Turning Information into Insights

Introduction

Business intelligence as it is understood today is said to have evolved from the decision support systems that began in the 1960's and developed through to the mid-1980s. DSS originated in the computer-aided models created to assist with decision making and planning. From DSS, data warehouses>, Executive Information Systems, OLAP and business intelligence began to develop in the late 80s. The diagram below depicts Analytics subject areas.

Figure 1: Analytics Subject Areas


Figure 1: Analytics Subject Areas

Business Intelligence Vs. Business Analytics

Often BI applications use data gathered from a data warehouse or a data mart. However, not all data warehouses are used for business intelligence, nor do all business intelligence applications require a data warehouse. The following sections describe in detail Business Intelligence and Business Analytics and its various sub categories/domains.

Business Intelligence (BI)

Refers to skills, technologies, applications and practices used to help a business acquire a better understanding of its commercial context. Business intelligence helps users to explore all types of information from all angles and to assess the current business situation to gain a deeper understanding of the patterns that exist in the data.

Figure 2: BI Categories


Figure 2: BI Categories

Operational Analytics: Provides LOB managers with actionable insights and recommendations through packaged analysis and reporting solutions, such as supply chain optimization. These solutions provide actionable, cross-functional insight that is drawn from information that is locked in enterprise resource planning (ERP) and other data sources. This powerful business analysis software offers organizations an integrated view of performance across business functions and departments. It provides dashboards and interactive reporting so that business users at all levels can quickly get the insight they require to drive smarter decisions and outcomes that are better aligned with business strategy.

Performance management: Provides various services that you can use to evaluate business performance:

  • Planning, analysis, and forecasting to automate budgeting and to perform driver-based forecasting, “what-if” scenario modeling, and multidimensional profitability analysis
  • Profitability modeling and optimization to accelerate profitability analysis with an organization-wide approach that joins financial, operational, and strategic planning
  • Performance reporting and scoring that helps to align strategy with execution, to communicate goals, and to monitor performance against targets

Risk Management: Makes risk-aware decisions and meets regulatory requirements with improved risk management programs and methods. Risk management capabilities support the following areas:

  • Capital management
  • Credit risk
  • Governance, risk, and compliance
  • IT governance
  • Liquidity risk
  • Market risk
  • Operational risk
  • Policy and compliance management

Business Analytics (BA)

Analytics are defined as the extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based decision-making. In businesses, analytics represents a subset of business intelligence (BI). BA Is how organizations gather and interpret data in order to make better business decisions and to optimize business processes.

Figure 3: BA Categories


Figure 3: BA Categories

Analytic technologies fall into the following categories:

  • Descriptive Analytics provides information about the past state or performance of a business and its environment. It needs data that is already stored (for example, in a database). Therefore, by definition, it is a view of the past, even if the past happened just a second ago. Descriptive analytics provides regular reports for events that already happened and ad hoc reports to help examine facts about what happened, where, how often, and how many. It includes the capability to perform individual queries so that one can investigate a specific problem.
  • Predictive Analytics helps predict (based on data and statistical techniques) reliably what will happen next so that one can make well-informed decisions and thus improve business outcomes. Predictive analytics relies on real-time events and alerts to suggest actions. It uses simulation models to suggest what could happen. For example, one can apply predictive analytics to enable the performance of the following tasks:
    • Forecasting, which is a process of making statements about events whose outcomes have not yet been observed
    • Predictive modeling for what-if situations or scenarios
  • Prescriptive Analytics recommends high-value alternative actions or decisions given a complex set of targets, limits, and choices. Optimization is used to examine how one can achieve the best outcome for a particular situation. It is most useful when there is no practical way to show the breadth of information or its complexity to human experts in a way to enable decision-making. Stochastic optimization recommends how one can mitigate or even avoid uncertain risks. Therefore, prescriptive analytics predicts future outcomes and suggests courses of actions that will held the greatest benefit to the enterprise.

The following diagram explains the difference between BI and BA in terms of common questions and techniques.

Figure 4: BI vs. BA Techniques


Figure 4: BI vs. BA Techniques

Value Quadrant for BI and Analytics

This is an important diagram that overlays the BI areas on a quadrant with Value vs. Criticality on a different axis. The BI maturity tools are the best avenues for building such bubble charts and are a good data resource for business users/executives to assess where they are and where they want to be in the ensuing 3 to 5 years.

Figure 5: Value Quadrant


Figure 5: Value Quadrant

BI Challenges

The following two sections explain the IT and business challenges for BI applications and domains.

IT Challenges

The following diagram depicts the IT Challenges for BI and Analytics applications.

Figure 6: IT Challenge


Figure 6: IT Challenge

Business Challenges

The following diagram depicts the Business Challenges for BI and Analytics domain.

Figure 7: Business Challenges


Figure 7: Business Challenges

BI and Analytics Reference Architecture and Framework

The following sections describe in detail BI reference architecture and BI framework.

BI and Analytics Reference Architecture

Reference Architecture provides architectural guidance relating to best practices, defines hardware, software and the BI environment using implementable components. BI Reference Architecture also helps establish a common language for communicating with customers.

BI Reference Architecture can also be used as a gap analysis tool for identifying those components that a client may be missing. It does not imply the client must have all the components in the architecture, but rather implies the standard list of components they might consider.

Figure 8: Reference Architecture


Figure 8: Reference Architecture

BI and Analytics Framework

Business View is intended to communicate with a business or end-user audience. The diagram shows each layer of the analytical framework from the information consumer to data sourcing. The different components that provide analytics capability are SOA compliant. Each of these is integrated into the architecture through the best practice or the standards based approach which might require an ESB based integration than a P2P based solution.

Figure 9: BI and Analytics Framework


Figure 9: BI and Analytics Framework

Business Intelligence and Analytics Techniques

The value of Business Intelligence Increases as the delivery of information is embedded in the processes and systems of the enterprise.

Business Intelligence Delivery Stages:

  • Stage I – Unstructured Investigative: Provide a robust database of business information to analysts seeking information to support infrequent and non-recurring business questions (modeling, mining, visualization)
  • Stage II – Structured Investigative: Deliver structured sets of information on-demand to end consumers to provide answers to recurring business questions (reporting, monitoring, scorecards)
  • Stage III – Embedded: Intelligently “push” information directly to end-consumers by continuously monitoring ongoing business performance

Technology Vendors – BI and Analytics

Different types of Business Intelligence Software: There are scores of different business intelligence software and tools, some of which are illustrated below.

Figure 10: BI Tools


Figure 10: BI Tools

Different types of Business Analytics software: There are also scores of different business analytics software and tools as shown below.

Figure 11: BA Tools


Figure 11: BA Tools

Benefits of Analytics to the Enterprises

The following diagram depicts the benefits of BI and Analytics to enterprises.

Figure 11: BA Benefits


Figure 12: BA Benefits

Conclusion

There can be no doubt that the business landscape has changed more in the last two years than in the last ten. Businesses that not only survived, but were able to thrive in the unstable economic climate, were those that could quickly identify and act on trends.

Most of that ability resulted from accessing and acting on critical information more quickly than competitors. That's the power of Business Intelligence and why it pays to develop an appropriate strategy focused on your business, (not IT goals)—a strategy that is flexible enough to adapt to changing trends and deliver continuous value to end-users. Identifying the trends in Data Warehousing, BI and Analytics will be an important asset in ensuring the continued success of your business.

Acknowledgements

I would like to thank EY IT Advisory Leadership for giving me the opportunity to work in this area and for providing with the guidance and valuable inputs.

Sameer Paradkar

Sameer Paradkar

Sameer is a Solution Architect with IBM GBS (Global Business Services). He is responsible for the Presales, IT Strategy and Business Case Development for Commerce Solutions. Sameer has worked with fortune 100 organizations to advise their Business and Technical leaders on roadmaps to successful technology adoption strategies. Sameer is regarded as a creative and out-of-the-box-thinker. He has an in-depth understanding of a variety of systems and is able to articulate advantages and disadvantages of each as they relate to a customer's business model. Sameer has extensive experience in the ICT industry and has worked extensively in the U.S., UK, Europe, Asia Pacific and the Middle East regions.
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