Friday Afternoon Data Strategy: A Quick Wins Playbook
If you read our last post (From Chaos to Clarity: Why 2025 Should Be Your Nonprofit's Year of Data), you might be thinking that the road to better data is a bit too overwhelming. The good news is, if you’ve got a free Friday afternoon, you can bring some order to your data and reporting processes. Read on for some quick wins, and remember that the journey to better data starts with a single spreadsheet. 😉
Quick Win #1 - Create a Data Inventory
Create a list of all the data sources your organization currently has access to, and what they’re used for today. Include where they’re stored (CRM, spreadsheets, databases, paper files, etc.), how they’re collected, who the primary users are, who is responsible for maintaining them, and any known issues/limitations.
Identify obvious duplications, gaps, or unused/outdated data sources and make a plan to address some of the highest priority issues.
This simple exercise gives immediate clarity on your data landscape and often reveals easy opportunities to consolidate or clean up data. It's also an important first step for any larger data initiative.
Quick Win #2 - Automate One Key Report
Identify one report your team spends time creating on a regular basis. Ideally, select a report that is both frequent (i.e. weekly/monthly) and relatively stable (i.e. avoid reports whose processes or inputs change significantly each time they are created).
Map the current process:
How is the input data uploaded and updated?
What data cleanup or manipulation is needed (sorting, filtering, copy/pasting)?
What calculations or summaries are performed?
How are graphs and other visual outputs created?
Identify repeated steps that could be standardized with formulas or other tools, and create an automated (or at least, more automated) solution using tools you already have (like Google Sheets, CRM reporting/analytics, or BI tools).
Make sure to run your new and old process in parallel the next time you create the report to be sure the results are as expected!
Document the new process so others can use or maintain it. Make sure to highlight any manual steps that might remain so they aren’t missed.
This reduces errors, saves time, and makes life easier. Plus, it serves as a jumping off point for future report automation.
Quick Win #3 - Standardize your Key Metrics
Create a list of the most important metrics your organization regularly calculates, reviews, or distributes. Starting with current reports and dashboards will make this process much faster. Keep an eye out for the same calculation appearing with different names, or similar-sounding metrics that might actually be calculated differently.
Create a spreadsheet with a list of these metrics, including the “official” name, description, specific data sources used, and any formulas used to calculate. Capture any important caveats or exclusions (e.g., "volunteer hours exclude board meeting time" or "program participants are counted once per calendar year").
Share the information with the broader team, and store in an easy-to-find location for reference.
Review existing reporting to make sure all metrics are calculated correctly based on your definitions, and flag any that need to be updated (this might be an activity for next Friday!).
Clear metric definitions create stronger alignment, reduce time spent reconciling data, and ensure your team makes decisions based on the right information.
Quick Win #4 - Create a Simple Data Dictionary
Starting with your most used dataset (e.g., your main donor database or program tracking data), create an overview of the dataset itself, including what it’s used for, how data is populated, and what each row represents - a person (e.g. a donor, volunteer, or beneficiary), an action (e.g. a donation, a volunteer shift, receipt of services), or something else.
Create a list of columns or “fields” found in the dataset, and capture:
A clear description of what it represents, in plain English
Where it comes from / how it’s collected (e.g. online donation submissions, manual entry from post-course surveys)
The formula used to calculate, if applicable (e.g. lifetime donations = sum of all donation amounts for a single individual).
What the data should look like, considering:
Data types (e.g. date, currency, number, or text)
Data formats (e.g. dates as MM/DD/YYYY)
Data values (e.g. a specific set of categories, a number in a specific range, etc.)
Which fields are required (should always contain a value) vs. optional (might sometimes be empty)
Definitions for any codes or abbreviations used
Any important caveats or exclusions
Share the information with the broader team, and store in an easy-to-find location for reference.
A data dictionary eliminates the need to repeatedly answer the same data questions, and helps to reveal inconsistencies that can be quickly addressed to improve data quality. It's also a valuable resource for new staff.
