At this annual Gartner Business Intelligence (BI) and Analytics Summit, three themes resonated:\n\nSelf-service analytics is white hot and growing while demand for traditional dashboard BI is in remission.\nBI on Big Data (i.e., Hadoop-based and outside of the data warehouse) is a dynamic new class of problem that requires a new class of solution.\nToday's buyers are increasingly coming from the business side of the house and not from corporate IT, which is moving the center of gravity from the hub to the spokes.\n\nSo what do these three trends mean for corporate IT? For achieving a single version of the truth? For enterprise data lakes and for cost control?\nIn the self-service paradigm, \u201cpower users\u201d trump portal users.\u00a0Tools are analytic-centric rather than reporting-centric. Business discovery supersedes information delivery. Semantic layer-free data exploration and rapid prototyping are where the action is.\u00a0According to Gartner, revenue growth for BI tools is slight and IT budgets are flat.\u00a0Looking closer, however, traditional BI tools are approaching no-growth while growth for data visualization and business discovery tools is in the double digits. This bimodal scenario reflects the exploding number of data-savvy knowledge workers and the desire to eliminate the IT bottleneck.\nSelf-service analytic tools allow power users to quickly explore, blend and visualize data from disparate sources to produce new business insights and to validate business data requirements to support application development and data management. Consequently, business-owned and operated data islands are forming that include data from enterprise data warehouses and big external sources such as web logs, industry hubs, social media, sensors, et al.\nAt Gartner's annual conference, "Big Data Discovery" was trumpeted as something new; however, I wonder if we\u2019re talking about something new or whether we\u2019re talking about analytics on Hadoop as opposed to a relational database management system (RDBMS). It seems to me that, while there are certainly uses cases dealing with billions of observations, the \u201cBig Data\u201d moniker more often points to the storage system rather than the data volume, velocity, and variety. In any case, there\u2019s a hot sub-market of tools for this category, and clearly they\u2019re focused on the business buyer as much or even more so than they are the IT buyer.\nThis brings me back to the question, what does this mean for corporate IT? For a single version of the truth? For enterprise data lakes? For cost control?\nAll things equal, self-service analytics on proliferated data islands leads to multiple versions of the truth and spend duplication.\u00a0Hmm, haven't we spent the last two decades working in the other direction with enterprise data warehouses? Is the pendulum swinging in the direction of\u00a0analytics empowerment and reduced time-to-answer and away from cost control and data quality management?\nThis is the traditional struggle between centralization and decentralization, which is brought to our analytics niche by radical cost reduction of open source software and commodity-priced compute infrastructure.\u00a0How can IT help its customers have their cake and eat it too?\u00a0\u00a0\nGartner thought leader and featured speaker, Frank Buytendijk, suggested that we look to the business model that cracked the code on optimizing the centralization versus decentralization trade-off; namely, franchising.\u00a0In that model, the role of corporate is to drive universal standardization to bring down costs, accelerate time-to-market, and improve quality.\u00a0Franchise owners run their own businesses based on standardized processes and corporate supply chain economies of scale.\u00a0Think McDonald's. In our domain, the McDonald's mentality implies standardization of tools and enterprise licensing to drive down costs, tool-specific skilling to create larger pools of skilled workers to be shared across projects and centralized provisioning of compute infrastructure to save time and money. At a more nuanced layer, standardization re-usable algorithms, data quality management methods, shared data lineage repositories, and data-as-a-service data provisioning.\nBut let\u2019s not forget corporate-owned and operated enterprise data lakes to support business users\u2019 self-service BI. Hadoop\u2019s schema-less write capabilities enable quick, cheap and very large scale data lakes that empower business\u2019 self-service-inclined data geeks.\u00a0The cost of these data lakes are low and the planning overhead is light. If Corporate IT doesn\u2019t get there first, business units will \u2014 over and over again. So what are we waiting for?\u00a0Let's go!