Value of Business Intelligence 1 st Meeting

2. Value of Business Intelligence 1st Meeting Course Name: Business Intelligence Year : 2009

3. References Main Books • (1). Williams, Steve & Williams, Nancy (2007). The Profit Impact of Business Intelligence. Morgan Kaufmann. San Francisco. ISBN 978-0-12-372499-1. • (2). Loshin, David (2003). Business Intelligence: The Savvy Manager’s Guide. Morgan Kaufmann. San Francisco. ISBN 978-1-55860-916-7. Supporting Books • (1). Moss, Larrisa T. & Atre, Shaku (2003). Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications. Addison Wesley. Boston. ISBN 0-201-78420-3. • (2). Vercellis, Carlo (2009). Business Intelligence : Data Mining and Optimization for Decision Making. John Wiley and Sons. United Kingdom. ISBN: 978-0-470-51138-1. • (3). Turban, Efraim, Sharda, Ramesh, Aronson, Jay E., & King, David (2008). Business Intelligence: A Managerial Approach. Person Education. New Jersey. ISBN 978-0-13-234556-9.

4. Source of this Material (1). Williams, Steve & Williams, Nancy (2007). The Profit Impact of Business Intelligence. Chapter 1 (2). Loshin, David (2003). Business Intelligence: The Savvy Manager’s Guide. Chapter 1 & 2

5. Introduction to Business Intelligence

6. Why Business Intelligence? • Increased Profitability Business intelligence can help business clients to evaluate customer lifetime value and short-term profitability expectations and to use this knowledge to distinguish between profitable and non profitable customers. • Decreased Cost Decreasing of the investments required to make sales, BI can be used to help evaluate organizational costs. • Improved Customer Relationship Management (CRM) This is basically a BI application that applies the analysis of aggregated customer information to provide improved customer service responsiveness. • Decreased Risk Applying BI methods to credit data can improve credit risk analysis, whereas analyzing both supplier and consumer activity and reliability can provide insight into how to streamline the supply chain.

7. What is Business Intelligence? The Data warehousing institute defines business intelligence as: “The process, technologies, and tools needed to turn data into information, information into knowledge, and knowledge into plans that drive profitable business action. Business Intelligence encompasses data warehousing, business analytic tools, and content/knowledge management.” In addition, this definition also exposes two critical notions: • A BI practice is more that just a collection of tools. This means that without the processes and the right people, the tools are of little value. • The value of BI is realized in the context of profitable business action. This means that if knowledge that can be used for profitable action is ignored, the practice is of little value.

8. What is Business Intelligence? (cont..) • Turning Data into Information The process of turning data into information can be summarized as the process of determining what data is to be collected and managed and in what context. • Turning Information into Knowledge Accumulate piles of information, which are then analyzed in many different ways until some critical bits of knowledge are created. BI involves the analytical components, such as data warehousing, online analytical processing (OLAP), data quality, data profiling, business rule analysis, and data mining. • Turning Knowledge into Actionable Plans A BI program provides benefits that increase business efficiency, increase sales, provide better customer targeting, reduce customer service costs, identify fraud, and generally increase profits while reducing costs. Because of this, implemented properly, BI is one IT area that can be a profit center instead of the traditional cost center.

9. What is Business Intelligence? (cont..) • BI combines products, technology, and methods to organize key information that management needs to improve profit and performance. • BI as business information and business analyses within the context of key business processes that lead to decisions and actions and that result in improved business performance. • BI means leveraging information assets within key business processes to achieve improved business performance. Figure 1-1

10. Business Intelligence in Action • BI Investment are wasted unless they are connected to specific business goals, analyses, decisions, and actions that result in improved performance. • BI from business perspective is primarily about profit. • BI needs to be highly specific to the industry and to how the company competes in that industry. • The kinds of business information, business analyses, and business decisions that BI must deliver or enable, and the way that BI creates business value must be specifically determined for each company. That’s the only way to get the best possible return on your BI investment.

11. The Information Asset • Significant amount of money has been invested in attempts at building and launching BI frameworks and applications (most on infrastructure). • Very little has been invested in managing and exploiting a valuable corporate asset  a company’s data • There are a number of organization that have started to view their data as a corporate asset and to realize that properly collecting, aggregating, and analyzing their data. • BI as a set of tools and methodologies designed to exploit actionable knowledge discovered from the company’s information asset.

12. Exploiting Information Figure 1-2 Level of Abstraction Size of Data A Pyramid of Abstraction

13. The Origins of BI • Decision Support Systems (DSSs) Since the 1970s and 1980s, business have used business information and structured business analysis to tackle complex business decisions. DSSs range from sophisticated, customized analytical tools running on mainframe computers to spreadsheet-based products running on personal computers. • Executive Information Systems (EISs) These were an early attempt to deliver the business information and business analyses to support management planning and control activities. EIS applications have been replaced and extended by BI applications such as scorecards, dashboards, performance management, and other “analytical applications.” These application combine business information and business analyses to provide custom built and/or packaged BI solutions.

14. Business Intelligence Today • BI is bringing a powerful new tool to businesses. • With an effectively executed BI program, businesses can compete by being better that competition at leveraging information to improve profits and performance. • Business need to rethink how they use information in general and BI particular. • A key recent innovation is the use of business-centric BI methods, these methods are designed to help companies fully leverage the profit potential of BI. • The availability and affordability of business- centric methods for designing and developing BI means that a cohesive BI systems that drives profits and performance.

15. Value of Business Intelligence

16. Using BI to Capture Business Value Figure 1-3 • In economic term, the business value of an investment (an asset) is the net present value of the after-tax cash flows associated with the investment. • BI can be seen as a matter of determining how an organization can use BI to: • Improve management processes • Improve operational processes • Figure 1-3, capturing the business value of BI requires organizations to go well beyond the technical implementation of a BI environment. • Figure 1-4, The implication of this requirement is that BI methodologies must be extended to include these additional preconditions. Figure 1-4

17. How do We Achieve Strategic Alignment? • Understand your organization’s strategic drivers and goals. • Determine the business question you need BI to answer in order to achieve those goals. • Identify tools, methods, and analytical frameworks to inform decisions and measure performance. • Deliver the information your organization needs to take actions that improve performance and support your goals.

18. The Need of Process Engineering Figure 1-5 • Process engineering focuses on providing answer to the questions posed in figure 1-5, and those answer can be captured as the foundation for business rules, standard processes, and standard analytical applications for responding to productivity variances. • This approach can be used for all planned BI applications and will allow organizations to move from ad hoc responses for recurring business conditions to effective repeatable responses that capture the business value of BI.

19. The Need for Change Management • Process engineering provides a map of which processes must change and how they must change in order to create business value with BI applications. • Change management is a generic discipline with principles that are generally understood and have been widely applied for decades to a variety of organizational change processes, including business process changes induced by IT investment in enterprise applications such as ERP. • One of the primary change management challenges for BI applications is that most organizations use information and analytical frameworks within management processes has, until recently, been very limited. • To capture the business value of BI initiatives aimed at management processes, organizations will have to apply scientific management and process control thinking to “white collar” activities, a substantial change.

20. The Information Assets and Data Valuation Data can be viewed as an asset, because data can be used to provide benefits to the company, is controlled by organization, and is the result of previous transactions. The Value of Information and The Value of BI: • Time Value of Data • Information as a Sharable Resource • Increasing Value through Increased Use • Increasing Value through Quality • Increasing Value through Merging • Value versus Volume • Measuring the Value of Information

21. BI Application and Dashboard The different uses of data and the contexts of each use as it pertains to the exploitation of information. It’s can break those into two areas. The first area is operational data use, and the other is strategic use. The strategic uses of information as manifested through BI analytics: • Customer Analytics Customer Relationship Management (CRM) is actually based on a number of customer analytic functions that together help people in a company better understand who their customers are and how to maximize the value of each customer. • Customer profiling: the continuous refinement of individual customer profiles that incorporate demographic, psychographic, and behavioral data about each individual. • Targeted Marketing: knowledge of a set of customer likes and dislikes can augment a marketing campaign to target small clusters that share profiles. • Personalization: web site personalization exploits customer profiles to dynamically collect content designed for an individual, and it is meant to enhance that customer’s experience. • Collaborative Filtering: this kind of recommendation generation creates relatively reliable cross-sell and up-sell opportunities. • Customer Satisfaction: this can improve these representatives’ ability to deal with the customer and expedite problem resolution.

22. BI Application and Dashboard (cont) • Customer Lifetime Value: the lifetime value of a customer is a measure of a customer’s profitability over the lifetime of the relationship. • Customer Loyalty: that company’s best new customers are its current customers. • Human Capital Productivity Analytics One way to attain value internally from BI is to be able to streamline and optimize people within the organization, including: • Call Center Utilization and Optimization: high level to high-value customers, minimal support to low-value customers. • Production Effectiveness: includes evaluating on-time performance, labor cost, production yield, etc. • Business Productivity Analytics Another popular analytic realm involves business productivity metrics and analysis, including: • Defect Analysis: companies struggle to improve quality production, there may be specific factors that affect the number of defective item produced. • Capacity Planning and Optimization: understanding resource utilization for all aspects of a physical plant.

23. BI Application and Dashboard (cont) • Financial Reporting: financial reporting analytics provide the means for high-level executives to take the pulse of the company and drill down on particular areas. • Risk Management: to make better decisions about how and when to allocate resources in a way that minimize risk to the organization. • Just-in-Time: can help in accurately delivering products built to customer order within a predictable amount of time. • Asset Management and Resource Planning: to provide insight into short and long-term resource planning. • Sales Channel Analytics Sales channel analytics a subset of business productivity analytics, yet there is enough value is segmenting this area of application. • Marketing: to fine-tune a marketing program and the ability to determine marketing effectiveness. • Sales Performance and Pipeline: to identify variables that affect the efficiency of the sales cycle. • Supply Chain Analytics Aspects of supply chain analytics involve the following:

24. BI Application and Dashboard (cont) • Supplier and Vendor Management: to track performance and reliability by supplier, evaluating and rating the quality of the product supplied. • Shipping: there are different methods by which a company delivers its products to its customers, each with its own cost schedule. • Inventory Control: maintaining an inventory of commodity products that exhibit volatile pricing and limited useful life creates a market risk if those products cannot be used before their obsolescence. • Distribution Analysis: the optimal distribution model would arrange for the delivery of the exact number of products from each factory to its closets warehouse. • Behavior Analysis This type of analytical processing makes use of historical data to look for behavior patterns that take place before the significant event. This allow for the following kinds of analytics: • Purchasing Trends: indentify purchasing patterns that indicate a growing trend that can be used to adjust a company’s reaction to customer trends. • Web Activity: the content presentation can be crafted to direct the web site visitor into these success patterns. • Fraud and Abuse Detection: fraudulent behavior frequently is manifested in patterns.

25. BI Application and Dashboard (cont) • Customer Attrition: can be done by evaluating patterns of behavior before previous attritions and then using those patterns for ongoing customer behavior analysis. • Social Networking Analysis: it is important to identify relationships between specific entities within a system and to analyze their behavior as a group. The Intelligence Dashboard A key performance indicator (KPI) is some objective measurement of an aspect of a business that is critical to the success of that business. In fact a large number of KPIs can be defined in terms of measuring performance associated with many of the BI analytics functions that we described earlier. Another conceptual value of BI is the ability to capture the business definitions of the key performance indicators, manage those definitions as part of the corporate knowledge base, and then provide a visualization dashboard that reflects those KPI measurements, presented in a form for management review.

26. End of Slide “The social responsibility of business is to increase its profits.” -Milton Friedman, Nobel laureate economist