5 Common Data Governance Pain Points for Analysts & Data Scientists

Are you an analyst or data scientist at a large organisation? Raise your hand if you’ve ever come across these head-scratchers: <ul> <li>Finding data felt like going on a Sherlock expedition.</li> <li>Understanding data lineage was impossibly frustrating.</li> <li>Accessing data became a showdown with the red tape monster.</li> </ul> Here’s a common quip I hear from citizen and professional analysts alike: <blockquote> “Those data governance guys sure know how to make life interesting…” </blockquote> It’s time to cut them some slack. Drawing from my experience as an engineer and data scientist at one of Australia’s banking giants for <a href="https://towardsdatascience.com/from-data-warehouses-and-lakes-to-data-mesh-a-guide-to-enterprise-data-architecture-e2d93b2466b1" rel="noopener" target="_blank">half a decade</a>, I’ve had the privilege of straddling both sides of this heated fence: being a hungry consumer of data while simultaneously standing as a gatekeeper for others. In this article, I’ll do a three-part dive into… <ol> <li>Fundamentals: How data flows through organisations. It’s messy!</li> <li>Understanding common pain points encountered by data users.</li> <li>Enlightenment: Understanding that guardrails support innovation.</li> </ol> The third point is really important. Organisations worldwide are <a href="https://generativeai.pub/modern-enterprise-data-strategy-a-guide-for-analysts-data-scientists-engineers-2d4b45a31427" rel="noopener ugc nofollow" target="_blank">scrambling</a> to become data-driven firms. There is a constant tension between fostering innovation yet having appropriate controls in place to keep a company’s customers, staff and reputation safe. As new data use cases arrive — and that’s all the time — data governance structures try to evolve in tandem. And it’s typically a struggle, because unbridled innovation has no natural speed limit. Learn More