This decentralization, without any governance present, still posed the same challenge that led to centralization in the first place; no single source of truth, discrepancies in metrics values from one report or system to then next, the list goes on. For example, you can have one department using MicroStrategy, another using Tableau, and a third using Power BI. Even if they all connect to the same data warehouse, they can end up with different values for a metric, like comparable net sales. They may also end up in different roll-up or aggregation values, due to the applied logic against the dimensions. Furthermore, the expansion of the big data ecosystem and data science means it’s less feasible to do a majority of analytics through a single data warehouse. For example, it’s a challenge for certain use cases to analyze sensor or other IoT data with a traditional data pipeline to a data warehouse. The combination of analytics decentralization, and expansion of the data ecosystem has led to a major challenge that is not easy to solve.