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Data Lake Maturity Model

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People at the Manage level The company has established the expectation that business strate‐ gists cite data and the results of analytics when making decisions. The company is using analytics not only to streamline current oper‐ ations, but to generate more value, relying on predictive analytics. IT staff and business users communicate well about the direction of big data efforts. Self-service might be starting, but at a rudimentary level. The business is evolving into a data-driven institution where managers do not fear the encroachment of data on their expertise. Processes at the Govern level Data lake architecture and the use of tools are informed by docu‐ mented rules and best practices. Enterprise-wide governance and security policies are in place, probably through a newly created cor‐ porate body responsible for policies regarding data. The staff follows well-documented policies about essential decisions such as how to protect privacy, and life cycle issues such as how long data has value and when it should be archived. There is a repeatable, well- understood way to onboard new divisions and incorporate their data into the lake. Biggest challenges of the Govern level If your data catalog doesn't exist yet or is thinly populated, you need policies to ensure that all data is entered and tagged. Self-service should be expanded. You also must make sure to monitor regulatory compliance and keep track of audit logs. This in turn requires hiring and retaining highly skilled data engineers, who should be instruc‐ ted to automate as many processes as possible and expose data through web forms and dashboards to users. Overall, this level still involves a lot of manual labor in the data life cycle: people are carrying out such tasks as recognizing duplicate datasets, adding them to the catalog, and determining which fields are sensitive. Getting to the next stage requires an investigation into the processes people are routinely going through and then automat‐ ing these. Level 4: Automate This level frees employees from many routine tasks and helps speed up innovation. One key to reaching this stage is autodiscovery: for 28 | The Data Lake Maturity Model

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