This video explains how to measure and report on open data progress and value,
- [Instructor] Let's talk about how we might measure the value of open data in a government agency. Before I begin, I'd like to just discuss context and metrics for a moment. I strongly believe that in any organization, measuring what we do and the reporting metrics are important. What is often said, what gets measured gets managed. Metrics provide us with a way to know if we're meeting the objectives for a given activity. It's a way to course correct when our metrics show that things are not going well. They are very often used when determining if we're getting a return on an investment, an ROI.
Importantly, metrics help us to make evidence based decisions regarding a project or an operational item. So, the question that comes to mind is, are there times when metrics may not be as important? To answer this question, ask yourself whether you think your organization used metrics to determine the value of investment on email. The answer is most likely no. Email was purchased because it's a tool that supports organizational function, end of story. Rather than belabor the point, you can likely now think of many things that are purchased or implemented where value is simply assumed, and so measurement doesn't make a lot of sense.
Now, how might this relate to open data? The question we must ask is, how important are metrics in the early months of an open data initiative? As we discussed in an earlier video, open data is not deployed primarily as a revenue generator, and it doesn't have to have specific goals in terms of access and usage. The initial value of open data is somewhat less quantifiable. It is about doing the right thing and building trust and increasing transparency and informing those that choose to be informed. We could work hard to spin out metrics on these items, but does that make sense? Instead, I'm suggesting we don't invest heavily in metrics at the beginning of an open data initiative.
Sure, we should capture metrics such as the number of visits, source of visits, say, by country, popularity of data sets, and other metrics that are more akin to those we collect for a typical website. But let's not sweat creating complex and burdensome metrics in their early months. Later in the initiative, say, when an open data has been aligned with specific performance goals or been used at events or supporting a process, then the value of metrics is more important. Surprising? Perhaps. Sensible? For sure.
Dr. Jonathan Reichental introduces real-world use cases for open data, as well as the steps you need to take to develop and operationalize an open data program. He also explains how data scientists use open data to tell stories and drive data visualizations. Along the way, he provides numerous examples of open data in action: improving government, empowering citizens, creating opportunity, and solving public problems.
- Understanding what open data really is
- Current open data efforts around the globe
- Open data in action
- Designing an open data governance process, including policies
- Monetizing open data
- Storytelling with open data
- Selling the value of open data
- Measuring the value of open data