5 Common Data Governance Pain Points for Analysts & Data Scientists
Drawing from my experience as an engineer and data scientist at one of Australia’s banking giants for half a decade, 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…
Fundamentals: How data flows through organisations. It’s messy!
Understanding common pain points encountered by data users.
Enlightenment: Appreciating how guardrails support innovation.
The third point is really important.
Organisations worldwide are scrambling 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.
An all-you-can-eat data buffet sounds good until your firm gets lobbed a multimillion dollar penalty from regulators for having customer data leaked onto the dark web.
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