Bad data is bad business, and there’s a cost to it: Inaccurate reporting, inefficient business process, and wasted time. Inevitably frustrating staff, inhibiting effective-decision making, slowing down your organization, and ultimately negatively impacting donors.
On the flip side, there’s quantifiable value in good data. Supporters are the lifeblood of every nonprofit, and information systems represent a very real and tangible asset to organizations that rely on private contributions as they provide the only systematic way to maintain, preserve, and understand relationships with supporters.
And, yet, I find that most organizations have come to tolerate bad data, often only for lack of time and focus to address the issue, other times to a sense of futility. While purging duplicates, clearing up data-entry errors, and sorting through years of stagnant development information isn’t exactly fun, a little attention and focus on the integrity of data pays dividends in the quality of donor servics, success rate of solicitations, and the effectiveness of management reporting.
Here are some thoughts on cleaning up your data. I’ve found with a little effort and potentially collaboration from different areas of the business, it is relatively straightforward to build a business case for resources to clean up the database.
Backup. The first step in your database cleanup – or any other major change to your system – is to back up your system and test the archive file. It goes without saying that backing up your database on a regular basis will spare you data-loss headaches and embarassment over the long run.
Prioritize. Eyeball your most important constituent information: The records for board and committee members, staff, solicitors, major donors, high-priority prospects and other key individuals. If necessary, solicit new or updated information on these individuals to ensure your organization stays on top of important details.
Analyze. Run a search for duplicate records in your database and merge any redundant records. Having surplus records for the same constituent wastes money on postage and materials, clutters reporting, and complicates data-entry processes by spreading information out among multiple records.
Let go. Go ahead and write off long-overdue pledges and delete unused queries, outdated reports and anything else data-entry packrats have kept around “just in case.” Purging this stagnant information — including unwanted clutter from prior conversions and legacy systems — will simplify daily operations and can improve system performance.
Archive old stuff. Donors who haven’t given, gifts from the 60’s, deceased donors… there isn’t really a “right” answer, but think about what your organization can archive. Archive the records for inactive and deceased constituents so they won’t show up in reports, mailings and other queries. Save these records for historical purposes, but segregate them from active records to streamline your data, simplify reporting and provide a more accurate picture of your current development base.
Document. Do you rely on staff to interpret your data and make sense of even simple reports? If so, this is a significant continuity risk: What happens when your oracle wins the lottery or moves on in their career?
Clean house. Walk through the various departments in your organization, and see where rogue systems and spreadsheets have proliferated. Where possible, migrate these shadow sources into your system of record. Where not possible, take steps to ensure these systems are practicing proper data hygiene and not introducing data integrity issues into the organization.
Get Help. Got a few million records? Not sure how many of them are duplicates? Want to “start fresh” with addresses you know are good? Look into a data service, such as an append, National Change of Address (NCOA), de-dupe, or similar service. For large-scale donor files these services often pay for themselves.
Go to the source. If your database has an inordinate amount of duplicate records, outdated information or other errors, try to figure out why. These problems may indicate deeper issues within your organization that need to be addressed. That automated import that no one quite understands? Vendor file with wonky data? Volunteer data entry staff?
Train. Would extra training for the end-users improve the quality of your data? Could a policies and procedures manual help to reduce these problems in the future? Are there steps you can take to reduce turnover among data-entry personnel?
Commit. Build business processes for managing data. Automate where possible, but keep in mind that often decisions must (and should) be made about data and standards (e.g. are these two records the same, or different; should we keep this historic detail, or delete?) . Consider a consultant to design and document processes – both day-to-day operations, as well as regular data maintenance tasks – so they are standardized and can be managed or even improved moving forward. I’ve found that documenting business processes often makes the process of delegating tasks to new or otherwise technically challenged staff a bit easier (Power Users: hint, hint).
Details like this may seem small but will make a big difference in reducing future clutter, simplifying day-to-day activities, improving the accuracy of reporting and increasing your fundraising capabilities. While few things in life are guaranteed, it is safe to say that not addressing data quality issues this years means you’ll be facing the same issues next year, likely on a larger scale.