Data Governance: It's Not Just About Data, It's About People

The Essence of Data Governance: Taming the Wild Humans

By Lao Shifu (Old Driver), July 24, 2024

Let's face it, data governance can feel like herding cats. As a humble data mule in a large company's data governance team, I often find myself feeling utterly useless. Meetings on data governance are a bizarre affair:

• Before the meeting: Nobody knows what to talk about.
• During the meeting: Everyone gets into heated arguments and nobody listens.
• After the meeting: We achieve nothing concrete and just waste everyone's time.

It's exhausting, demoralizing, and makes you question the very nature of data governance: Is it really about managing "data" or is it about managing "people"?

Navigating a sea of conflicting demands, egos, and resistance to change can feel like being a street-level bureaucrat dealing with endless complaints. Business folks just want the data they need, but getting them to contribute feels like pulling teeth.

"I don't even use this data, why should I input it?" — A classic line that always silences the room. Sometimes we manage to reason with them, other times they storm out in a huff. It all depends on who’s around and how much political capital someone has.

As IT personnel, functional departments, external vendors, or tech consultants, our role feels more like begging for scraps than leading a crucial initiative. It's demoralizing to say the least.

I often dream of being a powerful executive who could simply dictate and enforce data governance policies. But reality bites, and I'm just a data mule stuck in the trenches.

The Real Obstacle:

While technology plays a role, the biggest hurdle to effective data governance is people.

High-level executives often prioritize the digital transformation outcomes and view data governance as an IT burden, something to be dealt with passively. Meanwhile, business departments resist anything that adds to their workload, viewing data governance as irrelevant to their core objectives.

Redefining Responsibilities:

To break this cycle, we need to clearly define roles and responsibilities in the data governance process:

• Who generates the data? They are the data owners.
• Who uses the data? They are responsible for its quality.
• Ultimately, whoever generates the data is responsible for its quality.

Empowering Data Contributors:

To encourage participation and ownership, we should:

• Make it clear who is responsible for inputting data.
• Establish a direct link between data quality and individual performance.
• Provide incentives and recognition for those who contribute high-quality data.

My role as a data governance project manager in this chaotic landscape is to strive for fairness, transparency, and accountability. My mission is to create a system where data flows smoothly, reliably, and ethically from its origin to its final destination.

This blog post was originally written by Cheng Yu Nian (成于念) on the Chinese platform 人人都是产品经理 and translated into English by an AI assistant.