About this book

Based on in-memory technology with write-back capabilities for What-if scenario planning, Cognos Insight gives spreadsheets the rich visual appeal for better analysis and planning. Leverage the collaborative features to seamlessly publish the personal analysis workspaces to an enterprise-wide Cognos Business Intelligence solution.

"IBM Cognos Insight" is a fast paced, practical hands-on guide with step-by-step instructions to build Cognos Insight workspaces. Take advantage of the in-memory TM1 cubes as the underlying engine to answer business questions by analyzing data in the form of reports & dashboards. Share these insights with other Cognos services or mobile devices to empower users with real-time data.

This book introduces Cognos Insight as a personal yet powerful analytics application. It covers how decision making is applied in all domains of the business world and how data can be analyzed effectively in the form of fast in-memory cubes. Leverage the write-back functionality to build budgets, plans and forecasts.

"IBM Cognos Insight" will empower new and existing users to maximize the features of the application and analyze data by building visually rich reports and dashboards in minutes.

Publication date:
November 2012


Chapter 1. The New-age Analytics and IBM Cognos Insight

Business decision making relies on data analysis. Over the time, the analytical proficiency has improved with technological advancements in software and the supporting hardware. To satisfy the growing demands of quality data analysis, organizations such as IBM are constantly investing in better hardware and software applications that not only meet the business demands but also tackle the current needs such as exponential data growth, predicting outcomes from historical data trends, managing risks real time, and applying smarter analytical capabilities over structured and unstructured data.

IBM Cognos Insight is one of IBM's newest innovations surrounding smarter analytics. A software product installed on desktops yet runs in memory, and builds fast analytical cubes to answer the four broad questions—what, when, why, and how. IBM Cognos Insight answers these questions by working with numerous types of data to provide deeper insights to help make better business decisions. Coupled with genius algorithms and rich visual, descriptive and textual outputs, this new product has the capabilities to start at a desktop level and launch into an enterprise-wide Business Analytics solution.

We use analytics in every sphere of life, be it financial services, consumer behavioral management, healthcare management, human capital management, risk management, sales and marketing, or technology, to name a few. Business Analytics is a term that sums up the various analytical domains. It serves to improve the business performance and adds to the total business value by leveraging on raw data, and trusting the processes and software applications to provide smarter analytical capabilities.

Business Analytics is gaining more recognition in the business world than ever before. It provides management teams with a competitive advantage, business performance visibility, and high Returns On Invested Capital (ROIC). In a joint MIT Sloan Management Review and IBM's Institute for Business Value (IBV), the top-performing organizations use analytics five times more than the lower performers.

The following chart tracks the differences between the tendencies of the top-performing organizations versus the lower performing organizations in terms of usage of business analytics:

The graph shows that the lower performing organizations tend to use intuition when it comes to customer service, risk management, product research and development, and so on, within their organization, which might suggest improper (or no) use of business analytics at an enterprise level. Thus they have a higher probability of not making the cut of a top performing organization.

To understand Business Analytics further, it is important to understand its building blocks and take a deeper dive into its core components. The four main components that fall under the Business Analytics umbrella are shown in the following diagram:

The following are the explanations for these four building blocks:

  • Business Intelligence (BI): This was first described by computer scientist Hans Peter Lunn of IBM in 1958 as an "intelligence system that will utilize data-processing machines for auto-abstracting and auto-encoding of documents…for creating interest profiles for each of the "action points" in an organization". The definition has changed over the years with advancements in technology which IBM today defines as: "BI helps predict, track, analyze, and present information as it relates to business performance".

  • Performance Management: Performance Management asks scenario-based questions to organizations and their departments with the intention of improving their overall health. These what-if simulations are an input to gain insight and to add value to the business. Typically consisting of planning, budgeting, and forecasting solutions, performance management tends to improve the financial processes, and increases the profitability for all the organizations that invest in its processes and methodologies.

  • Predictive Analytics: Predictive Analytics, as the name suggests, predicts the future outcomes by trending the historical values mixed with intelligent calculations and algorithms from mining, on structured and unstructured data. Statistical analysis towards various data models enable the prediction of outcomes ranging from text mining on social media to correlation analysis that link entities, for understanding the growing volumes of data better.

  • Risk Management: Risk Management applications provide risk analysis and manage change in the regulations along with compliance procedures and governance regulations to reduce financial losses.


Analytics with IBM Cognos Insight

IBM Cognos Insight is the a new product on IBMs Business Analytics block installed on desktops and capable of supporting the five pillars of business intelligence and analytics—reporting, advanced analytics, visual dashboards, score carding, and predictive and risk analytics. The following diagrams hows the features of IBM Cognos Insight:

Features of IBM Cognos Insight

The simplicity of the application lies in its drag-and-drop functionality and built-in smart metadata wizard that enables importing spreadsheets and other types of databases. This desktop application is built upon the robust foundations of IBM TM1 (formerly Applix ), which stores the data in the memory and in multi-dimensional formats. Much like the structure of a rubix cube, data analysis is performed by analyzing multiple dimensions of the cube structure. In addition, the cube compresses and rolls up the data points into levels to form a hierarchical design that can be analyzed by drilling up and drilling down, or slicing and dicing across the multiple-dimensions for a complete analytical exploration.

From a performance stand point, IBM Cognos Insight is an in-memory application that makes for faster development and enhances analysis efforts. Coupled with its visual appeal and data exploration capabilities, it makes for a tremendously powerful reporting and dashboard application. With IBM TM1 as the underlying technology and with its write-back functionality, part of the advanced analytics provides the what-if scenario approach used for an alternate scenario analysis.

It also helps in spreading the data across plans, budgets, and forecasts, which the organizations are looking for to give greater financial visibility and improved business performance. Even when not connected to a server system, IBM Cognos Insight provides write-back, report authoring, dashboard designing, and scorecard developing capabilities in its offline mode, as the data resides within the application and syncs to larger deployments on demand.

IBM Cognos Insight has the ability to share authored reports and dashboards that can be distributed to larger workgroups, collaborating on the same data sets and towards similar goals. The collaborative effort is a huge plus for this self-service desktop application. As per an IBM Institute for Business Value (IBV) study in which 1700 CEO's were interviewed globally, 75 percent of the CEOs demand collaboration as a priority among its workforce. Another benefit of sharing a stand-alone data asset with larger workgroups is that is provides standardization across departments, and governance with workflow processes that have an overall effect towards improvement in performance.

All of the features mentioned here minimize the IT involvement and hand-holding for a technology application used in the business world, thereby making organizations more self-sufficient and agile.


An example case for IBM Cognos Insight

Consider an example of a situation where an organization from the retail industry heavily depends on spreadsheets as its source of data collection, analysis, and decision making. These spreadsheets contain data that is used to analyze customers' buying patterns across the various products sold by multiple channels in order to boost the sales across the company. The analysis hopes to reveal customers' buying patterns demographically, streamline sales channels, improve supply chain management, give an insight into forecast spending, and redirect budgets to advertising, marketing, and human capital management, as required.

As this analysis is going to involve multiple departments and resources working with spreadsheets, one of the challenges will be to have everyone speak in similar terms and numbers. Collaboration across departments is important for a successful analysis. Typically in such situations, multiple spreadsheets are created across resource pools and segregated either by time, product, or region (due to the technical limitations of spreadsheets) and often the analysis requires the consolidation of these spreadsheets to be able to make the educated decision. After the number-crunching, a consolidated spreadsheet showing high level summaries is sent out to executives, while the details remain on other tabs within the same spreadsheet or on altogether separate spreadsheet files. This manual procedure has a high probability of errors.

The similar data analysis process in IBM Cognos Insight would result in faster decision making by keeping the details and the summaries in a highly compressed Online Analytical Processing (OLAP) in-memory cube. Using the intuitive drag-and-drop functionality or the smart-metadata import wizard, the spreadsheet data now appears instantaneously (due to the in-memory analysis) in a graphical and pivot table format. Similar categorical data values, such as customer, time, product, sales channel and retail location are stored as dimension structures. All the numerical values bearing factual data such as revenue, product cost, and so on, defined as measures are stored in the OLAP cube along with the dimensions. Two or more of these dimensions and measures together form a cube view that can be sliced and diced and viewed at a summarized or a detailed level. Within each dimension, elements such as customer name, store location, revenue amount generated, and so on, are created. These can be used in calculations and trend analysis. These dimensions can be pulled out on the analysis canvas as explorer points that can be used for data filtering and sorting. Calculations, business rules and differentiator metrics can be added to the cube view to enhance the analysis.

After enhancements to the IBM Cognos Insight workspace have been saved, these workspaces or files can be e-mailed and distributed as offline analyses. Also, the users have the option to publish the workspace into the IBM Cognos Business Intelligence web portal, Cognos Connection or IBM Cognos Express, both of which are targeted to larger audiences, where this information can be shared with broader workgroups. Security layers can be included to protect sensitive data, if required. The publish-and-distribute option within IBM Cognos Insight is used for advanced analytics features and write-back functionality in larger deployments. where, the users can modify plans online or offline, and sync up to the enterprise environment on an as-and-when basis. As an example, the analyst can create what-if scenarios for business purposes to simulate the introduction of a new promotion price for a set of smart phones during high foot traffic times to drive up sales. Or simulating an extension of store hours during summer months to analyze the effects on overall store revenue can be created.

The following diagram shows the step-by-step process of dropping a spreadsheet into IBM Cognos Insight and viewing the dashboard and the scorecard style reports instantaneously, which can then be shared on the IBM Cognos BI web-portal or published to an IBM TM1 environment.

The preceding screenshot demonstrates the steps from raw data in spreadsheets being imported into IBM Cognos Insight to reveal a dashboard style report instantaneously. Additional calculations to this workspace creates scorecard type graphical variances, thus giving an overall picture through rich graphics.


The building blocks of Business Analytics

The four building blocks or components of Business Analytics comprise of Business Intelligence, Performance Management, Predictive Analytics, and Risk Management. Built to perform and deliver as a Business Analytics software, IBM Cognos Insight can answer the business queries from each of these components.

Business Intelligence

Decision making that is based on large volumes of data can be a daunting task and using data intelligently, and converting it into an asset to reveal answers that help decision making easier is defined as Business Intelligence. Transactional and operational reporting, scorecards, and analysis together form the basis of Business Intelligence.

IBM Cognos Insight provides the basis of business intelligence through its features. Leveraging on the cube design fundamentals of dimensions and measure for analysis, creating business reports and scorecards displaying variances are the core features and benefits that enable Business Intelligence capabilities. The drag-and-drop functionality or the smart-metadata import wizard provides value in reducing time consuming processes such as requirements gathering and depending on IT to build reports. Users of IBM Cognos Insight can benefit from publishing workspaces as starting points for reports, which can be further customized and formatted in IBM Cognos BI or IBM Cognos Express environments, thereby creating Cognos Report Studio reports for each published workspace. This saves tremendous amounts of time in building simple to complex BI reports.

Performance Management

Executives rely on data to answer questions, so they can make the difficult decisions and/or strategic moves that will define the fate of their organization. Questions, such as the following, can be answered intelligently using Performance Management methodologies, which take an organization's performance to the next level and ahead of the competitors:

  • How can our revenue grow faster?

  • How and why are our competitors out-performing us?

  • Where can we trim the expenses for a lean structure?

  • When should we see a profit on a new product?

  • How can we structure bonus payouts to maximize employee retention?

Performance Management can be applied to any part of the Business world as it improves the decision making capabilities and gives insight into the day-to-day operations, strategic moves and future investments, streamlines processes, automates procedures and so on. The write-back functionality enables simulating business scenarios that demonstrate alternative business cases that eventually lead towards the right decision making for any organization.

From a performance management perspective geared towards the financial health of an enterprise, Financial Performance Management (FPM) plays a key role where CFOs are constantly engaged in maintaining a balance between reducing costs and increasing profitability from the customers and product bands. Other Key Performance Indicators (KPIs) of financial growth are as follows:

  • Gross profit

  • Net profit

  • Interest rates

  • Variable costs

  • GL expenses

  • Labor, material, and production costs

  • Marketing and advertising costs

  • Operating variances

  • Assets and liabilities

  • Net cash flow

Using IBM Cognos Insight, these metrics can be graphically, comparatively, or numerically displayed, and vital financial reports such as profit and loss statements, balance sheets, and income and cash flow statements to name a few, can be authored to give deeper financial insight into the functioning of the organization. The simulation of alternate business cases using write-back technology is often used for creating budgets, plans, and forecasts.

IBM Cognos Insight is used as a Performance Management application and in areas such as Financial Performance Management (FPM) to increase revenue growth, lean operating expenses, shorten financial close processes and improving corporate planning, budgeting, and forecasting processes in many organizations.

Predictive Analytics

Prior to the year 2007, before the boom of social networking, if a product was shipped out we didn't receive a direct feedback from the consumer until after the product was a sell-out or a high percentage of returned items were received.

Five years later, it is a different situation as vast amount of data is collected, analyzed, and business decisions are made faster. These vast amounts of data can be used to measure patterns and be able to predict the outcomes based on past trends. IBM Cognos Insight connects to these predictive databases such as IBM SPSS to quickly analyze the current market scenarios so that businesses can act faster on customer feedback.

IBM Cognos Insight has the capability to connect to predictive data to analyze future outcomes. An example of a Customer Relationship Management case is a call center. Cognos Insight uses the predictive data to read the customers intentions before connecting live with the customer, thereby giving higher risk callers faster attention over other satisfied customers. Not only does this improve customer service but reduces the risk of losing the customer by providing IBM Cognos Insight dashboards, based on the customers' behavioral patterns and past positive preferences.

Risk Management

A Risk Management case where IBM Cognos Insight can provide high value in predictive modeling is when it collaborates with IBM SPSS to predict natural disasters from past patterns before they arise. An alert system can flag disaster management and first-response entities to prepare the insurance companies better, thereby reducing the risk before the disaster arrives. Cognos Insight can act as a desktop application for various field experts who can input live data into their dashboard design workspaces and publish them into a centralized Cognos BI environment. This collaborative effort between experts on the field and other organizations using IBM Cognos Insight and Cognos BI provides fast decision making capabilities. This in turn can minimize the stress on the government and the financial institutions with reduced insurance damage payouts.

In Other cases where Business Analytics using IBM Cognos Insight, IBM Cognos BI and IBM SPSS serves industries is to ensure higher customer satisfaction, better sales pipelines, greater product yields, better order fulfillments, reduced overall risks, and avoiding bottlenecks in complex supply chains.

The power of Analytics today is applied in and outside of the business world. IBM Cognos Insight can leverage spreadsheets, IBM Cognos BI content, and other database systems (relation, statistical, and so on) through ODBC connections, to build faster analytics. Cognos Insight can act as a provider of data that might not exist in the organization's main database systems.

With all the data in the world today, 90 percent of which has been created over the last two years alone, Analytics is going from the business world into the world of sports. Sports Analytics is the term used to define what global sport associations and groups are doing with predictive analytics (using statistics and mathematics) by tapping into years of historical data to spot trends and predict the outcomes of games before they begin and well before they end.

One such example recently has been the 2012 Wimbledon Championship, where IBM's Business Analytics team set up IBM SlamTracker. A web product that displays data in visual dashboard styles to display in real-time, player statistics using key metrics such as number of aces, the percentage of wins on the first serve, the percentage of wins, percentage on the second serve, receiving points won, break points conversions, net approaches, and other such parameters. SlamTracker analyzes 39 million data points available over the last seven years of all the Grand Slams for each player to come up with a player pattern in real time.

Using Analytics in sports is taking place in other sports leagues as well such as National Basketball Association (NBA), National Hockey League (NHL), National Football League (NFL), Soccer World Cups, and the Olympic games.


Using analytics successfully

Over the past few years, there have been huge improvements in the technology and processes of gathering the data. Using Business Analytics and applications such as IBM Cognos Insight we can now analyze and accurately measure anything and everything. This leads to the question: Are we using Analytics successfully?

The following high-level recommendations should be used as a guidance for organizations that are either attempting a Business Analytics implementation for the first time or for those who are already involved with Business Analytics, both working towards a successful implementation:

  1. The first step is to prioritize the targets that will produce intelligent analytics from the available trustworthy data. Choosing this target wisely and thoughtfully has an impact on the success rate of the implementation. Usually, these are high value targets that need problem solving and/or quick wins to justify the need and/or investment towards a Business Analytics solution.

  2. Avoid the areas with a potential for probable budget cuts and/or involving corporate cultural and political battles that are considered to be the major factors leading to an implementation pitfall. Improve your chances by asking the question—where will we achieve maximum business value?

  3. Selecting the appropriate product to deliver the technology is the key for success—a product that is suitable for all the skill levels and that can be supported by the organization's infrastructure. IBM Cognos Insight is one such product where the learning curve is minimal; thanks to its ease of use and vast features. The analysis produced by using IBM Cognos Insight can then be shared by publishing to an enterprise-level solution such as IBM Cognos BI, IBM Cognos Express, or IBM TM1. This product reduces dependencies on IT departments in terms of personnel and IT resources due to the small learning curve, easy setup, intuitive look, feel, and vast features. The sharing and collaborating capabilities eliminate the need for multiple silos of spreadsheets, one of the reasons why organizations want to move towards a more structured and regulated Enterprise Analytics approach.

  4. Lastly, organize a governing body such as a Analytics Competency Center (ACC) or Analytics Center of Excellence (ACE) that has the primary responsibility to do the following:

    • Provide the leadership and build the team

    • Plan and manage the Business Analytics vision and strategy (BA Roadmap)

    • Act as a governing body maintaining standardization at the Enterprise level

    • Develop, test, and deliver Business Analytic solutions

    • Document all the processes and procedures, both functional and technical

    • Train and support end users of Business Analytics

    • Find ways to increase the Return on Investment (ROI)

    • Integrate Business Analytics into newer technologies such as mobile and cloud computing

The goals of a mature, enterprise-wide Analytics solution is when any employee within the organization, be it an analyst to an executive, or a member of the management team, can have their business-related questions answered in real time or near real time. These answers should also be able to predict the unknown and prepare for the unforeseen circumstances better. With the success of a Business Analytics solution and realized ROI, a question that should be asked is—are the solutions robust and flexible enough to expand regionally/globally? Also, can it sustain a merger or acquisition with minimal consolidation efforts?

If the Business Analytics solution provides the confidence in all of the above, the final question should be—can the Business Analytics solution be provided as a service to the organizations' suppliers and customers?



As analytics advances with technological improvements and innovations with tools such as IBM Cognos Insight, decision making capabilities will only get faster and more accurate. IBM Cognos Insight provides the starting point in analytics with offline capabilities for business users, with the option of taking their development efforts to an enterprise level so as to share and collaborate with teams within their organization. Being able to build dashboards in minutes with powerful analytical capabilities makes IBM Cognos Insight an application for all.

The next chapter will show a step-by-step installation procedure of IBM Cognos Insight. The readers will be able to then build their own analytical solutions to answer business-related questions and get deeper insights.

About the Author

  • Sanjeev Datta

    Sanjeev Datta is a seasoned Consultant, passionate text and video blogger, and Business Analytics enthusiast. As Practice Director at PerformanceG2, Inc., he works extensively with executives and decision-makers across finance, manufacturing, retail, and pharmaceuticals as a trusted advisor in corporate performance management, building client relationships and managing Business Analytics implementations. Sanjeev's work as a strong Project Manager, Pre-sales and Post-sales Consultant, trainer, and mentor has led to many successful implementations. While at Merador, LLC, Sanjeev worked as a Consultant/Architect building solutions for global organizations. Previous to that, Sanjeev was a Cognos Developer/Consultant while at Softpath Systems, LLC and lead successful Cognos BI solutions and developed Cognos BI training material for numerous clients. He is certified in numerous IBM products and is an IBM Technical Specialist and IBM Sales Mastery Professional. Sanjeev has a degree in Computer Science from Mumbai University and a degree in Interdisciplinary Studies from The University of Texas. Connect with Sanjeev on LinkedIn or Twitter: @1dsanjeev

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