Applying KM lessons learned to business analytics

• Collaboration. DCBI must provide support for collaboration, because decision making most often is a collaborative process.
• Rules. DCBI must provide rule capabilities or be integrated with a rules engine in order to make decisions actionable. The process of translating a policy or rule into the specific form required by each relevant operational system is also required.
• Decision capture. DCBI must be capable of capturing the record of what decision was made and why. The resulting repository of decisions (the decision base or knowledgebase) anchors a learning environment and provides a persistent record to address compliance demands.
• Decision search. More often than not, if an organization tracks decisions, it is likely to be in the form of project reports that document lessons learned. However, text is not generally accessible by traditional business intelligence tools. Hence, DCBI adds the requirement to combine searching through a decision base with analyzing the unstructured content and structured data pertinent to the decision. That process enables a decision maker to find a situation or problem that is analogous to the current one and to discover the reasons for the earlier decision. Here (and in the previous requirement) DCBI intersects with knowledge management.
• Decision tracking and monitoring. After a decision is made, there is a need to monitor events to determine whether the decision was effective. Are the predicted effects of the decision actually being realized? If not, responsible individuals need to be alerted to analyze the results and potentially revisit the decision, initiating another cycle in the decision process. Here, DCBI intersects with business activity monitoring (BAM).

The learning gap

The disconnect between the delivery of information and the processes of decision making comprises what IDC calls the "learning gap." The learning gap operates in two respects, from information monitoring and delivery to decision making, and from decision making to information monitoring.

First, there is a disconnect from information monitoring and delivery to decision making. Traditional business intelligence leaves off at the information delivery stage, paying insufficient attention to forming a problem statement, searching for candidate solutions (hypothesizing) and evaluating the likely outcomes (modeling). The danger is that a decision maker without information relevant to the decision at hand is likely to rely exclusively on intuition--a notoriously unreliable practice.

Second, there is a disconnect from decision making to information monitoring and delivery. If decisions are not captured, there is no opportunity to track information relevant to the measurement of whether they were effective.

For example, a planner in a high-tech company is responsible every month for forecasting demand for each of the next six months. The forecast is then provided as input into a system that yields a schedule for use by a contract manufacturer. When there are last-minute adjustments after the schedule is committed, the contract manufacturer can assess penalties. There was no tracking and analysis of past forecasts for learning and improvement. Then, the high-tech company implemented an analytic application that captured each forecast and analyzed deviations from actuals over time. With that type of feedback, the planner was able to improve accuracy to a significant degree.

If decisions are captured, criteria can be established for prioritizing information in order to track the effectiveness of their quality. This has several advantages:

• Using decisions as filters for information monitoring and delivery helps an organization determine whether to stay the course or trigger a re-examination and restart of the decision-making process.
• The workflow for decision making indicates which individuals should be alerted when expected outcomes do not come to pass. In other words, if you know what decisions are made and who made them, you can be far more precise in tracking relevant information and alerting the right decision makers in time to revisit them.
• A better understanding of the relevant information, in turn, enables adjustments to the type of data being captured and collected through ETL and other data integration processes.
• Linking a BAM product to DCBI would mean deriving the definitions of the monitoring rules from the metadata on the actual decisions that were made and the individuals responsible for them. As new decisions are made, event monitoring/alerting rules would be defined or revised.

The ability to capture, monitor and analyze decisions and their effects requires rich, higher-order metadata constructs for defining decisions and related events. Those constructs must then be mapped to the metadata for the underlying structured and unstructured data that document the decision. That metadata mapping should enable access methods for users, who can search or query by specifying the relevant events and decisions. That convergence of DCBI and BAM would close the learning gap in a truly automated way.

Over the past two decades, business intelligence solutions have made great strides in automating the delivery of information to individuals. Latest graphic user interfaces provide robust interactivity and more advanced data visualization. In addition, the speed with which data is being delivered to users has improved greatly. However, shortcomings in supporting the decision-making needs of decision makers remain. As business intelligence solutions become more operational, they must increasingly incorporate functionality present in both ERP and KM systems that deal with business process automation, collaboration, rules management, and decision capture and retrieval.