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This means data solutions are people solutions, too! Data governance can be divided into three components: people, processes, and technology. In this post, we’ll talk about people. They’re the non-technical, ever-present, and most important component of anything you do with data. As usual.

Data Governance is a big topic, right up there with regulatory compliance, data-based decision-making, and being a learning organization. For leadership, it sounds like an initiative, with committees, lots of meetings, and tons of paperwork. If an organization’s leaders don’t have a strong data background, they may find it hard to understand the value of investing time and money to improve data governance.

There’s been a lot of talk lately about Data Governance. As awareness grows, many of our clients are generally feeling like they need to work on their data governance, and some are asking us if we can help. In response, I’ve decided to write a blog series about this mysterious new-to-many and important subject. In this series, I’ll describe what Data Governance is (this post) and why it’s important, and provide you with a framework for understanding and improving Data Governance in your organization, regardless of where you are on the Data Governance spectrum.

Your reports are on the struggle bus. Your data is all over the place. Everyone is complaining that they can’t get the reports they need, or spend too much valuable time getting mediocre reports. You and your staff are spending way too much time battling data systems that should be making your life easier instead of harder. Are you fishing around for days just trying to get the right information? Answering 20 questions a day from people who can’t get the data they need? Spending hours (or days, or weeks) cleaning and merging the same data over and over in various spreadsheets?

Every professional field has its own language. When we started working with fundraising data, I had to figure out what caging was. (In case you aren’t a fundraiser, it doesn’t involve chickens.) I bet you didn’t know that a diplopodologist is someone who studies millipedes. And why would you? That’s not information you need to do your job. (Although we do love learning about other people’s jobs from Allie Ward at Ologies). When it comes to data - talking about data, thinking about data, working with data - no-data folks sometimes get intimidated. Yet, if you want to work with data engineers, data analysts, programmers, or other specialists from the data world, we need to find some shared language to get onto the same page when collaborating on projects.

Data engineers have amazing skills and a unique perspective on getting the most out of your data. But sometimes it can be hard to work with them. They speak their own language, do their own magic, and look at the world differently. They may have completed their degrees at Hogwarts (just kidding, they probably studied at a non-magical university) but they weren’t born and raised on Mars - that’s just an urban myth. (Nor, to put another urban myth to rest, are they secretly robots.) When you have mountains of data to crunch, a data engineer, aka Data Wizard, might be just what you need. At Inciter, we have Data Wizards - data engineers, analysts, and other data specialists - as part of our team.

What’s in a word? Quite a lot, in fact. We may choose our words wisely, spit our words out, or leave our words unspoken. But for others to understand what we mean, we need to share a language. French, American Sign, and Egyptian hieroglyphics are examples of widely-recognized languages. Even street signs and traffic lights are a universal language.

Ahh, the lengthy and traditional evaluation report. Funders require them, then struggle to read them. Organizations invest precious time and human resources to prepare them. The results are often dry tomes illustrated with graphs that capture specific results to specific questions posed a year ago when you applied for a grant. But as a one-shot-static-data-snapshot they can’t fully capture the successes and innovations of your programs over time, nor can you be sure that the data holds true even a month after you drop your pen.

Funders love an evaluation report. They especially love well-written reports supported by rigorous studies and accurate information based on the right indicators. This takes resources. And not always the funder’s. It takes up a huge chunk of most evaluation budgets, as well as significant time from program staff who labor over the drafts of each evaluation report to make sure it portrays their efforts accurately and compassionately. And the typical result?