Guest post by Alf Gracombe
Everyone is talking about big data, and for good reason. No doubt, businesses and philanthropic and nonprofit organizations should be educating themselves on how big data can be harnessed to help better deliver on their missions, be they profit-driven or philanthropic. But before grantmaking organizations get too excited about riding the big data wave, there is plenty more they can and should be doing with the data right at their fingertips — their small data.
What is Small Data?
Before describing what small data is, let’s first look at what big data is, or at least, how it is commonly defined. Big data refers to large sets of data. And by large, we’re talking on the order of terabytes of data. Big data can also mean data that is derived from disparate sets of data from multiple sources. Other characteristics include data that requires sophisticated analytic capacity in order to extract its value or, to state the issue in technical terms, data that does not lend itself to be easily stored or queried from a relational database. And lastly, big data is often unstructured or, in the case of data coming from multiple sources, not consistently structured. Understandably, and by its very definition(s), big data can be a big challenge.
Small data, on the other hand, is typically the opposite of how we define big data. Small data is measured in kilobytes, megabytes and sometimes gigabytes. It is the data you already have within the walls of your organization or is data you can easily obtain. You’ll find it in spreadsheets, relational databases, and the software you use every day. For those of us working in philanthropy, small data is most evident in our grants management systems. Small data is, in essence, right under our very noses. Unlike big data, it is far from the abstract. We know small data very well. We work with it every day.
So while all this talk about big data is important, let’s not forget about the data we already have, as well as how we can better use it to improve our mission-oriented work.
It’s Here! Or It’s Near! Get Used to It!
Big data requires a tremendous up front investment. Tremendous. To do much of anything with big data, you need sophisticated software and usually hardware systems. And while hardware and software costs are rarely an obstacle, the expertise and know-how required to derive real value and use from big data is another thing altogether. The fact is most foundations don’t have this expertise. Although there is plenty that philanthropy can be doing with big data (and there are some success stories, for sure), the reality is that there isn’t a lot of capacity or expertise to actually do this work. And it’s far from clear what types of big data we should be working with, or the questions a typical foundation should even be asking of big data. These are not challenges specific to philanthropy. In fact, the world’s largest corporations are wrestling with how to implement big data solutions and they are investing lots of money, often with very uncertain returns, to do so.
Let’s not forget about big data altogether, but let’s turn our attention first to what we can do with our small data assets. Let’s work better with the data we already have at our disposal. Let’s improve our grantmaking and streamline our processes using our own organization’s data. Let’s operationalize our data and put interactive, visually rich tools in the hands of our colleagues so that their work can be more data-informed. After all, we already have this data. We work with it all the time. Now let’s find ways to work with it better.
So How Do We Do It?
In our work with foundations using our GivingData platform, our goal is to make these small data assets more accessible and useful for staff. We do this through dashboards, data visualization, data aggregation, and working with staff to refine their everyday processes around their in-house data. This is all done with the goal of making the data easier to access, understand, and act upon.
The business world calls this Business Intelligence, or BI. Although our work is in philanthropy, the need for having tools and processes to support our decision-making is the same. But rather than BI tools to help an organization make more money, foundations need BI tools that help them give money away more effectively and better measure the social return.
Instead of Business Intelligence, we can call it Grantmaking Intelligence. And good Grantmaking Intelligence (GI) should do the following:
- Illuminate: GI tools should organize and present data and information in ways that are easy for people to understand. Data should be a true representation of itself rather than an abstraction. Don’t try to mask bad data collection and management with overly complex systems.
- Align: Nothing is more important than applying the right technology and design to the way your organization gets its work done. If the tools don’t support your processes then at least one of those things, and likely both, need to change.
- Track: The data you are tracking and asking your colleagues to work with needs to be the right data. Understand what your key metrics are and collect, manage, and present data on those metrics to the people who manage the performance of those metrics.
- Identify: As your organization refines its use of GI tools, you will start to see patterns and trends in the data for which you never thought to look. You will come to the data with specific questions and hopefully be able to answer them, but you will also start finding answers for questions you never had. And they may lead to entirely new ways of thinking about your work. Your GI tools will serve as a mirror held up to your organization. As with any mirror, what you see in it won’t always please you, but at least you’ll know what you would like to improve.
A foundation’s small data, primarily its grants-related data, is one of the most valuable and easily available data assets of a foundation. And best of all, we don’t have to go anywhere to access it. We just have to find ways to better work with it. Data doesn’t drive the work. It informs the work. The use of data for evidence-based decision-making, be it operational or strategic, leads to better outcomes (be they operational or strategic). To do this, first start with your small data. If you do it right, the benefits will be readily apparent.
For some additional reading on the subject of small data:
- Most Data isn’t “Big,” and Businesses are Wasting Money Pretending it Is
- Forget Big Data, Small Data is the Real Revolution
- Focus on Big Data Misses the Big Picture
About the Author
Alf Gracombe is the founder and president of GivingData, a data analytics platform for grantmaking organizations. He lives in Boston and he can be reached at alf@GivingData.com or on Twitter at @agracombe.