Data Generation and Processing

High-quality data doesn't fall from the sky. It takes intention, commitment, and skills.

It’s hard to get far along your analytic journey without data. Fortunately, nearly every nonprofit has far more data than they realize. Useful tidbits of information can be found not just in your donor database, but also in your webinar signups, student lists, payment transactions, veterinary bills, website visitors, social media responses, satisfaction surveys, adoption paperwork – and on and on. 

So if data could be anywhere, where do you start looking? You don’t want to waste time finding all possible data. So focus instead on the data that will move the needle. Your data strategy will direct you first by raising the most pressing questions you need to answer. Do you need to increase your donations (who doesn’t…)? Then you know you want to explore data sources that will make your outreach more effective, identify new donors, or raise amounts of donations. Are you faced with potential program cuts? Then you need data that will tell you which programs are most effective. This isn’t rocket science, but it’s easy to forget what we’re looking for when faced with a dizzying array of possible things to track, measure, and count. 

Don't have a data strategy yet?

Get your analytic journey on course with a data strategy aligned to your organization's most central goals.

Set your compass

Once you do get your data, you’ll need to asses its health. Are your data sources robust, trustworthy, and there when you need them? Or are they error-prone, messy, blockaded by departmental divisions, delayed, nursed along by too many hours of manual labor…? Data quality isn’t sexy. It’s not easy. But it is critical to everything that comes next. Data quality is about sourcing, processing, and delivering the raw materials that will feed transformative analytics.  

What is Data quality?

Data quality refers to the accuracy, reliability, usability, and value of your data.

Data accuracy is achieved with a very low error rate in your data. In other words, accurate data has few to no mistakes, typos, incorrect values, or missing fields. Inaccurate data may have invalid entries (e.g. a six digit social security code), misplaced elements (like a donor ID in the name field), or simply mis-entered data (e.g. under “Services Provided” someone entered “Yes” instead of the specific service). 

Having accurate data is obviously essential to creating useful insights, because if your data have errors so will the analyses it generates.

Reliable data shows up when you expect it, in the format you expect it, containing what you expect it should. Unreliable data changes between uses, is erratic in its availability, and varies in its contents. For example, if a donor database sometimes has only active donors but other times has the full history of donors, that’s unreliable. Similarly, if you’re supposed to receive an Excel file of all meals delivered every week, but half the time it doesn’t show up, arrives two days late, or comes as a CSV or PDF file, that’s also unreliable.

Unreliable data creates frustrating and unnecessary work, interferes with automation, and causes bottlenecks in analytic processes. 

Usable data comes in an accessible format and lives in a place you can easily log into, read, or download. Unusable data doesn’t fit into your processes or software, resides in places you can’t get to, or is organized in a way that makes it difficult to process. For example, without the right tools you might struggle to make sense out of data stored in JSON, fixed length, or other more technical formats. 

Most data needs to be machine-readable to be useful in enduring analytic processes. It doesn’t mean you can’t use PDFs, images, audio, or other unstructured data, it just means part of your process will have to be converting those resources into useable data.

Accurate, reliable, useable data may STILL not provide you any real value. The data might be too old, not granular or detailed enough, missing the groups or breakouts you need, or straight up doesn’t have the answers you seek.

Valuable data provides information and fuels analytic insights that improve your organization’s outcomes and powers progress towards its goals.

how we can help

Conduct a Data Audit

Data sources are often scattered, undocumented, duplicated, or even forgotten. Our data audits construct a map of all the data resources already at your disposal, helping you and your teams find them when you need them. Through in-depth interviews, we understand your current processes and can suggest critical improvements. Finally we review your existing strategic plan and any metrics already in place, so we can outline how the data sources we uncover can more effectively support your organization’s mission and values.

Train on a Data Platform

Sometimes you just need a better tool for your work. We can help you implement tools like Alteryx, SQL, or Power BI to clean up, manage, and put your data to work for you. And if you’re not sure what platform would be right for you, we’re happy to help you review your needs and select one that will get the job done.

Let Us Handle It

As non-profits, we’re already doing too much with too little. If you don’t have time to read all your emails, let alone learn a new platform, we can set up our own process to collect or prep your data for analysis. We can build and run it in house or within your systems, whichever you prefer.

I want to amplify my impact!

Leave behind the overwhelm and get back to serving your mission. 

I'm Ready!