When considering business justification for technology projects such as building a data warehouse, business users determine their return on investment (ROI) to allocate limited resources for the organization. How quickly will it yield business benefits? How will we know we are getting a good ROI? These are the fundamental business questions posed to secure sponsorship for a data warehouse project. The compelling answer to these questions lies not only in how effectively we deliver reliable, accurate data, but how we transform warehouse data into business intelligence. A combination of Executive Information Systems (EISs), Decision Support Systems (DSSs) and On-line Analytical Processing (OLAP) with the data warehouse will deliver business intelligence and a solid ROI.
Business Intelligence and EIS
Business intelligence is the timely combination of tactical and strategic input that helps an organization achieve its goals. At the highest level, an EIS on top of the data warehouse fills this need. Executives need fast, reliable feedback about how the business is operating compared to plans, to measure success and adjust corporate strategy. Business intelligence begins with a top-down view of the key performance indicators (KPIs) of how the business is performing. The standard queries and reports that create the KPIs may include revenue and profit growth, quality of products and services, and customer satisfaction. The data warehouse provides a quick review of these broad measures and trends along with variances against expected results. This "electronic dashboard" of KPIs allows timely executive attention to be focused on understanding and managing the variances in the plan.
Business Intelligence and DSS
Each KPI from the EIS needs to be supported by detailed views and queries in a Decision Support System (DSS) for the data warehouse. Users need to "drill down" from the KPIs to analyze trends and variances within a KPI. For example, if the KPI for customer satisfaction is low, how did we determine this? The drill-down views may combine customer survey results, industry benchmarks and leading indicators to alert us to the problem. Why are we concerned? Because customer retention is a vital component to every business. It costs more money to attract new customers than it costs to keep existing customers happy (sometimes the cost is as high as 5:1). Every dissatisfied customer that we can turn around means money in the bank. Business intelligence can yield a 5:1 return by focusing resources on retaining dissatisfied customers over spending money to attract new ones.
Business Intelligence and OLAP
OLAP offers the potential for "order of magnitude" business intelligence gains for the organization. In the EIS and DSS layers, we knew the questions and sought to provide fast, reliable answers. Using OLAP, we want to reverse the thinking--play Data Jeopardy. We have the answers (the data), but how do we formulate a representative question? What can we learn from the data that we didn't know to ask? What we learn may not only surprise us, but prove to be true "out-of-the-box" thinking that could revolutionize our products and services, giving us competitive advantage.
For example, a convenience store chain was using data visualization to look for patterns in customer purchases. Since convenience stores only stock a limited number of items, they need to predict exactly which items customers want to purchase quickly (for which they are willing to pay a premium rather than going to a fully stocked supermarket). Selecting and stocking only these items means the difference between profit and bankruptcy. Using data visualization, they charted items purchased and sought to find buying patterns and relationships. What they discovered was interesting--large spikes in purchasing of disposable diapers in the fall on certain nights. What was happening? Monday night football, of course--more beer and chips purchased.
What wasn't expected was that apparently many male purchasers were arriving at half time to get the standard football party supplies, but also arrived with a list of the other essential items to bring home as well--that needed to be replenished at any cost. Apparently time is more important than money during half time--as long as you don't come home to watch the game without the rest of the list. An ample supply of cold beer, clean diapers and domestic tranquillity. Business intelligence, courtesy of your friendly neighborhood data warehouse. Who could ask for more?
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