How to achieve analytical, data-driven decisions with decision modeling and the Decision Model and Notation (DMN) standard.
The value proposition of analytics is almost always to improve decision-making. Being explicit about the decision-making to be improved is an effective tool for framing analytic requirements.
Organizations are making significant investments in analytic technologies. These investments range from improved business intelligence and reporting infrastructure to interactive dashboards, advanced visualization tools and, increasingly, data mining and predictive analytics. While these technologies are powerful, many projects fail to get as much value from them as they might.
The key problem for analytic projects is that they generate insight without impact. The tools can be used to develop beautiful visualizations, highly interactive environments and powerful predictions.
Yet ad-hoc, intuitive decision making remains the dominant approach for most employees. Despite investment in analytic decision support tools, analytical, data-driven, decisions remain a rarity.
Similarly most enterprise applications fail to apply analytics to improve performance. Most systems lack even the basic capability to make decisions, constantly deferring to a human user. Even when they do, this decision-making is often mindlessly repetitive, ignoring available data and the analytic insight that could be derived from it to improve results.
At the heart of the problem lies the way analytic requirements are developed today. The focus on trying to guess what data might help and the focus on workflow fail to establish the analytic requirements that will make a difference. Organizations need to frame their analytic requirements in a new way, using decision modeling to explicitly and clearly define the decision-making to be improved.