Data Governance Dilemma — The Case for Not Hiring Tool Specialists!

Warning! The following scenario is based on true events.

Your company invested a hefty sum in a top-tier data governance tool. Your team spent months and months (and a few more months) on implementation. Things were going okay, when you got word that your lead driver of the project, the go-to expert, is quitting. $@#%!!

Now you’re in a bind, scrambling to fill the shoes. So, you post a job listing for a "Blank Developer" or "Blank Specialist” to keep the project going and push organizational adoption and training for the tool.

Here’s why that’s the wrong move:

Hiring a specialist for a specific tool might seem like the fastest way to get back on track, but it’s a shortsighted solution. What happens when you decide to switch tools? All the time and money spent on training and certifications becomes a sunk cost.

Plus, if a tool requires an expert just to function, it’s a clear sign that it’s not intuitive or user-friendly. Modern SaaS tools should be straightforward enough for your team to pick up quickly, without extensive training or a steep learning curve.

Who should you hire? 

Focus on data governance, not the tool. Data governance experts go beyond knowing the ins and outs of a single tool. They understand the principles, policies, and best practices that can be applied universally. A true data governance expert can adapt to any tool, making your organization more flexible and resilient to change. 

Let’s review: Clear signs you’re using the wrong tool

• Requires certified experts: If you need an expert with a specific certification just to operate the tool, it’s too complicated.

• Manual onboarding of data assets: Your governance team shouldn’t have to manually onboard data assets. Connections to data systems should be made once, with automatic monitoring and cataloging according to preset policies.

• Manual lineage building: Building data lineage manually is a clear sign of inefficiency. Modern data systems should provide the necessary information to compute lineage automatically and accurately.

• Dependent on experts for queries: If your catalog requires an expert to answer questions, it’s not self-serve enough. For true data awareness and enablement, your governance tool has to allow users to find and understand data independently, regardless of their technical expertise. 

• User training/certification required: Your team doesn’t have time for a steep learning curve. It should be easy to use with minimal training. 

We’ve covered what not to do, now let’s focus on the fix. 

How to achieve organizational adoption (without losing your mind)

• Choose intuitive tools: Select tools that the whole company (not just the data team) can start using with minimal fuss. If it requires extensive training, it’s probably not the right fit.

• Train on governance, not tools: Spend your training time and budget on data governance fundamentals, not tool-specific certifications. Equip your team with knowledge that’s applicable across various platforms.

• Short, effective enablement sessions: Keep training sessions concise and focused. Demonstrate how the tool supports overall data governance, rather than diving into every tiny feature.

• Integrate with native applications: Choose tools that integrate seamlessly with applications your team already uses, like Slack and Teams. Empower them to ask questions about data, submit requests, and view relevant metadata, including lineage and incidents, all within their existing workflows.

• Listen and adapt: Create feedback loops to gather user input. If something isn’t working, be ready to pivot and improve. Make sure to choose a tool that evolves based on real user experiences.

The future is Expertise, not Tools

Data is only going to become more complex. The solution isn’t to get bogged down with specialists for every tool on the market. Instead, focus on cultivating a deep understanding of data governance. Choose tools that facilitate, not hinder, this understanding. Create an environment where your team can thrive, learn, and adapt.

In short, stop wasting time and money on vendor-specific roles. Invest in real expertise. Choose tools that make sense. And for the love of data, let’s make things easier, not harder.

About the Author:

Pardhu Gunnam is a technology entrepreneur best known as the Co-founder and CEO of Metaphor Data, a company that provides a modern metadata platform designed to help organizations manage and utilize their data more effectively. His background includes significant experience at major tech companies, including LinkedIn, where he was part of the team that developed DataHub, an open-source metadata platform widely used in the industry.

In 2020, Gunnam co-founded Metaphor Data, driven by the vision of enhancing data discovery and governance by integrating technical, business, and behavioral metadata. This platform helps companies better understand their data assets, streamline data governance, and ultimately make more informed decisions. Gunnam’s work in this space has positioned him as a key figure in the data management industry, particularly in addressing the challenges of data decentralization in modern enterprises.