When decision-makers implement a business intelligence solution to help in their analytical tasks, they use the system with different scopes of analysis. They start their day faced with business decisions to solve problems, or opportunities, such as identifying the most profitable customers, determining the products with low margins or choosing the ideal price point for a product offering. Without a business intelligence solution that provides efficient reports and charts for different business processes, this sort of analysis becomes a long and tedious task involving spreadsheet software products. With state-of-the-art software, the task becomes easier. Still, the true "intelligent" component of a BI solution is not in the efficiencies of grouping and displaying data at the summary and detail level; the "intelligence" is a combination of technology and identifying the appropriate scope of analysis and decision-making paths when faced with a business decision.
When a business manager starts her day, she starts looking at a few reports which are identified as "most important" for her. These reports are usually at the aggregate level. The level of this aggregation increases as the individual using the system has greater responsibilities in a more senior role in the organization. She starts looking at these key reports and drills down to a lower level of detail or across to another report. She may identify some key factors, maybe some short-term trends, look at some more reports and run some ad hoc queries. She may still need some other reports, so she might look at some cross-functional data residing in a nearby data mart. When she finds enough data as the basis for her task, she identifies some alternatives and makes a business decision.
Figure 1 outlines a simple decision-making path using standard and ad hoc reports at the summary and the detail level in an enterprise data warehouse. As the user starts with a business problem or opportunity, she goes to different places to build the foundation of her business decision. Steps 1 through 8 outline the steps she takes in this process. These tasks are valid within a given scope of analysis.
Figure 1: Decision-Making Path
The corporate data is well captured in the data marts for this routine. The reports are well defined and the charts certainly work well in explaining trends. But how did she get to the point where she makes a decision? What path did she take? How many different reports did she look at and in which order? Did she run any ad hoc queries? In other words, what business wisdom did she withdraw from the data warehouses and how? This path to decision making depends on the business process but is usually not captured in the state-of-the-art software solutions. The path gets more complex as the complexity of the business decision increases. Could the decision paths be captured in the business intelligence solution?
One factor that contributes to the complexity of using standard and ad hoc reports in decision making is the statistical validity of data captured in the data mart. In cases where financial analysis is performed, the path is less complex because all the transaction data is captured in the data mart, and there’s no doubt about the validity of the transaction data (or there shouldn’t be) at the summary at the detail level. But in cases where the amount of collected data is an important factor, such as in pricing and revenue management and supply chain management, the decision-makers look at reports at lower levels of detail and aggregate when necessary. In other words, the decision will be based on different levels of aggregation for different cases for the same scope of analysis. Such criteria are hard to capture in conventional tools but are a core element of the business intelligence system. The scope of analysis and the paths to decision making are essential elements that should be captured somewhere in the overall business intelligence solution.
The business manager’s tacit information is essential in making the data warehouses more effective and key in obtaining the positive return on investment made in creating business intelligence systems. This is why the involvement of business users is always emphasized at all stages of a business intelligence initiative. By the same token, the limited involvement of business users makes it hard to create the various paths to business decisions so the business intelligence system doesn’t get used much. The reports don’t answer the business problems everyone had in mind and eventually the initiative fails. The level of involvement by decision-makers in the design phase is proportional to the probability of success in business intelligence. If decision-making paths are captured and well documented, the success of the project has a very high probability.
In summary, business intelligence solutions achieve higher levels of intelligence if they can guide the decision-makers from their business problems to business decisions through a path that contains standard, ad hoc, drill-through, drill-across reports and whatever additional capability the system has. In order to build such a solution, the OLAP tools should be pushed to their limits and some additional development will be needed. But once built, the system will not only capture data that’s relevant to source systems and reports to be generated, but will also contain strategic business information that will help decision makers navigate in the system. These paths or scopes of analysis can be shared among the decision-makers in the organization just like reports. This key capability will enable the new users get up to speed on their daily tasks of decision making. Overall, by using an approach such as the one suggested here, organizations can convert the tacit or implicit knowledge to explicit knowledge and practicality, therefore, maximize the return on investment on business intelligence solutions.
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