Here at Heller, we talk a lot about databases—fundraising CRM platforms, engagement systems, donor and program data. We also talk about people, processes, workflows, and workarounds. Last year, I wrote about the bedrock of your fundraising program: data itself. This year, the conversation has only become more urgent, as nonprofits navigate an explosion of AI and automation tools. The fundamentals of good data management haven’t changed, but the stakes (and opportunities) are higher. Robust data practices not only ensure accuracy, security, and regulatory compliance – they also position your organization to leverage AI responsibly for greater impact.
In this guide, we’ll share updated best practices for nonprofit data governance and quality in 2026, grounded in Heller’s recent work with mission-driven organizations and informed by emerging trends in the broader industry. We’ll cover how to foster a culture of data ownership, break down silos (when it makes sense), clean and streamline your data, and prepare your team and technology for an AI-powered future. Along the way, I’ll offer practical steps to get started and real examples (anonymized) of how these practices help nonprofits accelerate their mission.
First, let’s set the stage: Why does data readiness matter so much right now? Simply put, poor data is expensive – it wastes staff time and leads to missed opportunities. One study found that knowledge workers spend ~30% of their time on non-value-added tasks due to bad data. We see this in nonprofits too: teams wrestling with duplicate records, outdated spreadsheets, and siloed lists when they could be focusing on donors or program insights.
Moreover, as AI tools become commonplace (for example, using ChatGPT to draft appeals or machine learning to predict donor churn), the effectiveness of these tools depends entirely on the quality and accessibility of your data. Garbage data in, garbage predictions out – and potentially damaging consequences for trust with your supporters. On the flip side, well-governed data can be transformative. It enables personalization in fundraising, accurate impact reporting, and confident decision-making. It’s also the foundation for ethical AI use: you can’t ensure fairness or transparency in AI if you don’t even know where your data came from or who owns it.
So, how can your nonprofit get there? Let’s dive into key best practices, updated for 2026, to strengthen your data foundation and prepare your organization for the next wave of technology.
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One of the primary challenges we see in nonprofits is lack of ownership over data. In many organizations, no one feels fully responsible for maintaining the donor database or the client records, so data hygiene falls through the cracks. Different teams collect their own data for their own needs, and no one is looking at the whole picture. The result? Inconsistencies, inaccuracies, and a lot of head-scratching when reports don’t line up.
Tech leaders can change this by cultivating a culture where clean, accurate data is everyone’s responsibility. This means assigning business owners to each major system or dataset – real people who are accountable for data quality and utilization in their area. For example, assign your Development Director as the owner of the fundraising CRM, your Volunteer Manager as owner of the volunteer database, etc. The data owner role isn’t about siloing access; it’s about stewardship. Owners ensure the data under their care is up-to-date, oversee proper usage and user training, and serve as point people when issues arise. They don’t necessarily fix every typo themselves, but they take ownership of the outcomes of that system’s data.
In our work, we’ve observed that when “everyone is responsible” in theory, often no one is responsible in practice. It helps to formalize responsibilities. One international relief nonprofit we partnered with found that many staff were freely modifying key records in Excel and internal systems with no oversight, leading to significant inconsistencies. Our recommendation was to establish a formal Data Governance Program led by a cross-departmental committee, with clearly defined data owners and custodians. This not only built a culture of accountability, but also introduced observable controls and change management discipline – for instance, requiring a review before bulk data changes, and documenting how data flows between systems. After implementation, they saw immediate improvements: fewer “miscoded” gifts, less finger-pointing about reports, and faster resolution of data issues because responsibilities were clear.
Form a Data Governance Committee. To support your data owners (and avoid each working in isolation), create a working group or committee that meets regularly to discuss data issues and policies. Include representatives from various departments – fundraising, programs, IT, finance, etc. In a large organization, you might invite department heads or even a leadership sponsor; in a smaller org, it might be a half-dozen power users who care about data. The goal is to align data practices with organizational goals and break down the walls between teams. For example, your committee might set organization-wide definitions (what exactly counts as an “active donor”?), decide on data standards (everyone enters state names using the same 2-letter abbreviations, for instance), and agree on data sharing protocols (who can access which data). They also serve as champions when new tools or processes roll out, making sure colleagues are trained and bought in.
Reality Check: A well-run data governance committee actually saves time by preventing problems. It’s a forum to catch and fix issues early. And it gives data the strategic attention it deserves. Make it practical: share quick wins (e.g. “we fixed 5,000 duplicate contacts this quarter”) and tie discussions back to mission impact (“cleaner data means our email outreach reached 20% more constituents”).
Top leadership support is also key. Data culture flows from the top. If your Executive Director and managers talk about the importance of good data (and allocate time and budget to manage it), staff will follow. One Heller client, a national foundation, kicked off their data initiative with a message from the CEO to all staff: “Data is one of our most valuable assets. Treat it with the same care as the funds we steward.” That kind of tone-setting empowers the governance team to enforce standards.
Key takeaway: Don’t let data management happen ad hoc. Assign data stewards and create a structure for cross-team collaboration. When everyone knows their role in keeping data healthy, you build trust – both internally and with your supporters.
As we often say, “bad data is nobody’s fault, but good data is everybody’s responsibility.”
To manage data effectively, you must zero in on what’s most important. Not all data is created equal! A common trap is to try to track everything and end up drowning in a swamp of fields and files. Instead, define the key fields and metrics that drive your nonprofit’s success, and concentrate your efforts there.
Start by asking: What core information do we need for a 360° view of our constituents? For a fundraising-focused org, that might include donation history, recency/frequency, communication preferences, and engagement indicators (event attendance, volunteer hours). For a service delivery org, it might be client intake data, service delivery dates, outcomes, and follow-up notes. Make a short list of the critical fields in your systems that, if well-maintained, give a reliable picture of your supporters or beneficiaries. These are your “golden” fields that warrant extra attention.
At Heller, when we conduct data assessments, we often help clients create a data dictionary or catalog of key fields – essentially, an inventory of what data you have and why it matters. In one project, we worked with a large humanitarian NGO to enumerate the essential donor data points they relied on and found over 250 fields in their donor database, but only about 40 were used regularly in reporting or segmentation! Those 40 became the focus for cleanup and standardization. We helped them document each field’s purpose and the acceptable values (for example, which codes represent which donor source).
This process uncovered a lot of ambiguity – e.g., multiple fields that all seemed to store “engagement score” from different tools – which we then helped consolidate. By listing out key fields and their definitions, you can spot overlaps, gaps, or useless information that’s just taking up space. As a best practice, maintain this data dictionary and update it whenever you add or change fields so everyone stays on the same page about what data means.
Once you know what’s important, plan to audit it regularly. Schedule periodic data health checks for those key fields. This could be quarterly de-duping of contacts, a monthly report of new records missing critical info, or an annual comprehensive audit. If that sounds like drudgery, make it fun: I love running cleanup events like “Data-athons” where staff come together (with pizza or snacks, of course) to tackle a data quality to-do list in a blitz. Alternatively, use downtime or volunteer power – one nonprofit we know engages skilled volunteers for an annual “data spring cleaning,” giving them clear instructions on how to merge duplicates and fill missing data. The point is to embed cleanup as a routine, not a one-time fire drill when something breaks.
Also, automation can be your friend here. Many modern CRM and data platforms have built-in tools to enforce data quality rules (e.g., preventing invalid values or alerting on duplicates) or can integrate with third-party services for checking addresses, standardizing names, etc. In 2026, we also have AI-powered data cleansing tools emerging – these can intelligently find anomalies or even correct data by referencing external sources. For example, an AI might flag that “Robert Hernandez” and “Bob Hernández” in your donor system appear to be the same person with differing info, prompting a review. While these tools aren’t magic, they can significantly reduce manual workload. Several nonprofits we work with have started using an AI-based de-duplication service that learns from past merges – it has sped up their duplicate resolution by more than half. Consider budgeting for such tools if you have a large volume of data; it often pays off in saved staff hours.
Consistency is king. If every department is using a different format or criteria for data, you’ll struggle to use it effectively. Set organization-wide standards for data entry and train everyone on them. Simple examples: decide on one format for phone numbers and stick to it; use picklists (dropdown options) instead of free-text for categories whenever possible; ensure dates are entered in a consistent way. These standards can be documented in a short “data entry guidelines” reference. One nonprofit we advised even instituted a short online training for all new hires on “Data 101,” covering how to use the CRM and the importance of following conventions. That investment has paid dividends in data quality.
Finally, don’t let perfect be the enemy of good. You will never have 100% pristine data – and that’s okay. Focus on making it trusted and fit for purpose. If you identify, say, 10 key donor attributes and you bring those from 60% complete to 90% complete and accurate, you’ve made huge progress. Leadership and teams start trusting the reports they get because the crucial stuff is right. And with trust comes adoption – staff will be more diligent about entering data once they see it actually being used to drive decisions.
In summary, know your critical data and continuously tend to it. As we noted in 2025, good data governance helps maintain trust with donors and stakeholders; in 2026, it also allows you to leverage new technologies effectively. For instance, if you ever want to implement predictive analytics to forecast donor giving or AI to personalize communications, those systems will lean heavily on those key fields being consistent and accurate. Think of it like feeding an engine: high-octane fuel (clean data) makes it run smoothly, but dirty fuel (bad data) can clog it up. So fuel your mission with the good stuff!
Data silos are the bane of many organizations. Nonprofits often end up with different departments or chapters using separate tools that don’t talk to each other. Your fundraising team logs donor interactions in one CRM, your programs team tracks outcomes in another database, the advocacy folks have a petition platform, and so on. It’s totally normal – each team picks the tool that suits their needs – but it can result in a fragmented view where no one can see the whole relationship with a supporter or beneficiary. Siloed data can also lead to inefficiencies (reporting takes ages because you must export and join spreadsheets from five places) and errors (one system says John Doe is a donor, another says he opted out of emails, another has an old address – which is correct?).
In 2025, we admitted that sometimes siloed data is completely fine, if it truly doesn’t impact cross-functional goals. We stand by that. Not every piece of data needs to be integrated into a monolithic system. The key is to identify which data must be shared across the organization and which can live happily in its own silo. This comes back to your organizational goals. For example, if one of your strategic goals is to improve supporter engagement across all touchpoints, then you likely want fundraising, volunteer, and event participation data in one place (or at least easily accessible to each other). If another goal is to measure program impact against dollars spent, you’ll need to link finance data and program outcomes data. On the other hand, suppose your Programs team gathers very detailed data for a specific grant report that no one else in the org uses – that might be okay to keep in a separate system or spreadsheet, as long as it’s not valuable to others.
We advise clients to develop cross-functional data goals: essentially, list what insights or operations require data from multiple teams. An example goal could be “Understand a constituent’s full journey (donations, event attendance, volunteer hours) to tailor our outreach.” Achieving that requires breaking silos between fundraising, events, and volunteer management. Another goal: “Reduce redundant data entry and manual reconciliation.” That might mean integrating the donation form on your website directly with the CRM, instead of having the web team maintain a separate donation log. Use these goals to prioritize integration efforts.
One membership nonprofit we worked with faced classic silo issues: membership records in their AMS (Association Management System), learning records in an LMS, emails in a marketing tool, and no unified view. Through workshops, it became evident that their cross-team goal was to increase member retention by providing more personalized, timely engagement. To do that, they needed membership status, event attendance, and content interaction data in one place. We helped them build a data integration roadmap focused on those areas – starting with a project to sync their event registration system with the main CRM, and to pipe key web engagement metrics into the CRM as well. Meanwhile, there were other datasets (like internal HR data and certain finance records) that we deliberately left siloed because they weren’t relevant to that goal and would have added complexity and cost for little benefit. This pragmatic approach ensured that effort went into integrations that move the mission needle, not integration for integration’s sake.
When you do need to break down silos, there are a few ways to tackle it:
The benefits of breaking down silos are huge. When data is connected, you get richer insights: you might discover that volunteers have a higher donation rate, or that event attendees are likely to become members. Your teams can collaborate better with a shared understanding of constituents. And you eliminate the frustrating duplication of effort (like five people all compiling slightly different lists of “active contacts” for their own use). A Heller analysis for one nonprofit found that a unified data architecture could drive efficiency gains and deeper constituent relationships – specifically noting that a connected ecosystem “empowers staff with timely, accurate data, deepening constituent relationships and enabling real-time insights and data-driven action at every level.” In plain language: when your data systems talk to each other, your team can talk to supporters in a more informed and impactful way.
That said, integration projects can be complex and require investment. It’s okay to pace yourself. Tackle one silo at a time based on priorities. Maybe this year you integrate your email marketing tool with the CRM so unsubscribes and preferences sync (preventing PR headaches of emailing people who asked out). Next year, target event registrations, and so on. Each integration will yield new value.
And remember: if you decide not to integrate a particular dataset, document the reason and periodically revisit it. Sometimes a data set that wasn’t important before becomes important later. For example, maybe that siloed grant report data wasn’t needed by others – until suddenly your development team attempts a big program-restricted fundraising campaign and realizes that information would actually be gold for donor updates. The landscape of needs can change, especially as you adopt new strategies or tools (like AI). Stay flexible.
Tear down those silos – but do it strategically. Share and unify data where it serves a clear purpose. Where silos remain, keep an eye on them. By addressing fragmentation, you pave the way for organization-wide initiatives (like an AI project that uses data from multiple sources) to succeed. In fact, in an AI-readiness assessment we did for a global research nonprofit, one major finding was that their data was “distributed across various systems and fragmented,” and that they needed to improve data accessibility and quality before implementing AI-driven analysis. In other words, no AI tool was going to magically knit together their silos – that work was a prerequisite. The more you can connect and centralize your data (securely and thoughtfully), the more AI and analytics can work for you down the road.
It’s tempting to keep data forever. Storage is cheap, and you never know when that old list or extra fields might be useful, right? Besides, many of us in the nonprofit sector have a bit of a hoarder instinct when it comes to historical data – “Don’t delete those records! That’s our institutional memory of donors from 20 years ago!” However, there are strong reasons to be deliberate about data retention and purging: compliance, cost, clarity, and yes, even performance.
First, consider privacy regulations. Depending on where you operate (and the jurisdictions your constituents reside in), there may be laws requiring you to not retain personal data longer than necessary. GDPR in Europe, for example, mandates that data should be kept only for as long as its original purpose requires. If your nonprofit has EU constituents, you should have a policy defining how long you keep their data and when you remove or anonymize it. Even outside of strict legal requirements, practicing good data hygiene with respect to personal information is part of ethical stewardship. Donors and clients trust you with their data; abusing that by hanging onto everything “just because” can backfire, especially if there’s a breach. Reducing your data footprint reduces risk.
Secondly, old data is often bad data. People move, change emails, change names, pass away. Organizations rebrand or merge. If your CRM is cluttered with thousands of inactive contacts or outdated entries, it becomes harder to separate signal from noise. When you run a query or train a model, those irrelevant records could throw things off. We’ve seen CRMs where 30% or more of contact records hadn’t been touched in a decade. In one case, an outreach email accidentally went to a bunch of lapsed contacts and resulted in a spike of bounces and even a few irritated replies (“I haven’t volunteered there in 8 years, why do you still have my info?!”). A solid retention policy would have archived or removed those long-disengaged contacts and avoided that reputational ding.
So how do you decide what to keep and what to let die? Define retention rules for each type of data. For example: “We retain donor records indefinitely, but we archive (deactivate) any donor who has been inactive for 7+ years and had no meaningful interactions in that time.” Or: “We purge event attendance records after 5 years.” The rules can vary – maybe programmatic data tied to federal grants must be kept for X years for audit purposes, whereas email logs can be dumped after Y months. The important part is making a decision and sticking to it.
A data governance committee is a great forum to develop these policies. They can weigh the pros and cons and consider input from all departments (maybe Programs feels they do occasionally reference client records from 10 years ago for follow-up research, so they advocate a longer retention for those). Aim for policies that balance historical value with relevance. And document the rationale so future staff understand why the policy is there (e.g., “Delete financial aid applicants’ data after 3 years to protect privacy and because analysis beyond 3 years isn’t used in decision-making”).
Many systems allow soft deletion or archiving. Use those features. “Archiving” often means the data is still in the system but excluded from everyday views and reports, keeping things cleaner for users. You might archive an alumni’s record when they haven’t engaged since graduation, but still have it in the background should they reappear or for historical reporting. True deletion means it’s gone permanently – use that for data that really has no future purpose or is sensitive. For instance, if you collected personal ID numbers or background check data for volunteers, you might choose to securely delete those once they are no longer volunteering, to eliminate any chance of misuse.
Data retention isn’t just about contacts. Think about your data warehouses, file repositories, and email archives too. Do you really need every email attachment from 2011’s project? Probably not. Clearing out old files (especially those containing personal data) is part of good data governance. Some orgs implement an automatic purge of files older than X years from certain drives, after notifying owners. Again, tie this to compliance and policy – e.g., “We keep board meeting recordings for 2 years, then delete.”
Another hidden cost of keeping everything: “data debt.” This term refers to the burden of maintaining and managing large amounts of legacy data that no longer provide value. It’s analogous to tech debt. Data debt can increase storage costs (especially if you’re on platforms that charge by records or storage, like certain CRMs). More importantly, it increases cognitive load on staff and systems. Imagine an analytics team trying to build a model but having to sift through mountains of outdated info – it’s inefficient. In fact, studies show a high percentage of companies struggle to get value from new systems because of messy, debt-laden data. Nonprofits are not immune to that; if anything, we have fewer resources to throw at the problem. Proactively managing retention helps prevent accumulating a swamp of unusable data.
One Heller client, a mid-size foundation, faced a situation where their CRM was nearing its record limit (which would bump them into a higher-cost tier). A big reason was that they had never deleted a contact, including thousands of one-time event attendees from 15 years ago and ancient mailing lists. We worked with them to define an archiving rule: contacts with no activity in 10+ years and not connected to any current program would be exported and removed from the live system. We verified no active staff was using those, then proceeded. The outcome: a leaner database (20% fewer records), lower monthly fees, and actually improved performance in the CRM (searches and reports ran faster with the reduced load). The team was initially nervous about “losing data,” but with policy and backups in place, they grew comfortable. Now they archive yearly as a routine.
Include data retention in your governance charter. Make it someone’s job to review data age and implement deletions or archiving on whatever schedule makes sense (year-end often is a good time). And make sure your privacy policy (the one you tell donors or clients) aligns – if you promise “we won’t keep your data longer than necessary,” then follow through internally.
To borrow a phrase: sometimes you have to let it go. Let that data die with dignity once it’s past its useful life. Your CRM (and your staff’s sanity) will thank you. Plus, having a lean dataset means when you do dive into analysis or feed data to an AI, you’re working with fresher, more relevant information, which typically yields better results. Cluttered data can confuse both humans and AI. By trimming the bloat, you ensure that what’s left is high signal, low noise.
Hand-in-hand with retention is the idea of ongoing data maintenance. Data quality isn’t a “set it and forget it” thing – it’s like garden maintenance. You’ve got to water the flowers (enter new data properly), pull weeds (fix errors), and trim when things overgrow (archive old stuff). We touched on audits and using data-athons earlier; let’s expand on making data cleanup a sustainable practice.
Many nonprofits kick off a data clean-up as part of a new CRM implementation or a big campaign (e.g. prepping for a capital campaign, you suddenly realize the data needs scrubbing). That’s great, but the worst thing is to clean everything once and then let it slowly decay again. Establish routine processes and even automation to keep your data clean as you go. Here are some tips:
Involve multiple teams in data quality checks. Data entered by one group is often used by another, so occasional cross-reviews can catch issues early and reinforce that data is a shared asset—not siloed work.
Bottom line: make data cleanup a habit, not a one-time project. When people see the payoff—better reporting, higher response rates, more trustworthy dashboards—it becomes easier (and even satisfying) to maintain. Clean data is infrastructure. You wouldn’t run programs in a messy space; don’t run operations on messy data.
And yes—data cleaning doesn’t have to be boring. A good playlist and coffee help.
Designate who is responsible for data in each system or domain, and form a cross‑functional data governance group. This creates clear accountability, establishes authority to enforce standards, and eliminates the “no one owns it” problem that drives poor data quality.
Identify the most critical fields and metrics across the organization. Document clear definitions, acceptable values, and standardized formats or picklists. This focuses effort on high‑value data and ensures everyone shares the same understanding of terms like “active member” or “engaged donor.”
Schedule routine audits to address duplicates, missing information, and errors. Use automation—such as validation rules and deduplication tools—and involve staff through data days or incentives. Ongoing maintenance keeps data trustworthy and prevents costly buildup of data debt.
Identify where data needs to be shared across teams, such as between programs and development. Implement integrations or centralized reporting, and establish a single source of truth for core constituent data. This enables holistic reporting and reduces time spent reconciling spreadsheets.
Decide how long different types of data should be kept, then archive or delete records that exceed those thresholds. Document and communicate these policies clearly. This reduces risk, improves data relevance, supports compliance, and removes clutter that obscures what matters most.
Train staff on data procedures and explain why they matter. Build data literacy so teams can confidently interpret reports, dashboards, and basic AI outputs. Sharing wins from data‑driven decisions builds buy‑in and reinforces a data‑informed culture.
Set basic rules for AI use before large initiatives begin—for example, prohibiting sensitive data in public tools and requiring human review of AI outputs. Consider a small AI working group to explore use cases responsibly. This reduces risk, prevents shadow AI, and aligns experimentation with privacy, ethics, and organizational values.
In the end, this isn’t really about data or AI – it’s about people. It’s about how we honor the information people share with us, how we break down barriers between teams to better serve our communities, and how we learn and adapt to create more value for those we exist to help. Data is the thread woven through all of that.
So take stock of where your nonprofit’s data practices stand today. Celebrate the things you’re already doing well (maybe you have a terrific CRM administrator who’s effectively the “data steward” unifying your org – give that person a high-five!).
Identify one or two areas from this post to focus on improving this quarter. Maybe convene that first data governance meeting, or clean up a pesky dataset that’s been problematic, or draft a simple AI usage guideline. Incremental steps go a long way.
By strengthening your data culture now, you’re not only solving today’s problems – you’re future-proofing your organization for the exciting changes ahead. Whether it’s AI or whatever next big trend comes, you’ll face it on solid footing. And I find that empowering.
Here’s to data-driven, AI-augmented mission success in 2026 and beyond. You’ve got this! And as always, if you need a thought partner on this journey, we at Heller are here to help (we admittedly love this stuff). Happy data cleaning, and may your data forever be in your favor!
Our data and AI strategy approach starts with clarity, not tools. We work with you to define ownership, prioritize the data that truly matters, and establish governance practices that make information reliable, usable, and secure. From there, we design a practical roadmap that connects data quality, integration, and analytics to real organizational goals—whether that’s better reporting, smarter segmentation, or preparing for responsible AI use. AI is introduced deliberately and safely, with clear guardrails, human oversight, and an emphasis on augmenting staff decision‑making rather than replacing it.
With decades of experience helping mission‑driven organizations manage complex data ecosystems, we understand both the technical and human sides of data and AI adoption. Our consultants combine deep platform knowledge with nonprofit‑specific insight, guiding organizations through data cleanup, integration, analytics foundations, and early AI exploration without creating unnecessary risk. The result is not experimentation for its own sake, but a durable data foundation that supports trust, scales with emerging technology, and enables AI to deliver meaningful, mission‑aligned value.
Heller Consulting helps nonprofit, education, and healthcare organizations leverage technology to achieve their missions. There is no more experienced platform-agnostic technology services partner serving the cause sector.
