Business Value From BI
- Track business performance
- Analyze business performance
- Impact business performance
Each step on the path to business value is supported by two kinds of BI services, as shown in the illustration.
- Tracking performance requires understanding what is currently happening (Performance Management) and what has happened in the past (OLAP).
- Analyzing performance requires the ability to get to detail (OLAP), develop insight into cause and effect (Business Analytics).
- Impacting performance requires targeting a business metric (Performance Management) and taking a prescribed course of action (Business Analytics.)
Each of these steps leverages a pair of BI services, and each service shares a common interest in business information.[2] Managing BI services therefore requires a consistent information architecture. This is true even when separate teams manage each area.
Tracking Performance
Understanding performance often starts with summarized data on dashboards and scorecards (Performance Management). The need investigate potential problems requires detailed data and history (OLAP and Reporting.)
As Wayne Eckerson demonstrated in Performance Dashboards,
both these areas provide stronger business value when they are integrated. For example, a dashboard is more useful when someone can click a metric and bring up supporting detail in an OLAP cube. To successfully link Performance Management and OLAP, the two domains must share common definitions for business metrics (facts) and associated reference data (dimensions). Metrics must be calculate in the same way, linked to reference data and different levels of detail, and synchronized (if managed separately).
Analyzing Performance
Analyzing performance is the process of breaking down what has occurred in an attempt to understand it better. Slicing and dicing an OLAP cube is a form of analysis, providing insight through detail. Analytic models provide a deeper level of analysis, providing insight into cause and effect, and extending this to the future through prediction.
OLAP is largely focused on exploring various aggregations of business metrics, while analytics is largely focused on the underlying detail that surrounds them. Our OLAP solutions provide historic detail to Business Analytics in the form of data from the data warehouse.[3]
The exchange flows the opposite direction as well. Business analytics develop insights that suggest other things that should be tracked by OLAP services. For example, a particular set of behaviors may define a high value customer. This assessment is developed using Business Analytics, and applied to the customers in the OLAP data mart. For a fun example from the world of sports, check out the book Moneyball
by Michael Lewis.[4]Improving Performance
All of this is somewhat academic if people in the business do not use all this information to make decisions. Business impact occurs at the intersection of Performance Management (which tells us what is important and how we are doing) and Analytics (which suggests the best course of action.)
Every analytic model targets a business metric or key performance indicator (KPI) from the world of performance management. That same KPI, in turn, can be used to measure return on investment of the analytic model.
For example, a direct sales manager of enterprise software wants to reduce cost of sales. An analytic model is developed that assesses the likelihood of a prospect to buy enterprise software.
The manager begins using the prospect assessment model to prioritize the work of the sales team. Less likely prospects are reassigned to a telesales force. Over the next two quarters, cost of sales starts falling. The same KPI that the analytic model targeted is used to measure its return on investment.
Information as an Asset
It is common to manage each of the pillars of Modern BI as a separate program. The path to business value, however, requires that these programs share a consistent view of business information. BI programs that are not centralized must carefully coordinate around a common information architecture.
Further Reading
- For more on this definition of business intelligence, see Business Intelligence in the Modern Era (9/8/2014)
- The three service areas are explained in The Three Pillars of Modern BI (2/9/2015).
- Sometimes analytic modelers bypass the data warehouse, but there are steps you can take to make this important repository more useful. For tips on how to make your data warehouse more useful to analytic modelers, see Optimizing Warehouse Data for Business Analytics (9/25/13). Note that even with a well designed data warehouse, analytic models often augment this enterprise data with additional data sources.
- The Oakland A's used analytics to re-evaluate the basic metrics used to assess the value of a baseball player. See Business Analytics and Dimensional Data (7/17/13).
** Interested in learning more about modern BI programs? **
Check out my new course, Business Information and Modern BI: Evolving Beyond the Dimensional Data Mart. Offered at TDWI conferences and onsite. See the sidebar for upcoming dates.
