5 Common Data Governance Pain Points for Analysts & Data Scientists
<blockquote>
<p>“Those data governance guys sure know how to make life interesting…”</p>
</blockquote>
<p>It’s time to cut them some slack.</p>
<p>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.</p>
<p>In this article, I’ll do a three-part dive into…</p>
<ol>
<li><em>Fundamentals</em>: How data <strong>flows</strong> through organisations. <em>It’s messy!</em></li>
<li><em>Understanding</em> <strong>common pain points</strong> encountered by data users.</li>
<li><em>Enlightenment</em>: Appreciating how <strong>guardrails support innovation</strong>.</li>
</ol>
<p>The third point is really important.</p>
<p>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.</p>
<p>As new data use cases arrive — and that’s <em>all the time</em> — data governance structures <em>try</em> to evolve in tandem. And it’s typically a struggle, because unbridled innovation has no natural speed limit.</p>
<p><a href="https://towardsdatascience.com/5-common-data-governance-pain-points-for-analysts-data-scientists-8efe8a007ac2"><strong>Click Here</strong></a></p>