by Angela Guess
Writing for Datanami, Alex Woodie recently said, “If you’re embarking upon a big data project, then you’re likely running into one or more data management challenges. The decisions you make regarding how you enforce data governance and how you control data flows can make or break your project. Here are five data governance mistakes you should avoid.” Woodie’s list begins with the basics: “(1) You Have No Data Governance Strategy. If you said to yourself, ‘Huh, what’s data governance?’ then you’re likely making this mistake. Data governance refers to an overarching strategy that defines how organizations ensure the data they use is clean, accurate, usable, and secure. As your organization embarks upon big data projects, you often solve one or more of these challenges in an ad-hoc manner. That approach may work for a while, but as you get big data successes under your belt and take on more complex projects, the lack of governance can come back to haunt you.”
His list continues, “(2) Relying Too Much on Unicorns. Many shops turn to their data scientists (i.e. unicorns) for all matters relating to big data. Like the poor miller who found he could turn straw into gold, corporate bosses expect their unicorns to magically turn raw data into actionable insight. That approach likely won’t work for long. The truth is, if you’re lucky enough to have landed a unicorn, you’re paying them way too much to ask them to be ‘data janitors,’ let alone be in charge of an entire data governance strategy. Data governance is best led by a collection of data stakeholders from the IT department, line of business, and compliance. The Data Governance Institute also recommends hiring a Data Governance Officer (DGO).”
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