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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 fi les 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.

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?

In 2012, a global study was conducted jointly by IBM’s Institute of Business Value (IBV) and MIT Sloan Management Review. This study, which included 1700 CEOs globally, reinforced the fact that one of the top objectives within their organizations was sharing and collaboration.

IBM Cognos Insight, the desktop analysis application, provides collaborative features that allow the users to launch development efforts via IBMs Cognos Business Intelligence, Cognos Express, and Performance Management enterprise platforms. Let us consider a fictitious company called PointScore.

Having completed its marketing, sales, and price strategy analysis, PointScore is now ready to demonstrate its research and analysis efforts to its client. Using IBM Cognos Insight, PointScore has three available options. All of these will leverage the Cognos Suite of products that its client has been using and is familiar with. Each of these options can be used to share the information with a larger audience within the organization.

Though technical, this article is written for a non-technical audience as well. IBM Cognos Insight is a product that has its roots embedded in Business Intelligence and its foundation is built upon Performance Management solutions. This article provides the readers with Business Analytics techniques and discusses the technical aspects of the product, describing its features and benefits.

The goal of writing this article was to make you feel confident about the product. This article is meant to expand on your creativity so that you can build better analysis and workspaces using Cognos Insight.

The article focuses on the strengths of the product, which is to share and collaborate the development efforts into an existing IBM Cognos BI, Cognos Express, or TM1 environment. This sharing is possible because of the tight integration among all the products under the IBM Business Analytics umbrella.

Summary

After reading this article, you should be able to tackle Business Analytics implementations It will also help you to leverage the sharing capability to reach an end goal of spreading the value of Business Analytics throughout their organizations.

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