The Foundation for Smarter Decisions
Good decisions start with good data. Whether you're running a multi-million-dollar organization or just trying to optimize your home’s energy use, the same principles apply: you need accurate, clean, and well-governed data to make meaningful choices.
Lately, I’ve been thinking about this a lot—not just in my professional work in Data Governance and Stewardship, but also in my personal projects. I wrote about how I ran an in-depth analysis on my home’s heating efficiency, comparing my furnace’s natural gas usage to the potential performance of a heat pump. I didn’t start with expensive systems. I didn’t build a custom database (at least, not initially). But I did apply the same best practices that have driven my success in enterprise data governance. And that made all the difference.
How Data Governance Supercharged My Analysis
When we think about data governance, we tend to imagine large-scale corporate infrastructures—metadata catalogs, retention schedules, master data management frameworks. But at its core, data governance is about enabling better decision-making through well-managed information. Here’s how that played out in my own project:
1. Ensuring Accurate, Clean Data
Messy data leads to bad conclusions. I manually logged my heating usage for over a year, ensuring that every data point was reliable. When I tried to automate collection via Google Takeout, I quickly discovered the exported data was riddled with duplicates and inconsistencies—classic bad data. Rather than relying on flawed automation, I curated my dataset, ensuring that what I analyzed was trustworthy.
2. Understanding Risk and Context
In enterprise data governance, risk assessment is key. Not all data carries the same level of sensitivity, and understanding its context allows us to focus resources appropriately. In my case, I knew that my heating data carried zero regulatory risk, but it did have decision-making value. This is the same logic that businesses need to apply—understanding which datasets need airtight controls and which simply need to be reliable for internal use.
3. Applying a Disposition Strategy
Good governance doesn’t just dictate how we store data; it ensures we don’t keep information beyond its useful life. Even in my small project, I set a disposition schedule: once I had enough historical data to model trends, I didn’t need to keep years’ worth of raw furnace logs. Organizations that fail to establish data lifecycle policies end up drowning in old, irrelevant information that slows down insights and increases risk.
4. Turning Raw Data into Actionable Insight
Collecting data is one thing. Using it to drive meaningful action is another. Once I had reliable heating and cost data, I could confidently compare gas vs. electric heating costs and determine whether switching to a heat pump was financially viable. The same process happens in large organizations—when data is properly managed, executives don’t just get reports; they get answers that inform strategy.
Why This Matters for Organizations
Data governance isn’t just an IT function. It’s not just compliance. It’s a competitive advantage. The organizations that manage their data well make better decisions, move faster, and reduce risk. Whether it’s a business optimizing its customer insights, a nonprofit tracking donor engagement, or a historic foundation preserving key records, data governance is the key to clarity.
I’ve spent nearly two decades ensuring that organizations don’t just have data—they have data they can trust. The same principles that made my home energy analysis successful are the same ones that allow companies and nonprofits to operate smarter.
Data-driven decision-making doesn’t start with AI, machine learning, or complex dashboards. It starts with governance. Get that right, and the insights will follow.