6 Steps to Implementing Data Science at Any Organization

Technology is all about buzzwords: the Internet of Things (IoT), Rich Media, Big Data, Selfie.

Don’t be intimidated by jargon. Many of the concepts are much easier to grasp than what these made-up words and phrases suggest.


If you browse the Internet for long enough, you’ll probably come across the concept of data science. In simple terms, data science is the process of systematically drawing insights from large volumes of data.

It’s a great time for higher education fundraising to run with the idea of data science. With competition rising for charitable support, higher expectations for ROI, and more accessible data from more reliable sources, the opportunity is right there for the taking. Data science can provide insights that define the most efficient and effective activities, who the best prospects are, and how to attack ever-increasing goals and needs. It can address any issue, provided that you are willing to a) ask the question and b) collect the correct data to address the question.

However, employing all of the facets of data science is costly, particularly for nonprofits. Successfully moving to data science requires an understanding of statistics, predictive analytics, data warehousing, and so much more. If you can afford such talent and software, good for you! Not every operation has those kinds of skill sets, people, or funds available. But keep in mind that data science isn’t just a switch to be flipped; it’s a cultural change.

The good news is that there are simple lessons that ANY organization can take from the facets of data science and implement immediately. These basic steps will have a significant impact on your data and the insights that can be drawn from data. As an added bonus, it’ll help you to move towards a culture of data science.

1. Document Standards for Data and Reporting

Shared databases allow many users to work in the same environment on the same datasets. However, it is commonly assumed that everyone is on the same page.

That’s a dangerous assumption.

To ensure alignment among team members, assemble a group of the main users of data—those who can discuss the uses and meanings of codes and fields in your database. Then create a master document that defines the proper uses of the data in your primary database. Accompany reports and analysis with narratives about the reasons why reports were created and what they were trying to accomplish.

It’s also worthwhile to make many of these reports—particularly commonly needed ones—easy to replicate. It may seem like extra busy work, but leaving those breadcrumbs for future generations will help immensely with the growth and health of your data. (Sometimes it means less work for future analysis and research!)

2. Know Your Program and Industry

It’s important to have an understanding of the overarching goals of your organization and the activities that are most important to achieve those goals. In addition, you should stay up-to-date with the general landscape of the industry, including national trends, risks, and opportunities.

Performing an initial SWOT analysis—one that focuses not just on your department, but on your fundraising operation as a whole—can be helpful here. This practice will help you anticipate which problems and opportunities to measure through reporting.

3. Implement Metrics and Analytics

Metrics should be repeatable, reliable, and timely. It’s essential to be able to assess how well your efforts are contributing to the fundamental objectives of your institution. This is one of the biggest factors in increasing efficiency. It’s also a worthwhile venture to benchmark your progress against previous fiscal years and campaigns to help identify trends.

And of course, don’t forget to share this information with others! If it’s important enough to measure, it’s important enough to inform the people who benefit from it. Plus, you can use analytics to confirm or disprove the assumptions within your operation. Prove it with data!

4. Use Visualization

Another buzzword, right? Visualization is the concept of telling a story from the data, generally through charts, graphs, and pictures. This is one of the most important parts of data science. Your visualizations must clearly communicate your findings to data and non-data people alike. The information should be easy to digest for all audiences.

5. Collect Data

Start with external data that’s easy to incorporate into your existing data structures. Have you done email appends? Wealth screenings? NCOA appends? (The latter should be done at least every 90 days!)

What other pieces of mass data can you apply to your database? Keep in mind, however, that the goal is not to gather the most information; it’s to gather more useful information. Collect data that gives you a better picture of your constituents and helps you raise funds.

6. Be Curious

Be willing to dig further into your information to find underlying issues, correlations, and other results—particularly unexpected ones. Don’t be afraid to ask questions and to use the data to test it.

Collectively, these fundamental pieces can help you see where you’ve been and how data can help you get to where you’re going. Then, you can move on to more advanced pieces, such as modeling, maybe machine learning. (Yeah… good luck with that one.)

Working effectively with data and leveraging technology will be incredibly important for the future of fundraising. It will provide better outcomes for our causes, tell us how much of an impact we’re making, and give us more time to focus on the activities that make the biggest difference. Let the data drive your institution!

Learn more about how Matt is empowering his team at Ashland University with data. 

Matt Gullatta
is the Director of Advancement Services at Ashland University. Since becoming an Advancement Services professional in 2007, Matt has made it a personal mission to make data accessible, reliable, and fun! You can find Matt on Twitter, likely chasing down Big Data and Analytics trends or posting animated gifs.

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