The most common pressures driving Agile data analytics investments are data stored in silos and poor data quality impacting decision making. Both complaints betray the burden of legacy business systems and analytics. Data in back-end IT systems is very often difficult to access, difficult to integrate, difficult to normalize, and therefore difficult to harness to answer the questions posed by the business. Mired in these inflexible and out-of-date sources of data, the efforts to create front-end tools and reports will have limited success.