Passionate about creating compelling visual stories through the analysis of big data, Chloe Maraina is a Business Intelligence expert with an aptitude for data science and a vision for the future of data management. Today, she joins us to discuss the persistent challenge of data governance—not in theory, but in practice. We’ll explore how to move from abstract frameworks to actionable strategies, focusing on securing executive buy-in with limited resources, engaging overworked teams without causing burnout, and transitioning initial wins into a resilient, enterprise-wide program.
Many data professionals understand governance theory but struggle with implementation. What are the first practical steps for moving from a theoretical framework to an actionable strategy, especially when facing tight budgets and organizational resistance? Please provide some details on how to get started.
The biggest mistake I see is people trying to boil the ocean. They have these perfect, comprehensive frameworks, but they falter against the reality of scarce resources and competing priorities. The key is to stop chasing theoretical perfection and start solving a real, painful business problem. Find a specific issue that’s costing the company money or hurting its reputation. As one of my colleagues, Renee Colwell, advises, you have to “pick something and zero in on it.” Don’t propose a new, expensive system. Instead, show how you can apply governance principles to existing operations to “move fast and fix things.” It’s about building your strategy on what’s already there and demonstrating value quickly, not trying to build a cathedral from scratch.
Imagine a retailer facing rising customer returns and a failing AI project. How can a data governance team secure executive buy-in with a limited budget? What specific, non-technical outcomes should they promise in the first 90 days to appeal to different C-suite priorities?
This scenario is incredibly common, and the winning approach is always to speak the language of the business, not the language of data. With a tight budget, say around $450,000, you can’t afford a massive overhaul. Instead, you propose a targeted, 90-day “Customer Validation Pilot.” You aren’t just cleaning data; you are directly investigating why customer returns have increased. You promise the CEO and COO a clear analysis of customer feedback that links product information data quality to the return rate. For the CFO, you highlight that this pilot will identify the root causes of audit flags. And for the CTO, whose AI initiative is floundering, you explain that the cleaned, validated product data from this pilot will provide a high-quality foundation for their models. You’re not selling “data governance”; you’re selling fewer returns, cleaner audits, and a functional AI project.
When forming cross-functional data groups, key experts often face burnout from their dual responsibilities. What practical strategies can leaders use to mitigate this risk and maintain momentum without overwhelming their best people? Please share some step-by-step examples.
This is a critical point because your domain experts are your most valuable asset, and you can’t afford to burn them out. The most effective strategy I’ve seen is acknowledging that their expertise is a project resource that must be freed up. Instead of just adding governance work to their already full plate, you build a case to get them dedicated time. This means requesting temporary staff to cover their day-to-day operational duties. It’s a direct and honest approach. You’re essentially telling leadership, “To solve this million-dollar data problem, we need our best people focused. For a fraction of that cost, we can backfill their roles for 90 days, eliminating the stress of juggling two jobs and ensuring they can dedicate their full attention to fixing the core data issues.” It reframes the experts’ time from an assumed asset to a protected investment.
In organizations like hospitals with high-stress, time-poor staff, how can you design an effective data literacy campaign? What communication tactics and training formats, delivered through existing channels like Slack or manager huddles, successfully drive engagement without causing more change fatigue?
In a healthcare setting, you absolutely cannot add to the burden. The campaign has to be embedded into the existing workflow and culture. First, you tailor the message. We’ve had great success using the analogy of routine car maintenance to explain data quality—it’s a simple, universal concept that makes the idea tangible for everyone from clinicians to finance teams. Second, you break the training down into incredibly small pieces. Instead of a day-long workshop, you deliver six 10-minute sessions over several weeks through a channel they already use, like Microsoft Teams. You have to respect their time, so the total commitment shouldn’t exceed an hour of general training. Finally, make it engaging. We’ve launched fun data quality quizzes and challenges where different business units can compete to win a free lunch. It fosters a sense of teamwork and makes a dry topic feel more approachable and less like another mandate.
For a new 90-day data governance initiative, what are the most critical KPIs to demonstrate early value? How do you balance process-oriented metrics, like training participation rates, with tangible business outcomes, such as reduced rework or improved data quality for a specific unit?
In the first 90 days, you need a mix of metrics that show both engagement and impact. For engagement, a key process metric is the participation rate in your training modules; you should be aiming for over 75% to show that people are buying in. But that’s not enough. You must pair it with a tangible business outcome. A great one to track is the number of rework tickets related to data errors for a specific department. Seeing that number decrease is a powerful, concrete indicator that the training is working. In the retail scenario we discussed, the primary outcome KPI would be the findings from the Customer Validation Pilot. Presenting a report that says, “We now understand the top three data issues driving customer returns,” is far more compelling than simply saying, “We cleaned 10,000 records.”
Initial 90-day wins are crucial, but what happens next? How do data governance leaders transition a successful pilot project into a resilient, enterprise-wide program that can adapt over time? What are the key steps for maintaining momentum and demonstrating long-term value?
The 90-day win is your proof of concept, your ticket to the next conversation. The key is not to treat governance as a one-time project but as an adaptive, ongoing capability. As Cindy Hoffman from Xcel Energy put it, it’s something you must “adopt and adapt along the way.” The next step is to take the lessons from your pilot and start applying them to another high-value business problem, piggybacking on existing goals and requirements. You maintain momentum by continuously demonstrating measurable results. For instance, Hoffman’s team at Xcel Energy built their program to support an ERP implementation, and their mature governance capabilities later enabled an AI project that cut a data migration timeline by a phenomenal 90%. That’s how you prove long-term value—by showing that good governance isn’t a cost center, but an accelerator for the entire business.
Do you have any advice for our readers?
Absolutely. Stop waiting for the perfect moment, the perfect budget, or the perfect framework. Data governance knowledge is useless until it’s applied to the chaos of actual business constraints. The most successful programs I’ve seen didn’t start with a massive budget or a new suite of tools. They started by meeting the business where it was, finding a real point of pain, and showing how good data practices could alleviate it. Acknowledge the needs of different stakeholders, tailor your message, and prove your value with small, tangible wins. That’s how data governance moves from a theoretical idea on a PowerPoint slide into a living, breathing practice that drives real results.
