WHAT YOU SHOULD KNOW
KPIs for Making Decisions with Certainty
A regimen for good decision-making often involves parsing immense and seemingly unrelated volumes of raw data. Selecting which data is relevant to which decision, and establishing meaningful standards to measure the impact of those decisions, is crucial to effective information management. Decision certainty, or decision confidence, is based on selecting the right data and analysis methods and evaluating the data against performance standards using Key Performance Indicators (KPIs).
KPIs are readily available for most business processes and are becoming increasingly valuable in both predicting and measuring performance. These KPIs are most effective when viewed through Business Intelligence Visualization tools (i.e. dashboards).
Most enterprise applications now include some kind of dashboard capability. Many dashboards work because they are tied to high-powered database engines that quickly process large volumes of data. However, this often means that incomplete or inaccurate data can enter our decision processes just as quickly. Data quality is more important than ever because we can merge databases and process analyses so swiftly that it's easy to lose our connection with the original data source(s).
Reliable dashboard implementations start with understanding an organization's span of responsibility and it's controls over source data. While this sounds obvious, understanding the limits of source data is critical to success. On the other hand, focusing on proven methodologies minimizes the introduction of errors into decision support systems and adds certainty to the decision.
In general, robust dashboards can be implemented quickly at a relatively modest cost, yet provide significant improvements in the quality of decisions. The key to achieving this result is coupling a concerted effort to validate data quality with a well-defined modeling approach.
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