Data Lake Maturity Model

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companies that will be the winners. Furthermore, the winners are winning more while the losers are losing big. Your organization has to take that to heart. Large companies and other organizations take to the halls of gov‐ ernment to lobby for regulations that slow down the forces threaten‐ ing those organizations. (Of course, they couch their requests in terms of public safety or level playing fields.) When that fails, they appeal to public nostalgia, like the automobile workers who appealed to their fellow citizens to "Buy American" or attempts to rebrand declining regions as "historic." These subterfuges work for only limited times; no single person or organization can hold back global trends for very long. To propose the use of big data, you need to have a very strong align‐ ment with the business. You might find obvious pain points that the data lake will solve, or you might suggest that analytics can find new revenue streams delivered by existing business teams. You need to articulate the business value of the big data initiative and get buy-in from many sides, upper management as well as key teams. Ideally, a company will review its entire way of doing business when committing to a data lake. This rarely happens, though—or happens after the business has lost so much ground that change might come too late. Most businesses set up test projects after hearing about big data, analytics, and data lakes. If chosen carefully, these will produce the desired results and stimulate wider adoption of the tools and data- driven thinking. Level 2: Store At this level, there's executive-level recognition of the value of data and its return on investment (ROI). A few departments are running some of the big data tools and data stores mentioned earlier in this report, either on-premises or in the cloud. Data at the Store level Although a few teams are using data lake tools and data stores, the use of each dataset still tends to be limited to one team. Oversight and governance at the corporate level are limited, so the data is not as visible and available as it should be. Each team might begin to The Maturity Model | 23

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