Data warehouses were built for reporting, but Data Lakes are built for advanced analytics. The real-time digital world of business applies advanced automated analytics including statistical, relationship/graph, location/spatial, machine learning, complex event processing and prescriptive real-time actions, and artificial intelligence. With data visualization and traditional, descriptive analytics, the range of analytics is large and need to be properly applied to realize business value effectively.
Realizing business value is the result of effective analytic practices. Achieving the result requires a range of analytic, statistical, mathematical, and technical capabilities and delivering the resulting information value in a form usable by individuals so they can take the appropriate business action.
The analytic practices that are required are:
- Data Delivery – delivering data for the business usage scenario, that is the answer an analytic result is to provide for such uses as what-if analyses, forecasts, and so forth;
- Descriptive Analytics – developing metrics, KPIs, and analytic results for operational insights into the business;
- Performance Management – measuring and monitoring the performance of the business to maximize performance and results;
- Advanced Analytics & Insight – preparing data, identifying variables that most affect an analytic area of concern, developing statistical and analytic models, providing analytic results, and measuring and monitoring the performance of predictive / inferential models and algorithms to maximize their performance and results;
- IT Cost Reduction – managing analytic objects and data they use requires tracking, monitoring, and ongoing management of them, particularly in regards to costs that come from duplication, storage, and physical data management to optimize IT's investment in them.