Descriptive Analytics gives insight into the past and current state of your business by using mainly two key methods, data aggregation and data mining (data discovery), to discover/find any patterns, trends and meaning through the comparison of historical data then presented in an understandable way typically takes the form of reports, charts, and dashboards. It often starts as a high level, one-dimensional view of history but an organization can achieve more sophistication by progressing toward business intelligence which allow drill-down, multi-dimensional views of historical results, which allows us to learn from past behaviors, and understand how they might influence future outcomes. |
What we do in descriptive analytics are primarily aggregate transactional/historical data to better track performance, identify issues/problems that need attention and deliver clear and timely information for company’s decision-making. In this stage we typically measure “actual” performance and encapsulates master data about the business, forecasts for known variables and targets that drive performance scorecards. What we try to achieve in this stage is reporting what is happening in real-time and generates standardized and ad-hoc reports, scorecards, alerts and basic “slice and dice.” Under diagnostic analytics we enable business users to understand why something is happening. We group information in multiple ways, and visualize it in charts and graphs, which helps uncover root causes (e.g. deviations from target, outliers) for poor business performance. The vast majority of the statistics we use fall into this category. Usually, the underlying data is a count, or aggregate of a filtered column of data to which basic math is applied. For all practical purposes, there are an infinite number of these statistics. |
