takes up a staggering 22.40% of the whole model size! It’s interesting to see its Cardinality (or uniqueness) value is about twelve times lower than the next largest column (SalesKey):|Figure 6.10 – Two columns monopolizing the semantic modelIn Figure 6.10, we can also see that Data Type is String for Online Sale-SalesOrderNumber, which was a column suggested by Tabular Editor to have a large dictionary footprint. These statistics would lead you to deduce that this column contains long, unique test values that do not compress well because there is a large cardinality. Indeed, in this case, the column contains a sales order number that is unique to each order plus is not used to group or slice analytical data in a Power BI report well.This analysis may lead you to re-evaluate the need for this level of reporting in the analysis of sales data. You’d need to ask yourself whether the extra storage space and time taken to build compressed columns and potentially other structures is worth it for your business case. In cases of highly detailed data such as this where you do not need detail-level sales order data, consider limiting the analysis to customer-related data such as demographics or date attributes such as year and month.Now, let’s learn about how DAX Studio can help us with performance analysis and improvement.Performance tuning the data model and DAXThe first-party option for capturing Analysis Services traces is SQL Server Profiler. When starting a trace, you must identify exactly which events to capture, which requires some knowledge of the trace events and what they contain. Even with this knowledge, working with the trace data in Profi ler can be tough since the tool was designed primarily to work with SQL Server application traces. The good news is that DAX Studio can start an Analysis Services server trace and then parse and format all the data to show you relevant results in a well-presented way within its user interface. It allows us to both tune and measure queries in a single place and provides features for Analysis Services that make it a good alternative SQL profiler for tuning semantic models.Capturing and replaying queriesThis All Queries command in the Traces section of the DAX Studio toolbar will start a trace against the semantic model you have connected to. Figure 6.11 shows the result when a trace is successfully started:Figure 6.11 – Query trace successfully started in DAX StudioOnce your trace has started, you can interact with the semantic model outside DAX Studio, and it will capture queries for you. How you interact with the semantic model depends on where it is. For a semantic model running on your computer in Power BI Desktop, you would simply interact with the report. This would generate queries that DAX Studio will see. The All Queries tab at the bottom of the tool is where the captured queries are listed in time order with durations in milliseconds. The following figure shows two queries captured when opening the Unique by Account No page from the Slow vs Fast Measures.pbix sample file:Figure 6.12 – Queries captured by DAX StudioThe preceding queries come from a screen that has the same table results in a visual, but two different DAX measures that calculate the aggregation. These measures make one table come back in less than a second while the other returns in about 17 seconds. The following figure shows the page in the report:Figure 6.13 – Tables with the same results but from using different measuresThe following screenshot shows the results of the Performance Analyzer for the tables previously.Observe how one query took over 17 seconds, whereas the other took under 1 second:Figure 6.14 – Vastly different query durations for the same visual resultIn Figure 6.12, the second query was double-clicked to bring the DAX text to the editor. You can modify this query in DAX Studio to test performance changes. We see here that the DAX expression for the UniqueRedProducts_Slow measure was not efficient. We’ll learn a technique to optimize queries soon, but first, we need to learn about capturing query performance traces.Obtaining query timingsTo get detailed query performance information, you can use the Server Timings command shown in Figure 6.11. After starting the trace, you can run queries and then use the Server Timings tab to see how the engine executed the query, as shown in the following figure:Figure 6.15 – Server Timings showing detailed query performance statisticsFigure 6.15 gives very useful information. FE and SE refer to the formula engine and storage engine. The storage engine is fast and multi-threaded, and its job is fetching data. It can apply basic logic such as filtering data to retrieve only what is needed. The formula engine is single-threaded, and it generates a query plan, which is the physical steps required to compute the result. It also performs calculations on the data such as joins, complex filters, aggregations, and lookups. We want to avoid queries that spend most of the time in the formula engine, or that execute many queries in the storage engine. The bottom-left section of Figure 6.15 shows that we executed almost 4,900 SE queries. The list of queries to the right shows many queries returning only one result, which is suspicious.For comparison, we look at timing for the fastest version of the query and we see the following:Figure 6.16 – Server Timings for a fast version of the queryIn Figure 6.16, we can see that only three server engine queries were run this time, and the result was obtained much faster (milliseconds compared to seconds).The faster DAX measure was as follows:UniqueRedProducts_Fast = CALCULATE( DISTINCTCOUNT('SalesOrderDetail'[ProductID]), 'Product'[Color] = "Red" )The slower DAX measure was as follows:UniqueRedProducts_Slow = CALCULATE( DISTINCTCOUNT('SalesOrderDetail'[ProductID]), FILTER('SalesOrderDetail', RELATED('Product'[Color]) = "Red"))TipThe Analysis Services engine does use data caches to speed up queries. These caches contain uncompressed query results that can be reused later to save time fetching and decompressing data. You should use the Clear Cache button in DAX Studio to force these caches to be cleared and get a proper worst-case performance measure. This is visible in the menu bar in Figure 6.11.We will build on these concepts when we look at DAX and model optimizations in later chapters. Now, let’s look at how we can experiment with DAX and query changes in DAX Studio.Modifying and tuning queriesEarlier in this section, we saw how we could capture a query generated by a Power BI visual and then display its text. A nice trick we can use here is to use query-scoped measures to override the measure definition and see how performance differs.The following figure shows how we can search for a measure, right-click, and then pull its definition into the query editor of DAX Studio:Figure 6.17 – The Define Measure option and result in the Query paneWe can now modify the measure in the query editor, and the engine will use the local definition instead of the one defined in the model! This technique gives you a fast way to prototype DAX enhancements without having to edit them in Power BI and refresh visuals over many iterations.Remember that this technique does not apply any changes to the dataset you are connected to. You can optimize expressions in DAX Studio, then transfer the definition to Power BI Desktop/Visual Studio when ready. The following figure shows how we changed the definition of UniqueRedProducts_ Slow in a query-scoped measure to get a huge performance boast:Figure 6.18 – Modified measure giving better resultsThe technique described here can be adapted to model changes too. For example, if you wanted to determine the impact of changing a relationship type, you could run the same queries in DAX Studio before and after the change to draw a comparison.Here are some additional tips for working with DAX Studio:Isolate measure: When performance tuning a query generated by a report visual, comment out complex measures and then establish a baseline performance score. Th en, add each measure back to the query individually and check the speed. This will help identify the slowest measures in the query and visual context.Work with Desktop Performance Analyzer traces: DAX Studio has a facility to import the trace files generated by Desktop Performance Analyzer. You can import trace files using the Load Perf Data button located next to All Queries highlighted in Figure 6.12. This trace can be captured by one person and then shared with a DAX/modeling expert who can use DAX Studio to analyze and replay their behavior. The following figure shows how DAX Studio formats the data to make it easy to see which visual component is taking the most time. It was generated by viewing each of the three report pages in the Slow vs Fast Measures.pbix sample file:Figure 6.19 – Performance Analyzer trace shows the slowest visual in the reportExport/import model metrics: DAX Studio has a facility to export or import the VertiPaq model metadata using .vpax files. These files do not contain any of your data. They contain table names, column names, and measure definitions. If you are not concerned with sharing these definitions, you can provide .vpax files to others if you need assistance with model optimizationConclusionDAX Studio and VertiPaq Analyzer are indispensable tools for anyone working with Power BI or Analysis Services models. From detailed model size analysis to advanced performance tuning, these tools empower users to identify inefficiencies and implement optimizations effectively. By using their robust features, such as the ability to view metrics, trace query performance, and prototype query changes, professionals can ensure their models are both efficient and scalable. Mastery of these tools lays a solid foundation for building high-performing, resource-efficient analytical solutions.Author BioThomas LeBlanc is a seasoned Business Intelligence Architect at Data on the Geaux, where he applies his extensive skillset in dimensional modeling, data visualization, and analytical modeling to deliver robust solutions. With a Bachelor of Science in Management Information Systems from Louisiana State University, Thomas has amassed over 30 years of experience in Information Technology, transitioning from roles as a software developer and database administrator to his current expertise in business intelligence and data warehouse architecture and management.Throughout his career, Thomas has spearheaded numerous impactful projects, including consulting for various companies on Power BI implementation, serving as lead database administrator for a major home health care company, and overseeing the implementation of Power BI and Analysis Service for a large bank. He has also contributed his insights as an author to the Power BI MVP book.Thomas is recognized as a Microsoft Data Platform MVP and is actively engaged in the tech community through his social media presence, notably as TheSmilinDBA on Twitter and ThePowerBIDude on Bluesky and Mastodon. With a passion for solving real-world business challenges with technology, Thomas continues to drive innovation in the field of business intelligence.Bhavik Merchant has nearly 18 years of deep experience in Business Intelligence. He is currently the Director of Product Analytics at Salesforce. Prior to that, he was at Microsoft, first as a Cloud Solution Architect and then as a Product Manager in the Power BI Engineering team. At Power BI, he led the customer-facing insights program, being responsible for the strategy and technical framework to deliver system-wide usage and performance insights to customers. Before Microsoft, Bhavik spent years managing high-caliber consulting teams delivering enterprise-scale BI projects. He has provided extensive technical and theoretical BI training over the years, including expert Power BI performance training he developed for top Microsoft Partners globally.
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Fatema Patrawala
17 Dec 2019
8 min read
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15 things every BI professional should know about Tableau

Fatema Patrawala
17 Dec 2019
8 min read
“The art and practice of visualizing data is becoming ever more important in bridging the human-computer gap to mediate analytical insight in a meaningful way.” ―Edd Dumbill Tableau is a powerful data visualization and discovery tool. It is an important part of a data analyst or data scientist’s - skill set, with many organizations specifying it as a key skill in job adverts. In this article, we’ll take a look at few things in Tableau you need to know to successfully make a mark in your business intelligence career. While architecture of traditional BI tools has hardware limitations, Tableau does not have such dependencies and it can function independently and requires minimum hardware support. Traditional tools are based on a complex set of technologies when Tableau is based on Associative Search technology making it intuitive, fast and dynamic. Tableau supports in-memory, multi-thread and multi-core computing and more advanced capabilities while traditional BI tools do not offer such functionalities. Various Tableau products Tableau Desktop is a self service business analytics and data visualization suite that anyone can use. With tableau desktop, you can extract massive data offline from your data warehouse for live up to date data analysis. Tableau Online / Tableau Server is an online hosting platform designed for enterprise users. It lets users working on Tableau publish and share dashboards across organization and teams. Tableau Reader is a free desktop application that enables you to open and view visualizations that are built in Tableau Desktop. Tableau Public is a free Tableau software which you can use to make visualizations but you will need to save your workbook or worksheets in the Tableau Server for anyone else to view them. Different data types in Tableau All fields in a data source have a data type. The data type reflects the kind of information stored in that field, for example integers (410), dates (1/23/2015) and strings (“Wisconsin”). The data type of a field is identified in the Data pane by one of the icons shown below. Data type icons in Tableau Icon Data type Text (string) values Date values Date & Time values Numerical values Boolean values (relational only) for example True/False Geographic values (used with maps) Cluster Group   Source: Tableau website Measures and Dimensions in Tableau Measures contain numeric, quantitative values that you can measure. Measures can be aggregated. When you drag a measure into the view, Tableau applies an aggregation to that measure (by default). Dimensions, on the other hand, contain qualitative values (such as names, dates, or geographical data). You can use dimensions to categorize, segment, and reveal the details in your data. Dimensions affect the level of detail in the view. Ways to connect data in Tableau We can either connect live to your data set or extract data into Tableau. Live: Connecting live to a data set leverages its computational processing and storage. New queries will go to the database and will be reflected as new or updated within the data. Extract: The Extract API allows you to programmatically extract and combine any data sources for use in Tableau. There can be multiple data source connections to different sources in the same workbook. Each connection will show up under the Data tab on the left sidebar. The benefit of Tableau extract over live connection is that extract can be used anywhere without any connection and you can build your own visualization without connecting to database. You can read a complete section on how to extract data in Tableau from this book, Learning Tableau 2019 - Third Edition, written by Joshua Milligan. This book takes you through the foundations of the Tableau 2019 paradigm to the advanced topics.  Joins and Blends in Tableau Joining tables and blending data sources are two different ways to link related data together in Tableau. Joins are performed to link tables of data together on a row-by-row basis. Blends are performed to link together multiple data sources at an aggregate level.  Different filters in Tableau and different use cases in which these filters are more relevant than others In Tableau, filters are used to restrict the data from database. Often, you will want to filter data in Tableau in order to perform an analysis on a subset of data, narrow your focus, or drill into detail. Tableau offers multiple ways to filter data. If you want to limit the scope of your analysis to a subset of data, you can filter the data at the source using one of the following techniques: Data Source Filters are applied before all other filters and are useful when you want to limit your analysis to a subset of data. These filters are applied before any other filters. Extract Filters limit the data that is stored in an extract (.tde or .hyper). Data source filters are often converted into extract filters if they are present when you extract the data. Custom SQL Filters can be accomplished using a live connection with custom SQL, which has a Tableau parameter in the WHERE clause.    Dual axis in Tableau Dual Axis is an excellent phenomenon supported by Tableau that helps users view two scales of two measures in the same graph. Many websites like Indeed.com and other make use of dual axis to show the comparison between two measures and their growth rate in a septic set of years. Dual axis let you compare multiple measures at once, having two independent axis layered on top of one another.  Key components of a Tableau Dashboard Horizontal – Horizontal layout containers allow the designer to group worksheets and dashboard components left to right across your page and edit the height of all elements at once. Vertical – Vertical containers allow the user to group worksheets and dashboard components top to bottom down your page and edit the width of all elements at once. Text – All textual fields. Image Extract  – A Tableau workbook is in XML format. In order to extract images, Tableau applies some codes to extract an image which can be stored in XML. Web [URL ACTION] – A URL action is a hyperlink that points to a Web page, file, or other web-based resource outside of Tableau. You can use URL actions to link to more information about your data that may be hosted outside of your data source. To make the link relevant to your data, you can substitute field values of a selection into the URL as parameters. If you want to learn how to design dashboards in Tableau, this book Learning Tableau 2019, will give you a step by step process for designing dashboards.  Why automate reports in Tableau Once you have automated reporting, you’ll have time to spend on innovative projects. What can be done manually could be performed by automation, delivering the same results in a fraction of the time. Reducing such a time-consuming and repetitive task will make you more productive, and more efficient.  What is story in Tableau? Why would create a story and what are they used for? A story is a sheet that contains a sequence of worksheets or dashboards that work together to convey information. You can create stories to show how facts are connected, provide context, demonstrate how decisions relate to outcomes, or simply make a compelling case. Each individual sheet in a story is called a story point. The primary objective of creating stories in Tableau is to communicate data to a certain audience with an intended result.  How can you create stories in Tableau? There is a feature in Tableau named as Stories that allows you to tell a story using interactive snapshots of dashboards and views. The snapshots become points in a story. This allows you to construct guided narrative or even an entire presentation. Read this chapter, ‘Telling a Data Story with Dashboards’ from this book, Learning Tableau 2019, to create insightful dashboards in Tableau.    How to embed views into Webpages? You can embed interactive Tableau views and dashboards into web pages, blogs, wiki pages, web applications, and intranet portals. Embedded views update as the underlying data changes, or as their workbooks are updated on Tableau Server. Embedded views follow the same licensing and permission restrictions used on Tableau Server. That is, to see a Tableau view that’s embedded in a web page, the person accessing the view must also have an account on Tableau Server. Alternatively, if your organization uses a core-based license on Tableau Server, a Guest account is available. This allows people in your organization to view and interact with Tableau views embedded in web pages without having to sign in to the server. Contact your server or site administrator to find out if the Guest user is enabled for the site you publish to.  What is Tableau Prep? Can we clean messy data with Tableau? Tableau Prep extends the Tableau platform with robust options for cleaning and structuring data for analysis in Tableau. In the same way that Tableau Desktop provides a hands-on, visual experience for visualizing and analyzing data, Tableau Prep provides a hands-on, visual experience for cleaning and shaping data. If you wish to know more about Tableau Prep or how to clean messy data to create powerful data visualizations and unlock intelligent business insights, read this book Learning Tableau 2019, written by Joshua N. Milligan. ‘Tableau Day’ highlights: Augmented Analytics, Tableau Prep Builder and Conductor, and more! Alteryx vs. Tableau: Choosing the right data analytics tool for your business How to do data storytelling well with Tableau [Video]
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Richard Gall
19 Sep 2019
11 min read
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The best business intelligence tools 2019: when to use them and how much they cost

Richard Gall
19 Sep 2019
11 min read
Business intelligence is big business. Salesforce’s purchase of Tableau earlier this year (for a cool $16 billion) proves the value of a powerful data analytics platform, and demonstrates how the business intelligence space is reshaping expectations and demands in the established CRM and ERP marketplace. To a certain extent, the amount Salesforce paid for Tableau highlights that when it comes to business intelligence, tooling is paramount. Without a tool that fits the needs and skill levels of those that need BI and use analytics, discussions around architecture and strategy are practically moot. So, what are the best business intelligence tools? And more importantly, how do they differ from one another? Which one is right for you? Read next: 4 important business intelligence considerations for the rest of 2019 The best business intelligence tools 2019 Tableau Let’s start with the obvious one: Tableau. It’s one of the most popular business intelligence tools on the planet, and with good reason; it makes data visualization and compelling data storytelling surprisingly easy. With a drag and drop interface, Tableau requires no coding knowledge from users. It also allows users to ask ‘what if’ scenarios to model variable changes, which means you can get some fairly sophisticated insights with just a few simple interactions. But while Tableau is undoubtedly designed to be simple, it doesn’t sacrifice complexity. Unlike other business intelligence tools, Tableau allows users to include an unlimited number of datapoints in their analytics projects. When should you use Tableau and how much does it cost? The core Tableau product is aimed at data scientists and data analysts who want to be able to build end-to-end analytics pipelines. You can trial the product for free for 14 days, but this will then cost you $70/month. This is perhaps one of the clearest use cases - if you’re interested and passionate about data, Tableau practically feels like a toy. However, for those that want to employ Tableau across their organization, the product offers a neat pricing tier. Tableau Creator is built for individual power users - like those described above, Tableau Explorer for self-service analytics, and Tableau Viewer for those that need access to Tableau for limited access to dashboard and analytics. Tableau eBooks and videos Mastering Tableau 2019.1 - Second Edition Tableau 2019.x Cookbook Getting Started with Tableau 2019.2 - Second Edition Tableau in 7 Steps [Video] PowerBI PowerBI is Microsoft’s business intelligence platform. Compared to Tableau it is designed more for reporting and dashboards rather than data exploration and storytelling. If you use a wide range of Microsoft products, PowerBI is particularly powerful. It can become a centralized space for business reporting and insights. Like Tableau, it’s also relatively easy to use. With support from Microsoft Cortana - the company’s digital assistant - it’s possible to perform natural language queries. When should you use PowerBI and how much does it cost? PowerBI is an impressive business intelligence product. But to get the most value, you need to be committed to Microsoft. This isn’t to say you shouldn’t be - the company has been on form in recent years and appears to really understand what modern businesses and users need. On a similar note, a good reason to use PowerBI is for unified and aligned business insights. If Tableau is more suited to personal exploration, or project-based storytelling, PowerBI is an effective option for organizations that want more clarity and shared visibility on key performance metrics. This is reflected in the price. For personal users the desktop version of PowerBI is free, while a pro license is $9.99 a month. A premium plan which includes cloud resources (storage and compute) starts at $4,995. This is the option for larger organizations that are fully committed to the Microsoft suite and has a clear vision of how it wants to coordinate analytics and reporting. PowerBI eBooks and videos Learn Power BI Microsoft Power BI Quick Start Guide Learning Microsoft Power BI [Video] Qlik Sense and QlikView Okay, so here we’re going to include two business intelligence products together: Qlik Sense and QlikView. Obviously, they’re both part of the same family - they’re built by business intelligence company Qlik. More importantly, they’re both quite different products. What’s the difference between Qlik Sense and QlikView? As we’ve said, Qlik Sense and QlikView are two different products. QlikView is the older and more established tool. It’s what’s usually described as a ‘guided analytics’ platform, which means dashboards and analytics applications can be built for end users. The tool gives freedom to engineers and data scientists to build what they want but doesn’t allow end users to ‘explore’ data in any more detail than what is provided. QlikView is quite a sophisticated platform and is widely regarded as being more complex to use than Tableau or PowerBI. While PowerBI or Tableau can be used by anyone with an intermediate level of data literacy and a willingness to learn, QlikView will always be the preserve of data scientists and analysts. This doesn’t make it a poor choice. If you know how to use it properly, QlikView can provide you with more in-depth analysis than any other business intelligence platforms, helping users to see patterns and relationships across different data sets. If you’re working with big data, for example, and you have a team of data scientists and data engineers, QlikView is a good option. Qlik Sense, meanwhile, could be seen as Qlik’s attempt to compete with the likes of Tableau and PowerBI. It’s a self-service BI tool, which allows end users to create their own data visualisations and explore data through a process of ‘data discovery’. When should you use QlikView and how much does it cost? QlikView should be used when you need to build a cohesive reporting and business intelligence solution. It’s perfect for when you need a space to manage KPIs and metrics across different teams. Although a free edition is available for personal use, Qlik doesn’t publish prices for enterprise users. You’ll need to get in touch with the company’s sales team to purchase. QlikView eBooks and videos QlikView: Advanced Data Visualization QlikView Dashboard Development [Video] When should you use Qlik Sense and how much does it cost? Qlik Sense should be used when you have an organization full of people curious and prepared to get their hands on their data. If you already have an established model of reporting performance, Qlik Sense is a useful extra that can give employees more autonomy over how data can be used. When it comes to pricing, Qlik Sense is one of the more complicated business intelligence options. Like QlikView, there’s a free option for personal use, and again like QlikView, there’s no public price available - so you’ll have to connect with Qlik directly. To add an additional layer of complexity, there’s also a product called ‘Cloud Basic’ - this is free and can be shared between up to 5 users. It’s essentially a SaaS version of the Qlik Sense product. If you need to add more than 5 users, it costs $15 per user/month. Qlik Sense eBooks and videos Mastering Qlik Sense [Video] Qlik Sense Cookbook - Second Edition Data Storytelling with Qlik Sense [Video] Hands-On Business Intelligence with Qlik Sense Read next: Top 5 free Business Intelligence tools Splunk Splunk isn’t just a business intelligence tool. To a certain extent, it’s related to application monitoring and logging tools such as Datadog, New Relic, and AppDynamics. It’s built for big data and real-time analytics, which means that it’s well-suited to offering insights on business processes and product performance. The copy on the Splunk website talks about “real-time visibility across the enterprise” and describes Splunk as a “data-to-everything” platform. The product, then, is pitching itself as something that can embed itself inside existing systems, and bring insight and intelligence to places and spaces where it’s particularly valuable. This is in contrast to PowerBI and Tableau, which are designed for exploration and accessibility. This isn’t to say that Splunk doesn’t enable sophisticated data exploration, but rather that it is geared towards monitoring systems and processes, understanding change. It’s a tool built for companies that need full transparency - or, in other words, dynamic operational intelligence. When should you use Splunk and how much does it cost? Splunk is a tool that should be used if you’re dealing with dynamic and real-time data. If you want to be able to model and explore wide-ranging existing sets of data Tableau or PowerBI are probably a better bet. But if you need to be able to make decisions in an active and ongoing scenario, Splunk is a tool that can provide substantial support. The reason that Splunk is included as a part of this list of business intelligence tools is because real-time visibility and insight is vital for businesses. Typically understanding application performance or process efficiency might have been embedded within particular departments, such as a centralized IT function. Now, with businesses dependent upon operational excellence, and security and reliability in the digital arena becoming business critical, Splunk is a tool that deserves its status inside (and across) organizations. Splunk’s pricing is complicated. Prices are generally dependent on how much data you want to index - or, in other words, how much you’re giving Splunk to deal with. But to add to that, Splunk also have a perpetual license ( a one time payment) and an annual term license, which needs to be renewed. So, you can index 1GB/day for $4,500 on a perpetual license, or $1,800 on an annual license. If you want to learn more about Splunk’s pricing option, this post is very useful. Splunk eBooks and videos Splunk 7 Essentials [E-Learning] Splunk 7.x Quick Start Guide Splunk Operational Intelligence Cookbook - Third Edition IBM Cognos IBM Cognos is IBM’s flagship business intelligence tool. It’s probably best viewed as existing somewhere between PowerBI and Tableau. It’s designed for reporting dashboards that allow monitoring and analytics, but it is nevertheless also intended for self-service. To that end, you might say it’s more limited in capabilities than PowerBI, but it’s nevertheless more accessible for non-technical end users to explore data. It’s also relatively easy to integrate with other systems and data sources. So, if your data is stored in Microsoft or Oracle cloud services and databases, it’s relatively straightforward to get started with IBM Cognos. However, it’s worth noting that despite the accesibility of IBM’s product, it still needs centralized control and implementation. It doesn’t offer the level of ease that you get with Tableau, for example. When should you use IBM Cognos and how much does it cost? Cognos is perhaps the go-to option if PowerBI and Tableau don’t quite work for you. Perhaps you like the idea of Tableau but need more centralization. Or maybe you need a strong and cohesive reporting system but don’t feel prepared to buy into Microsoft. This isn’t to make IBM Cognos sound like the outsider - in fact, from an efficiency perspective it’s possibly the best way to ensure to ensure some degree of portability between data sources and to manage the age-old problem of data silos. If you’re not quite sure what business intelligence tool is right for you, it’s well worth taking advantage of Cognos’s free trial - you get unlimited access for a month. If you like what you get, you then have a choice between a premium version - which costs $70 per user/month, and the enterprise plan, the price of which isn’t publicly available. IBM Cognos eBooks and videos IBM Cognos Framework Manager [Video] IBM Cognos Report Studio [Video] IBM Cognos Connection and Workspace Advanced [Video] Conclusion: To choose the best business intelligence solution for your organization, you need to understand your needs and goals Business intelligence is a crowded market. The products listed here are the tip of the iceberg when it comes to analytics, monitoring, and data visualization. This is good and bad - it means there are plenty of options and opportunities, but it also means that sorting through the options to find the right one might take up some of your time. That’s okay though - if possible, try to take advantage of free trial periods. And if you’re in a rush to get work done, use them on active projects. You could even allocate different platforms and tools to different team members and get them to report on what worked well and what didn’t. That way you can have documented insights on how the products might actually be used within the organization. This will help you to better reach a conclusion about the best tool for the job. Business intelligence done well can be extremely valuable - so don’t waste money and don’t waste time on tools that aren’t going to deliver what you need.
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Richard Gall
16 Sep 2019
7 min read
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4 important business intelligence considerations for the rest of 2019

Richard Gall
16 Sep 2019
7 min read
Business intelligence occupies a strange position, often overshadowed by fields like data science and machine learning. But it remains a critical aspect of modern business - indeed, the less attention the world appears to pay to it, the more it is becoming embedded in modern businesses. Where analytics and dashboards once felt like a shiny and exciting interruption in our professional lives, today it is merely the norm. But with business intelligence almost baked into the day to day routines and activities of many individuals, teams, and organizations, what does this actually mean in practice. For as much as we’d like to think that we’re all data-driven now, the reality is that there’s much we can do to use data more effectively. Research confirms that data-driven initiatives often fail - so with that in mind here’s what’s important when it comes to business intelligence in 2019. Popular business intelligence eBooks and videos Oracle Business Intelligence Enterprise Edition 12c - Second Edition Microsoft Power BI Quick Start Guide Implementing Business Intelligence with SQL Server 2019 [Video] Hands-On Business Intelligence with Qlik Sense Hands-On Dashboard Development with QlikView Getting the balance between self-service business intelligence and centralization Self-service business intelligence is one of the biggest trends to emerge in the last two years. In practice, this means that a diverse range of stakeholders (marketers and product managers for example) have access to analytics tools. They’re no longer purely the preserve of data scientists and analysts. Self-service BI makes a lot of sense in the context of today’s data rich and data-driven environment. The best way to empower team members to actually use data is to remove any bottlenecks (like a centralized data team) and allow them to go directly to the data and tools they need to make decisions. In essence, self-service business intelligence solutions are a step towards the democratization of data. However, while the notion of democratizing data sounds like a noble cause, the reality is a little more complex. There are a number of different issues that make self-service BI a challenging thing to get right. One of the biggest pain points, for example, are the skill gaps of teams using these tools. Although self-service BI should make using data easy for team members, even the most user-friendly dashboards need a level of data literacy to be useful. Read next: What are the limits of self-service BI? Many analytics products are being developed with this problem in mind. But it’s still hard to get around - you don’t, after all, want to sacrifice the richness of data for simplicity and accessibility. Another problem is the messiness of data itself - and this ultimately points to one of the paradoxes of self-service BI. You need strong alignment - centralization even - if you’re to ensure true democratization. The answer to all this isn’t to get tied up in decentralization or centralization. Instead, what’s important is striking a balance between the two. Decentralization needs centralization - there needs to be strong governance and clarity over what data exists, how it’s used, how it’s accessed - someone needs to be accountable for that for decentralized, self-service BI to actually work. Read next: How Qlik Sense is driving self-service Business Intelligence Self-service business intelligence: recommended viewing Power BI Masterclass - Beginners to Advanced [Video] Data storytelling that makes an impact Data storytelling is a phrase that’s used too much without real consideration as to what it means or how it can be done. Indeed, all too often it’s used to refer to stylish graphs and visualizations. And yes, stylish graphs and data visualizations are part of data storytelling, but you can’t just expect some nice graphics to communicate in depth data insights to your colleagues and senior management. To do data storytelling well, you need to establish a clear sense of objectives and goals. By that I’m not referring only to your goals, but also those of the people around you. It goes without saying that data and insight needs context, but what that context should be, exactly, is often the hard part - objectives and aims are perhaps the straightforward way of establishing that context and ensuring your insights are able to establish the scope of a problem and propose a way forward. Data storytelling can only really make an impact if you are able to strike a balance between centralization and self-service. Stakeholders that use self-service need confidence that everything they need is both available and accurate - this can only really be ensured by a centralized team of data scientists, architects, and analysts. Data storytelling: recommend viewing Data Storytelling with Qlik Sense [Video] Data Storytelling with Power BI [Video] The impact of cloud It’s impossible to properly appreciate the extent to which cloud is changing the data landscape. Not only is it easier than ever to store and process data, it’s also easy to do different things with it. This means that it’s now possible to do machine learning, or artificial intelligence projects with relative ease (the word relative being important, of course). For business intelligence, this means there needs to be a clear strategy that joins together every piece of the puzzle, from data collection to analysis. This means there needs to be buy-in and input from stakeholders before a solution is purchased - or built - and then the solution needs to be developed with every individual use case properly understood and supported. Indeed, this requires a combination of business acumen, soft skills, and technical expertise. A large amount of this will rest on the shoulders of an organization’s technical leadership team, but it’s also worth pointing out that those in other departments still have a part to play. If stakeholders are unable to present a clear vision of what their needs and goals are it’s highly likely that the advantages of cloud will pass them by when it comes to business intelligence. Cloud and business intelligence: recommended viewing Going beyond Dashboards with IBM Cognos Analytics [Video] Business intelligence ethics Ethics has become a huge issue for organizations over the last couple of years. With the Cambridge Analytica scandal placing the spotlight on how companies use customer data, and GDPR forcing organizations to take a new approach to (European) user data, it’s undoubtedly the case that ethical considerations have added a new dimension to business intelligence. But what does this actually mean in practice? Ethics manifests itself in numerous ways in business intelligence. Perhaps the most obvious is data collection - do you have the right to use someone’s data in a certain way? Sometimes the law will make it clear. But other times it will require individuals to exercise judgment and be sensitive to the issues that could arise. But there are other ways in which individuals and organizations need to think about ethics. Being data-driven is great, especially if you can approach insight in a way that is actionable and proactive. But at the same time it’s vital that business intelligence isn’t just seen as a replacement for human intelligence. Indeed, this is true not just in an ethical sense, but also in terms of sound strategic thinking. Business intelligence without human insight and judgment is really just the opposite of intelligence. Conclusion: business intelligence needs organizational alignment and buy-in There are many issues that have been slowly emerging in the business intelligence world for the last half a decade. This might make things feel confusing, but in actual fact it underlines the very nature of the challenges organizations, leadership teams, and engineers face when it comes to business intelligence. Essentially, doing business intelligence well requires you - and those around you - to tie all these different elements. It's certainly not straightforward, but with focus and a clarity of thought, it's possible to build a really effective BI program that can fulfil organizational needs well into the future.
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Packt Editorial Staff
30 Jul 2018
11 min read
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What leaders at successful agile Enterprises share in common

Packt Editorial Staff
30 Jul 2018
11 min read
Adopting agile ways of working is easier said than done. Firms like Barclays, C.H.Robinson, Ericsson, Microsoft, and Spotify are considered as agile enterprises and are operating entrepreneurially on a large scale. Do you think the leadership of these firms have something in common? Let us take a look at it in this article. The leadership of a firm has a very high bearing on the extent of Enterprise Agility which the company can achieve. Leaders are in a position to influence just about every aspect of a business, including vision, mission, strategy, structure, governance, processes, and more importantly, the culture of the enterprise and the mindset of the employees. This article is an extract from the Enterprise Agility written by Sunil Mundra. In this article we’ll explore the personal traits of leaders that are critical for Enterprise Agility. Personal traits are by definition intrinsic in nature. They enable the personal development of an individual and are also enablers for certain behaviors. We explore the various personal traits in detail. #1 Willingness to expand mental models Essentially, a mental model is an individual's perception of reality and how something works in that reality. A mental model represents one way of approaching a situation and is a form of deeply-held belief. The critical point is that a mental model represents an individual's view, which may not be necessarily true. Leaders must also consciously let go of mental models that are no longer relevant today. This is especially important for those leaders who have spent a significant part of their career leading enterprises based on mechanistic modelling, as these models will create impediments for Agility in "living" businesses. For example, using monetary rewards as a primary motivator may work for physical work, which is repetitive in nature. However, it does not work as a primary motivator for knowledge workers, for whom intrinsic motivators, namely, autonomy, mastery, and purpose, are generally more important than money. Examining the values and assumptions underlying a mental model can help in ascertaining the relevance of that model. #2 Self-awareness Self-awareness helps leaders to become cognizant of their strengths and weaknesses. This will enable the leaders to consciously focus on utilizing their strengths and leveraging the strengths of their peers and teams, in areas where they are not strong. Leaders should validate the view of strengths and weaknesses by seeking feedback regularly from people that they work with. According to a survey of senior executives, by Cornell's School of Industrial and Labor Relations: "Leadership searches give short shrift to 'self-awareness,' which should actually be a top criterion. Interestingly, a high self-awareness score was the strongest predictor of overall success. This is not altogether surprising as executives who are aware of their weaknesses are often better able to hire subordinates who perform well in categories in which the leader lacks acumen. These leaders are also more able to entertain the idea that someone on their team may have an idea that is even better than their own." Self-awareness, a mostly underrated trait, is a huge enabler for enhancing other personal traits. #3 Creativity Since emergence is a primary property of complexity, leaders will often be challenged to deal with unprecedented circumstances emerging from within the enterprise and also in the external environment. This implies that what may have worked in the past is less likely to work in the new circumstances, and new approaches will be needed to deal with them. Hence, the ability to think creatively, that is, "out of the box," for coming up with innovative approaches and solutions is critical. The creativity of an individual will have its limitations, and hence leaders must harness the creativity of a broader group of people in the enterprise. A leader can be a huge enabler to this by ideating jointly with a group of people and also by facilitating discussions by challenging status quo and spurring the teams to suggest improvements. Leaders can also encourage innovation through experimentation. With the fast pace of change in the external environment, and consequently the continuous evolution of businesses, leaders will often find themselves out of their comfort zone. Leaders will therefore have to get comfortable with being uncomfortable. It will be easier for leaders to think more creatively once they accept this new reality. #4 Emotional intelligence Emotional intelligence (EI), also known as emotional quotient (EQ), is defined by Wikipedia as "the capability of individuals to recognize their own emotions and those of others, discern between different feelings and label them appropriately, use emotional information to guide thinking and behavior, and manage and/or adjust emotions to adapt to environments or achieve one's goal/s". [iii] EI is made up of four core skills: Self-awareness Social awareness Self-management Relationship management The importance of EI in people-centric enterprises, especially for leaders, cannot be overstated. While people in a company may be bound by purpose and by being a part of a team, people are inherently different from each other in terms of personality types and emotions. This can have a significant bearing on how people in a business deal with and react to circumstances, especially adverse ones. Having high EI enables leaders to understand people "from the inside." This helps leaders to build better rapport with people, thereby enabling them to bring out the best in employees and support them as needed. #5 Courage An innovative approach to dealing with an unprecedented circumstance will, by definition, carry some risk. The hypothesis about the appropriateness of that approach can only be validated by putting it to the test against reality. Leaders will therefore need to be courageous as they take the calculated risky bets, strike hard, and own the outcome of those bets. According to Guo Xiao, the President and CEO of ThoughtWorks, "There are many threats—and opportunities—facing businesses in this age of digital transformation: industry disruption from nimble startups, economic pressure from massive digital platforms, evolving security threats, and emerging technologies. Today's era, in which all things are possible, demands a distinct style of leadership. It calls for bold individuals who set their company's vision and charge ahead in a time of uncertainty, ambiguity, and boundless opportunity. It demands courage." Taking risks does not mean being reckless. Rather, leaders need to take calculated risks, after giving due consideration to intuition, facts, and opinions. Despite best efforts and intentions, some decisions will inevitably go wrong. Leaders must have the courage and humility to admit that the decision went wrong and own the outcomes of that decision, and not let these failures deter them from taking risks in the future. #6 Passion for learning Learnability is the ability to upskill, reskill, and deskill. In today's highly dynamic era, it is not what one knows, or what skills one has, that matters as much as the ability to quickly adapt to a different skill set. It is about understanding what is needed to optimize success and what skills and abilities are necessary, from a leadership perspective, to make the enterprise as a whole successful. Leaders need to shed inhibitions about being seen as "novices" while they acquire and practice new skills. The fact that leaders are willing to acquire new skills can be hugely impactful in terms of encouraging others in the enterprise to do the same. This is especially important in terms of bringing in and encouraging the culture of learnability across the business. #7 Awareness of cognitive biases Cognitive biases are flaws in thinking that can lead to suboptimal decisions. Leaders need to become aware of these biases so that they can objectively assess whether their decisions are being influenced by any biases. Cognitive biases lead to shortcuts in decision-making. Essentially, these biases are an attempt by the brain to simplify information processing. Leaders today are challenged with an overload of information and also the need to make decisions quickly. These factors can contribute to decisions and judgements being influenced by cognitive biases. Over decades, psychologists have discovered a huge number of biases. However, the following biases are more important from decision-making perspective: Confirmation bias This is the tendency of selectively seeking and holding onto information to reaffirm what you already believe to be true. For example, a leader believes that a recently launched product is doing well, based on the initial positive response. He has developed a bias that this product is successful. However, although the product is succeeding in attracting new customers, it is also losing existing customers. The confirmation bias is making the leader focus only on data pertaining to new customers, so he is ignoring data related to the loss of existing customers. Bandwagon effect bias Bandwagon effect bias, also known as "herd mentality," encourages doing something because others are doing it. The bias creates a feeling of not wanting to be left behind and hence can lead to irrational or badly-thought-through decisions. Enterprises launching the Agile transformation initiative, without understanding the implications of the long and difficult journey ahead, is an example of this bias. "Guru" bias Guru bias leads to blindly relying on an expert's advice. This can be detrimental, as the expert could be wrong in their assessment and therefore the advice could also be wrong. Also, the expert might give advice which is primarily furthering his or her interests over the interests of the enterprise. Projection bias Projection bias leads the person to believe that other people have understood and are aligned with their thinking, while in reality this may not be true. This bias is more prevalent in enterprises where employees are fearful of admitting that they have not understood what their "bosses" have said, asking questions to clarify or expressing disagreement. Stability bias Stability bias, also known as "status quo" bias, leads to a belief that change will lead to unfavorable outcomes, that is, the risk of loss is greater than the possibility of benefit. It makes a person believe that stability and predictability lead to safety. For decades, the mandate for leaders was to strive for stability and hence, many older leaders are susceptible to this bias. Leaders must encourage others in the enterprise to challenge biases, which can uncover "blind spots" arising from them. Once decisions are made, attention should be paid to information coming from feedback. #8 Resilience Resilience is the capacity to quickly recover from difficulties. Given the turbulent business environment, rapidly changing priorities, and the need to take calculated risks, leaders are likely to encounter difficult and challenging situations quite often. Under such circumstances, having resilience will help the leader to "take knocks on the chin" and keep moving forward. Resilience is also about maintaining composure when something fails, analyzing the failure with the team in an objective manner and leaning from that failure. The actions of leaders are watched by the people in the enterprise even more closely in periods of crisis and difficulty, and hence leaders showing resilience go a long way in increasing resilience across the company. #9 Responsiveness Responsiveness, from the perspective of leadership, is the ability to quickly grasp and respond to both challenges and opportunities. Leaders must listen to feedback coming from customers and the marketplace, learn from it, and adapt accordingly. Leaders must be ready to enable the morphing of the enterprise's offerings in order to stay relevant for customers and also to exploit opportunities. This implies that leaders must be willing to adjust the "pivot" of their offerings based on feedback, for example, the journey of Amazon Web Services, which was an internal system but has now grown into a highly successful business. Other prominent examples are Twitter, which was an offshoot of Odeo, a website focused on sound and podcasting, and PayPal's move from transferring money via PalmPilots to becoming a highly robust online payment service. We discovered that leaders are the primary catalysts for any enterprise aspiring to enhance its Agility. Leaders need specific capabilities, which are over and above the standard leadership capabilities, in order to take the business on the path of enhanced Enterprise Agility. These capabilities comprise of personal traits and behaviors that are intrinsic in nature and enable leadership Agility, which is the foundation of Enterprise Agility. Want to know more about how an enterprise can thrive in a dynamic business environment, check out the book Enterprise Agility. Skill Up 2017: What we learned about tech pros and developers 96% of developers believe developing soft skills is important Soft skills every data scientist should teach their child
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Packt Editorial Staff
25 Apr 2018
8 min read
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Setting up Logistic Regression model using TensorFlow

Packt Editorial Staff
25 Apr 2018
8 min read
TensorFlow is another open source library developed by the Google Brain Team to build numerical computation models using data flow graphs. The core of TensorFlow was developed in C++ with the wrapper in Python. The tensorflow package in R gives you access to the TensorFlow API composed of Python modules to execute computation models. TensorFlow supports both CPU- and GPU-based computations. In this article, we will cover the application of TensorFlow in setting up a logistic regression model. The example will use a similar dataset to that used in the H2O model setup. The tensorflow package in R calls the Python tensorflow API for execution, which is essential to install the tensorflow package in both R and Python to make R work. The following are the dependencies for tensorflow: Python 2.7 / 3.x R (>3.2) devtools package in R for installing TensorFlow from GitHub TensorFlow in Python pip Getting ready The code for this section is created on Linux but can be run on any operating system. To start modeling, load the tensorflow package in the environment. R loads the default TensorFlow environment variable and also the NumPy library from Python in the np variable: library("tensorflow") # Load TensorFlow np <- import("numpy") # Load numpy library How to do it... The data is imported using a standard function from R, as shown in the following code. The data is imported using the csv file and transformed into the matrix format followed by selecting the features used to model as defined in xFeatures and yFeatures. The next step in TensorFlow is to set up a graph to run optimization: # Loading input and test data xFeatures = c("Temperature", "Humidity", "Light", "CO2", "HumidityRatio") yFeatures = "Occupancy" occupancy_train <-as.matrix(read.csv("datatraining.txt",stringsAsFactors = T)) occupancy_test <- as.matrix(read.csv("datatest.txt",stringsAsFactors = T)) # subset features for modeling and transform to numeric values occupancy_train<-apply(occupancy_train[, c(xFeatures, yFeatures)], 2, FUN=as.numeric) occupancy_test<-apply(occupancy_test[, c(xFeatures, yFeatures)], 2, FUN=as.numeric) # Data dimensions nFeatures<-length(xFeatures) nRow<-nrow(occupancy_train) Before setting up the graph, let's reset the graph using the following command: # Reset the graph tf$reset_default_graph() Additionally, let's start an interactive session as it will allow us to execute variables without referring to the session-to-session object: # Starting session as interactive session sess<-tf$InteractiveSession() Define the logistic regression model in TensorFlow: # Setting-up Logistic regression graph x <- tf$constant(unlist(occupancy_train[, xFeatures]), shape=c(nRow, nFeatures), dtype=np$float32) # W <- tf$Variable(tf$random_uniform(shape(nFeatures, 1L))) b <- tf$Variable(tf$zeros(shape(1L))) y <- tf$matmul(x, W) + b The input feature x is defined as a constant as it will be an input to the system. The weight W and bias b are defined as variables that will be optimized during the optimization process. The y is set up as a symbolic representation between x, W, and b. The weight W is set up to initialize random uniform distribution and b is assigned the value zero. The next step is to set up the cost function for logistic regression: # Setting-up cost function and optimizer y_ <- tf$constant(unlist(occupancy_train[, yFeatures]), dtype="float32", shape=c(nRow, 1L)) cross_entropy<-tf$reduce_mean(tf$nn$sigmoid_cross_entropy_with_logits(labels=y_, logits=y, name="cross_entropy")) optimizer <- tf$train$GradientDescentOptimizer(0.15)$minimize(cross_entropy) # Start a session init <- tf$global_variables_initializer() sess$run(init) Execute the gradient descent algorithm for the optimization of weights using cross entropy as the loss function: # Running optimization for (step in 1:5000) {   sess$run(optimizer)   if (step %% 20== 0)     cat(step, "-", sess$run(W), sess$run(b), "==>", sess$run(cross_entropy), "n") } How it works... The performance of the model can be evaluated using AUC: # Performance on Train library(pROC) ypred <- sess$run(tf$nn$sigmoid(tf$matmul(x, W) + b)) roc_obj <- roc(occupancy_train[, yFeatures], as.numeric(ypred)) # Performance on test nRowt<-nrow(occupancy_test) xt <- tf$constant(unlist(occupancy_test[, xFeatures]), shape=c(nRowt, nFeatures), dtype=np$float32) ypredt <- sess$run(tf$nn$sigmoid(tf$matmul(xt, W) + b)) roc_objt <- roc(occupancy_test[, yFeatures], as.numeric(ypredt)). AUC can be visualized using the plot.auc function from the pROC package, as shown in the screenshot following this command. The performance for training and testing (hold-out) is very similar. plot.roc(roc_obj, col = "green", lty=2, lwd=2) plot.roc(roc_objt, add=T, col="red", lty=4, lwd=2) Performance of logistic regression using TensorFlow Visualizing TensorFlow graphs TensorFlow graphs can be visualized using TensorBoard. It is a service that utilizes TensorFlow event files to visualize TensorFlow models as graphs. Graph model visualization in TensorBoard is also used to debug TensorFlow models. Getting ready TensorBoard can be started using the following command in the terminal: $ tensorboard --logdir home/log --port 6006 The following are the major parameters for TensorBoard: --logdir : To map to the directory to load TensorFlow events --debug: To increase log verbosity --host: To define the host to listen to its localhost (0.0.1) by default --port: To define the port to which TensorBoard will serve The preceding command will launch the TensorFlow service on localhost at port 6006, as shown in the following screenshot: TensorBoard The tabs on the TensorBoard capture relevant data generated during graph execution. How to do it... The section covers how to visualize TensorFlow models and output in TernsorBoard. To visualize summaries and graphs, data from TensorFlow can be exported using the FileWriter command from the summary module. A default session graph can be added using the following command: # Create Writer Obj for log log_writer = tf$summary$FileWriter('c:/log', sess$graph) The graph for logistic regression developed using the preceding code is shown in the following screenshot: Visualization of the logistic regression graph in TensorBoard Details about symbol descriptions on TensorBoard can be found at https://www.tensorflow.org/get_started/graph_viz. Similarly, other variable summaries can be added to the TensorBoard using correct summaries, as shown in the following code: # Adding histogram summary to weight and bias variable w_hist = tf$histogram_summary("weights", W) b_hist = tf$histogram_summary("biases", b) Create a cross entropy evaluation for test. An example script to generate the cross entropy cost function for test and train is shown in the following command: # Set-up cross entropy for test nRowt<-nrow(occupancy_test) xt <- tf$constant(unlist(occupancy_test[, xFeatures]), shape=c(nRowt, nFeatures), dtype=np$float32) ypredt <- tf$nn$sigmoid(tf$matmul(xt, W) + b) yt_ <- tf$constant(unlist(occupancy_test[, yFeatures]), dtype="float32", shape=c(nRowt, 1L)) cross_entropy_tst<-tf$reduce_mean(tf$nn$sigmoid_cross_entropy_with_logits(labels=yt_, logits=ypredt, name="cross_entropy_tst")) Add summary variables to be collected: # Add summary ops to collect data w_hist = tf$summary$histogram("weights", W) b_hist = tf$summary$histogram("biases", b) crossEntropySummary<-tf$summary$scalar("costFunction", cross_entropy) crossEntropyTstSummary<-tf$summary$scalar("costFunction_test", cross_entropy_tst) Open the writing object, log_writer. It writes the default graph to the location, c:/log: # Create Writer Obj for log log_writer = tf$summary$FileWriter('c:/log', sess$graph) Run the optimization and collect the summaries: for (step in 1:2500) {   sess$run(optimizer)   # Evaluate performance on training and test data after 50 Iteration   if (step %% 50== 0){    ### Performance on Train    ypred <- sess$run(tf$nn$sigmoid(tf$matmul(x, W) + b))    roc_obj <- roc(occupancy_train[, yFeatures], as.numeric(ypred))    ### Performance on Test    ypredt <- sess$run(tf$nn$sigmoid(tf$matmul(xt, W) + b))    roc_objt <- roc(occupancy_test[, yFeatures], as.numeric(ypredt))    cat("train AUC: ", auc(roc_obj), " Test AUC: ", auc(roc_objt), "n")    # Save summary of Bias and weights    log_writer$add_summary(sess$run(b_hist), global_step=step)    log_writer$add_summary(sess$run(w_hist), global_step=step)    log_writer$add_summary(sess$run(crossEntropySummary), global_step=step)    log_writer$add_summary(sess$run(crossEntropyTstSummary), global_step=step) } } Collect all the summaries to a single tensor using themerge_all command from the summary module: summary = tf$summary$merge_all() Write the summaries to the log file using the log_writer object: log_writer = tf$summary$FileWriter('c:/log', sess$graph) summary_str = sess$run(summary) log_writer$add_summary(summary_str, step) log_writer$close() We have learned how to perform logistic regression using TensorFlow also we have covered the application of TensorFlow in setting up a logistic regression model. [box type="shadow" align="" class="" width=""]This article is book excerpt taken from, R Deep Learning Cookbook, co-authored by PKS Prakash & Achyutuni Sri Krishna Rao. This book contains powerful and independent recipes to build deep learning models in different application areas using R libraries.[/box] Read More Getting started with Linear and logistic regression Healthcare Analytics: Logistic Regression to Reduce Patient Readmissions Using Logistic regression to predict market direction in algorithmic trading  
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Amey Varangaonkar
02 Apr 2018
7 min read
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Top 5 free Business Intelligence tools

Amey Varangaonkar
02 Apr 2018
7 min read
There is no shortage of business intelligence tools available to modern businesses today. But they're not always easy on the pocket. Great functionality, stylish UI and ease of use always comes with a price tag. If you can afford it, great - if not, it's time to start thinking about open source and free business intelligence tools.  Free business intelligence tools can power your business Take a look at 5 of the best free or open source business intelligence tools. They're all as effective and powerful as anything you'd pay a premium for. You simply need to know what you're doing with them. BIRT BIRT (Business Intelligence and Reporting Tools) is an open-source project that offers industry-standard reporting and BI capabilities. It's available as both a desktop and web application. As a top-level project within the umbrella of the Eclipse Foundation, it's got a good pedigree that means you can be confident in its potency. BIRT is especially useful for businesses which have a working environment built around Java and Java EE, as its reporting and charting engines can integrate seamlessly with Java. From creating a range of reports to different types of charts and graphs, BIRT can also be used for advanced analytical tasks. You can learn about the impressive reporting capabilities that BIRT offers on its official features page. Pros: The BIRT platform is one of the most popularly used open source business intelligence tools across the world, with more than 12 million downloads and 2.5 million users across more than 150 countries. With a large community of users, getting started with this tool, or getting solutions to problems that you might come across should be easy. Cons: Some programming experience, preferably in Java, is required to make the best use of this tool. The complex functions and features may not be easy to grasp for absolute beginners. Jaspersoft Community Jaspersoft, formerly known as Panscopic, is one of the leading open source suites of tools for a variety of reporting and business intelligence tasks. It was acquired by TIBCO in 2014 in a deal worth approximately $185 million, and has grown in popularity ever since. Jaspersoft began with the promise of “saving the world from the oppression of complex, heavyweight business intelligence”, and the Community edition offers the following set of tools for easier reporting and analytics: JasperReports Server: This tool is used for designing standalone or embeddable reports which can be used across third party applications JasperReports Library: You can design pixel-perfect reports from different kinds of datasets Jaspersoft ETL: This is a popular warehousing tool powered by Talend for extracting useful insights from a variety of data sources Jaspersoft Studio: Eclipse-based report designer for JasperReports and JasperReports Server Visualize.js: A JavaScript-based framework to embed Jaspersoft applications Pros: Jaspersoft, like BIRT, has a large community of developers looking to actively solve any problem they might come across. More often than not, your queries are bound to be answered satisfactorily. Cons: Absolute beginners might struggle with the variety of offerings and their applications. The suite of Jaspersoft tools is more suited for someone with an intermediate programming experience. KNIME KNIME is a free, open-source data analytics and business intelligence company that offers a robust platform for reporting and data integration. Used commonly by data scientists and analysts, KNIME offers features for data mining, machine learning and data visualization in order to build effective end-to-end data pipelines. There are 2 major product offerings from KNIME: KNIME Analytics Platform KNIME Cloud Analytics Platform Considered to be one of the most established players in the Analytics and business intelligence market, KNIME has customers in over 60 countries worldwide. You can often find KNIME featured as a ‘Leader’ in the Gartner Magic Quadrant. It finds applications in a variety of enterprise use-cases, including pharma, CRM, finance, and more. Pros: If you want to leverage the power of predictive analytics and machine learning, KNIME offers you just the perfect environment to build industry-standard, accurate models. You can create a wide variety of visualizations including complex plots and charts, and perform complex ETL tasks with relative ease. Cons: KNIME is not suited for beginners. It's built instead for established professionals such as data scientists and analysts who want to conduct analyses quickly and efficiently. Tableau Public Tableau Public’s promise is simple - “Visualize and share your data in minutes - for free”. Tableau is one of the most popular business intelligence tools out there, rivalling the likes of Qlik, Spotfire, Power BI among others. Along with its enterprise edition which offers premium analytics, reporting and dashboarding features, Tableau also offers a freely available Public version for effective visual analytics. Last year, Tableau released an announcement that the interactive stories and reports published on the Tableau Public platform had received more than 1 billion views worldwide. Leading news organizations around the world, including BBC and CNBC, use Tableau Public for data visualization. Pros: Tableau Public is a very popular tool with a very large community of users. If you find yourself struggling to understand or execute any feature on this platform, there are ample number of solutions available on the community forums and also on forums such as Stack Overflow. The quality of visualizations is industry-standard, and you can publish them anywhere on the web without any hassle. Cons:It’s quite difficult to think of any drawback of using Tableau Public, to be honest. Having limited features as compared to the enterprise edition of Tableau is obviously a shortcoming, though. [box type="info" align="" class="" width=""]Editor’s tip: If you want to get started with Tableau Public and create interesting data stories using it, Creating Data Stories with Tableau Public is one book you do not want to miss out on![/box] Microsoft Power BI Microsoft Power BI is a paid, enterprise-ready offering by Microsoft to empower businesses to find intuitive data insights across a variety of data formats. Microsoft also offers a stripped-down version of Power BI with limited Business Intelligence capabilities called as Power BI Desktop. In this free version, users are offered up to 1 GB of data to work on, and the ability to create different kinds of visualizations on CSV data as well as Excel spreadsheets. The reports and visualizations built using Power BI Desktop can be viewed on mobile devices as well as on browsers, and can be updated on the go. Pros: Free, very easy to use. Power BI Desktop allows you to create intuitive visualizations and reports. For beginners looking to learn the basics of Business Intelligence and data visualization, this is a great tool to use. You can also work with any kind of data and connect it to the Power BI Desktop effortlessly. Cons: You don’t get the full suite of features on Power BI Desktop which make Power BI such an elegant and wonderful Business Intelligence tool. Also, new reports and dashboards cannot be created via the mobile platform. [box type="info" align="" class="" width=""]Editor’s Tip: If you want to get started with Microsoft Power BI, or want handy tips on using Power BI effectively, our Microsoft Power BI Cookbook will prove to be of great use! [/box] There are a few other free and open source tools which are quite effective and find a honorary mention in this article. We were absolutely spoilt for choices, and choosing the top 5 tools list among all these options was a lot of hard work! Some other tools which deserve a honorary mention are - Dataiku Free Edition, Pentaho Community Edition, QlikView Personal Edition, Rapidminer, among others. You may want to check them out as well. What do you think about this list? Are there any other free/open source business intelligence tools which should’ve made it into list?
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