Before beginning any data and analytics program, practitioners have to first assess the capabilities and willingness of their organization to move beyond current management practices and overcome separate, sometimes conflicting, systems, analytics, and organizational silos. Organizations in the early stages of analytics often don't have the ability to undertake the projects required in later stages that may exploit advanced analytics across the enterprise. The following five levels of analytic maturity are suggested. At level one, we call it the basic level. Analytics capabilities are largely siloed or ad hoc, often based on spreadsheet-based analytics, firefighting, data quality, etc. They're almost always unplanned attempts at exploiting data for better business outcomes. But people tend to argue about who's data is correct. Usually, there's a team of business analysts and a report writers who have exclusive use of analytic tools and handle analytic requests that come from the business. At level two maturity which we call opportunistic, individual business units pursue their own analytic initiatives with some structure within themselves. But there's still no common structure across them. Also, culture still tends to get in the way of acting on any analytics and there's really no senior leadership supporting it. At level three or systematic maturity, the organization as a whole begins to move forward in a matrix set of shared, centralized, and organized services. For example, analytics, reporting, governance, innovation, and even data monetization and technology standards such as technology standards for data and data models, analytics, and analytic models. This will include organized self-service analytic efforts as we've discussed. Also, there's some performance alignment using increased structured, repeatable processes. At this point, the organization is making regular use of exogenous or external or alternative data sources like those from data brokers or partners. We start to see business executives championing analytics efforts. At level four, much higher level of maturity, we call this the differentiating level. At this level, the organization is consistently performance oriented with enterprise metrics and an innovation framework providing appropriate alignment. The office of the chief data officer and/or chief analytics officer acts as the spearhead of business innovation and transformation, exploiting data and analytics. Data scientists are hired throughout the business and are driving analytics oriented innovation. Also, there is a decent degree of collaboration in developing and refining analytics models and some reuse of models for related purposes. Level five which is called transformational and this is of course very aspirational for most organizations, it's the ultimate level of maturity in which data and analytics is used widely in embedded in data strategy, tactical, and operational decision-making transparently. This is a central component of effective resource allocation processes. Business transformational efforts automatically relay user exploit data and analytics. Most business processes are data-driven with very few still reliant on gut feeling of managers and executives. Also, at this level of maturity, data is used to make operational tactical and strategic decisions using not just advanced analytics, but AI or artificial intelligence as well. Additionally, the business begins focusing almost exclusively on developing digitalize offerings that rely heavily upon data and analytics.