Data Analytics in Nonprofits

My nonprofits in the community - are you planning a donor survey in the next two months? Here are some examples of how you can ensure that the data does not sit silently in your work folders but actually lets it help you take meaningful actions. Example 1: Say your survey question is: "How likely are you to continue donating to our organization in the next year?" ● Data says: If 60% of donors say they are "very likely" to continue donating, but 30% are "somewhat likely" and 10% are "unlikely," this indicates a potential drop-off in donor retention. ● Turning that data into action: Focus retention efforts on the "somewhat likely" group. Create a targeted campaign that re-engages these donors by highlighting recent successes, impact stories, or new initiatives they might care about. Additionally, reach out to the "unlikely" group to understand their concerns and see if any issues can be addressed. Example 2: Say your survey question is: "Which of the following areas do you believe your donation has the most impact?" ● Data says: 50% of respondents say their donation has the most impact on "Education Programs," while only 10% say "Healthcare Initiatives." ● Turning that data into action: Understand the why and promote the success and need for your "Healthcare Initiatives" more prominently, aiming to increase donor awareness and support in this underfunded area. Example 3: Say your survey question is: "What is your primary reason for donating to our organization?" ● Data says: If the top reason to engage is "Alignment with my values" (40%) followed by "Transparency in how funds are used" (35%). ● Turning that data into action: Emphasize your organization's values and transparency in all communications. Regularly update donors on how their funds are being used with clear, detailed reports, and align your messaging with the core values that resonate with your donor base. Example 4: Say your survey question is: "How satisfied are you with the level of communication you receive from our organization?" ● Data says: If 70% of donors are "satisfied", 20% are "neutral," and 10% are "dissatisfied," there's room for improvement in communication. ● Turning that data into action: Understand the "neutral" and "dissatisfied" groups to pinpoint where communication may be lacking. This could involve increasing the frequency of updates, personalizing communications, or providing more opportunities for donor feedback and engagement. Sit with the data you collect. Read the numbers. Read the stories. Read the hopes, barriers, and interests of those humans in your data. The best possibility of a survey is to make the humans in that data feel included and belong by listening and acting on their perspectives. Co-create change with your community in those surveys. #nonprofits #nonprofitleadership #community #inclusion

Data and analytics in the nonprofit sector in the UK is honestly wanting, so I decided to do something about it. I've been working on something for the past several months; a practical framework/guide to use when conducting data and analytics audits in the nonprofit and charity sector in the UK and beyond. It came from a pattern I've been seeing across organisations: large amounts of data being collected, reports being produced, and very little clarity about whether the numbers were accurate, what they actually meant, or whether the people the organisation was serving had any voice in them at all. I have almost a decade of data and M&E experience across NGOs, government, and the private sector in Kenya and the UK. I've noticed that the problem is rarely a lack of data. It is almost always a lack of data infrastructure, definitions, governance, and honest measurement. The framework I've been working on covers audit methodology, data quality assessment, outcomes measurement, maturity modelling, and implementation planning. It will be free for anyone working in the nonprofit sector to download and use as a guide when conducting data audits. Whether you're a data analyst, a CEO, or a programme manager who inherited a reporting mess, this framework is for you. I'll be publishing it soon. If this is something your organisation needs, or you know someone who does, share this post. Let's get it to the people who need it most. And follow to be notified when I publish. Also, do you work in a nonprofit or public sector, how is the data ecosystem? I know some nonprofits don't even have data functions which is alarming. #dataanalysis #nonprofit #datagovernance #charitysector #dataanalytics #monitoringandevalution

No Data = No Value: The Inconvenient Truth About AI Everyone wants to talk about AI models. Nobody wants to talk about data. That’s a problem. The Uncomfortable Reality The most powerful algorithm in the world, fed garbage data, produces garbage outcomes. No exceptions. Yet organizations keep pouring millions into model development while treating data quality as an afterthought. Data scientists spend 60–80% of their time just cleaning data. That tells you everything you need to know about the state of most organizations’ data. What Trustworthy AI Actually Requires ·       Accurate data — or your model is confidently wrong ·       Complete data — or your model has dangerous blind spots ·       Consistent data — or your model learns confusion ·       Timely data — or your model solves yesterday’s problems ·       Properly labeled data — or your model systematically fails Miss any one of these, and trust evaporates. The Stakes Are Real Flawed hiring tools. Biased customer scoring. In every high-profile AI failure, the root cause wasn’t a bad algorithm. -->It was bad data. And with state-level AI legislation gaining momentum, “we didn’t know our data was flawed” won’t cut it anymore. What We’ve Seen at PwC This isn’t theory—it’s what we’ve lived. At PwC, our experience has been clear: investing in strong data governance, quality, and management didn’t just improve our AI outcomes—it skyrocketed the speed and quality of our AI delivery. When the data foundation is right, everything accelerates. Models train faster. Results are more reliable. Stakeholders trust the outputs. Deployment timelines shrink dramatically. When it’s not, even the best teams spend their time firefighting data issues instead of building value. What To Do About It 1.     Fund data like you fund models. Focused data investment in not optional. 2.     Treat labeling as strategy, not grunt work. Labelers encode judgment into your system. 3.     Measure data quality as a KPI. Not just model accuracy. 4.     Build feedback loops. Data drifts. Catch it early. The Bottom Line The winners in AI won’t have the fanciest models. They’ll have the cleanest data. Poor data = poor outcomes. Quality data = trustworthy AI. Stop chasing algorithms. Start fixing your data. Have you seen AI projects fail because of data quality? Share your experience below. #ArtificialIntelligence #DataQuality #TrustworthyAI #ResponsibleAI #AIStrategy Chris Dulny Jennifer Johnson Alaina Tennison, FCPA, FCA Josée St-Onge, FCPA, CIA, CRMA, PMP Mitchell Reisler Robert Bzdziuch Vanessa H.

𝗪𝗵𝗲𝗻 “𝗕𝗮𝗱” 𝗗𝗮𝘁𝗮 𝗶𝘀 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗚𝗼𝗼𝗱 Looks can be deceptive.  Ask the nonprofit that pursued a flashy corporate partner to make their Annual Report look good.  The corporate partner turned around and twisted the org to make major changes in their programs to better match their brand and required impossible reporting timelines. The same lesson applies to your data, but in reverse.  Real progress can actually be mistaken for decline. Consider the following: 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 > 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝘆 • 𝘚𝘶𝘳𝘷𝘦𝘺 𝘳𝘦𝘴𝘱𝘰𝘯𝘴𝘦 𝘳𝘢𝘵𝘦𝘴 𝘧𝘦𝘭𝘭: Did the feedback quality improve because only recipients genuinely vested in the cause responded? • 𝘕𝘰 𝘰𝘧 𝘱𝘢𝘳𝘵𝘯𝘦𝘳𝘴𝘩𝘪𝘱𝘴 𝘥𝘦𝘤𝘳𝘦𝘢𝘴𝘦𝘥: Have you started focusing on value alignment instead of logos? • 𝘌𝘮𝘢𝘪𝘭 𝘭𝘪𝘴𝘵 𝘴𝘩𝘳𝘢𝘯𝘬: Have you invested hours cleaning it up? Gone are all the ghosts.  Is your open rate higher?   𝗖𝗹𝗮𝗿𝗶𝘁𝘆 > 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 • 𝘋𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥 𝘒𝘗𝘐𝘴 𝘥𝘦𝘤𝘭𝘪𝘯𝘦𝘥 𝘧𝘳𝘰𝘮 20 𝘵𝘰 10 𝘮𝘦𝘵𝘳𝘪𝘤𝘴: Is clarity your new mantra?  Have you gotten off the “just in case” data collection bandwagon? • 𝘙𝘦𝘷𝘦𝘯𝘶𝘦 𝘥𝘦𝘤𝘳𝘦𝘢𝘴𝘦𝘥: Is it because you said "No" to restricted-funds that came with strings attached?   𝗗𝗲𝗽𝘁𝗵 > 𝗕𝗿𝗲𝗮𝗱𝘁𝗵 • "𝘔𝘦𝘴𝘴𝘺" 𝘯𝘰𝘯-𝘯𝘶𝘮𝘦𝘳𝘪𝘤 𝘥𝘢𝘵𝘢 𝘪𝘯𝘤𝘳𝘦𝘢𝘴𝘦𝘥: Kudos!  Have you started listening and capturing the stories that factor intangible impact your programs are having? • 𝘌𝘷𝘦𝘯𝘵 𝘈𝘵𝘵𝘦𝘯𝘥𝘢𝘯𝘤𝘦 𝘳𝘦𝘥𝘶𝘤𝘦𝘥: Is headcount no longer your goal?  Have you shifted attention to designing experiences that attract authentic engagement and behavioral change? • 𝘝𝘰𝘭𝘶𝘯𝘵𝘦𝘦𝘳 𝘴𝘪𝘨𝘯-𝘶𝘱𝘴 𝘧𝘦𝘭𝘭: Has your volunteer retention rate increased?  Are volunteer satisfaction levels and engagement up? • 𝘕𝘦𝘸 𝘪𝘯𝘪𝘵𝘪𝘢𝘵𝘪𝘷𝘦 𝘥𝘦𝘭𝘢𝘺𝘦𝘥: Have you started running test pilots before scaling to ensure higher probability of impact? 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺𝗹𝗶𝗻𝗲 I could go on but you get the drift. The drops above reflect an org’s growing maturity when it comes to its mission, staff, volunteers and data.  They are indicators that you are making hard choices over vanity metrics. 𝘏𝘰𝘸𝘦𝘷𝘦𝘳, 𝘯𝘰𝘵 𝘦𝘷𝘦𝘳𝘺 𝘥𝘦𝘤𝘭𝘪𝘯𝘦 𝘪𝘮𝘱𝘭𝘪𝘦𝘴 𝘱𝘳𝘰𝘨𝘳𝘦𝘴𝘴 - 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘮𝘢𝘵𝘵𝘦𝘳𝘴. 𝗪𝗵𝗮𝘁’𝘀 𝗮 𝗻𝗲𝘄 𝗺𝗮𝗻𝘁𝗿𝗮 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴 𝘁𝗵𝗮𝘁 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲 𝗮 𝘀𝘁𝗲𝗽 𝗯𝗮𝗰𝗸 𝗯𝘂𝘁 𝗶𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗴𝗿𝗼𝘄𝘁𝗵?

How do not-for-profit organizations overcome barriers to scale on tight budgets? Andrew Patricio, Principal for Data and Analytics at UnidosUS (@WeAreUnidosUS) shared their story and the answer to today’s big question. The story starts here: “I walked into a sea of Excel—nothing but spreadsheets.” UnidosUS is the largest Latino civil rights and advocacy organization in the U.S., supporting more than 300 affiliate nonprofits in health, education, housing, and workforce development. Their mission is expansive, but their tools weren’t keeping pace. Affiliate data was locked in a 256-column spreadsheet. One team fielded every data request. And every answer required time someone didn’t have. The risk wasn’t just inefficiency. It was a missed opportunity to act faster, listen closer, and deliver greater impact. That’s why Andrew sought out Quickbase.  “It’s been my go-to tool for years. Especially when you’ve got a pile of spreadsheets and no time, budget, or need for a full custom app—Quickbase is perfect.” Andrew rebuilt their affiliate system in Quickbase. Data became accessible without compromising control. Reporting became self-service. And for the first time, the organization could see and scale what was working, without needing a full-time developer. The impact speaks for itself: $80K+ saved in software spend, real-time visibility across affiliates, a more confident, empowered team, and a new way of thinking about what tech should do. UnidosUS doesn’t exist to be good at software. Chances are, neither does your organization. → When your productivity tools let you work the way you want to work, you get to spend more time scaling the impact of your mission and less time fighting your tech stack.  https://lnkd.in/d_ksmmfp 

Most nonprofits can tell you how many people they served last year, but have a much harder time telling you whether those people are better off. I've seen this pattern across the sector for years. Organizations collect a bunch of data, including intake numbers, service hours, referral counts, and demographic breakdowns, it gets reported to funders, and that's usually where it ends. When asking questions like did those services actually stabilize housing, reduce ER visits, or keep a family together, the data to answer them can't be found because everyone tracks their own piece, in their own system, with no way to connect it all. When I think about what a client's journey actually looks like... they might access employment support from one organization, transitional housing from another, and mental health services from a third. Each provider records its own outputs, but rarely can they see the full picture or tell you whether the combination of those services produced a lasting result. In the social sector, we are very good at counting activities, but have challenges measuring change. Moving from tracking transactions to tracking trajectories is important. Not just "we served this person," but "this person moved from crisis to stability over twelve months, and here's what that path looked like." That starts with integrated systems, shared outcome definitions across programs and services, and the analytical capacity to turn data into evidence. If boards, funders, and executive directors can work from the same understanding, then all of the data being collected can be used to really determine what's actually helping people and what isn't.

Your dashboard has 47 metrics on it. How many of them actually changed a decision last month? Most nonprofits aren't suffering from a lack of data. They're suffering from data that doesn't connect to action. The shift happening right now in the best development shops: Out: open rates, impressions, event attendance counts In: portfolio velocity, pipeline conversion, predicted giving capacity One fundraising team I worked with cut their reporting dashboard from 30 metrics to 6. Every metric had to answer one question: "What do we do differently because of this number?" Major gift meetings increased. Staff spent less time building reports and more time in front of donors. Vanity metrics feel productive. Actionable metrics change behavior. What's the one number your whole team actually rallies around?

𝗙𝗼𝗿 𝗺𝗮𝗻𝘆 𝗻𝗼𝗻𝗽𝗿𝗼𝗳𝗶𝘁𝘀, "𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲" 𝗶𝘀𝗻'𝘁 𝗮 𝗖𝗥𝗠. It's a collection of 5,000+ duplicate contacts, 7 fragile integrations, and 4 critical reports that are just exported to a spreadsheet anyway. The vision is a "single source of truth." The reality is manual receipts and data entry. We all know the friction points:  • 𝗗𝗮𝘁𝗮 𝗙𝗿𝗶𝗰𝘁𝗶𝗼𝗻: Duplicates from every import. Addresses that age faster than you can clean them.  • 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗙𝗿𝗶𝗰𝘁𝗶𝗼𝗻: Using spreadsheets as a database. Using your inbox as a coordination tool.  • 𝗧𝗲𝗰𝗵 𝗙𝗿𝗶𝗰𝘁𝗶𝗼𝗻: That online donation integration that fails silently, losing data until a fundraiser complains. 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗯𝗿𝘂𝘁𝗮𝗹 𝘁𝗲𝗻𝘀𝗶𝗼𝗻: They are selling you a "unified data model." But you can't unify chaos. You can't "orchestrate" with Flow and Omni-Channel when you don't even know what data is trustworthy. But here's what actually works, with measurable outcomes: 𝗙𝗜𝗫 #𝟭: 𝗗𝗮𝘁𝗮 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻  → Implement rule-based matching and duplicate jobs  → 80% reduction in duplicates (50k database: 4,000 down to 800)  → 8% increase in campaign conversion rates  → Measurable fundraising ROI improvement 𝗙𝗜𝗫 #𝟮: 𝗙𝗹𝗼𝘄-𝗕𝗮𝘀𝗲𝗱 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻  → Automate receipts, reconciliation, exception reporting  → 60% reduction in manual workload  → 50% fewer errors from manual processes  → SLAs drop below 24 hours 𝗙𝗜𝗫 #𝟯: 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗢𝘂𝘁𝗰𝗼𝗺𝗲 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴  → Build results model with 3-5 core indicators  → 70% reduction in report creation time  → Real-time KPI visibility for leadership  → 40% improvement in volunteer coordination efficiency Forget the 5-year AI roadmap. The real, un-glamorous work is 𝘀𝘁𝗮𝗯𝗶𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻. Before you look at another new feature, go find the one process that breaks the most. See why it breaks. Fix just that. Discipline before orchestration. Visibility before migration. That's the real roadmap. I'd love to hear from the Admins and Consultants working on Nonprofit Cloud. Share your thoughts on this (Maybe I'll learn something I am missing). #Salesforce #SalesforceAdmin #Nonprofit #NPSP #NonprofitCloud 

The latest episode of the Fundraising.ai podcast was recorded live at AWS HQ in New York, and it felt like one of those conversations that cuts through the noise fast. For the past few years, nonprofits have been nudged to believe AI is the strategy. Adopt the right tools quickly enough and the future will take care of itself. But that framing is backwards - or at least incomplete. In this conversation, industry pioneer and dear friend Kelley Hecht (AWS) and I unpack a more grounded truth: AI is not the strategy. It is a powerful set of tools that will either amplify clarity or accelerate chaos. Here are 6 takeaways we dive into that, I believe, can make a big difference for nonprofits navigating their AI journey: 1️⃣ Start with outcomes, then work backwards (mission obsessed, constituent obsessed). 2️⃣ Data strategy is not optional anymore - it is the foundation AI stands on. 3️⃣ Perfect data is a myth. Progress beats waiting to be “ready.” 4️⃣ Analytics should not only be a rearview mirror - it should help you drive. 5️⃣ This is a team sport. Real traction takes cross-functional ownership. 6️⃣ Senior leadership support matters (and CFOs can be an underrated ally). If you have felt overwhelmed by AI conversations, unsure where to start, or worried you are falling behind without even knowing what “ahead” looks like, this episode is for you. Listen here: https://lnkd.in/ef4BmFae Kelley brings what she always brings: intention, wisdom, and strategy (plus just enough friendly skepticism to keep the hype in check). Also, she now holds the unofficial record for most Fundraising.AI return visits, so yes, we probably owe her a jacket. #FundraisingAI #Nonprofits #DataStrategy #ResponsibleAI #AWS #CloudComputing #NonprofitLeadership #AIForGood

What an “AI-Ready Organization” Actually Looks Like (And why most institutions aren’t there yet.) Over the last 5 years, I’ve worked with nonprofits, universities, and mid-size organizations that want AI but struggle to get real outcomes from it. The reason is simple: AI doesn’t fail because of the model. It fails because the organization isn’t ready for it. Here’s what readiness actually looks like (with the numbers behind it): 1. Clean, trustworthy data Only 12% of organizations say their data is AI-ready and 67% don’t fully trust the data they use for decisions. AI can’t compensate for inconsistent, siloed, or outdated information. 2. Documented, stable workflows Where workflows are mapped and standardized, organizations see 30–50% cost reduction and major drops in error rates when they apply AI. AI accelerates clarity or accelerates chaos. 3. Internal champions, not just tools 77% of employees already have the potential to become AI champions. The companies that scale AI make these people the engine of adoption. Capability beats top-down mandates. 4. Governance that enables scale 62% of organizations say the biggest barrier to AI is lack of governance, not lack of technology. Clear roles, data access rules, and risk guidelines make AI reliable and safe. If your organization wants to see real results from AI, the foundations matter: your data has to be reliable, your workflows have to be clearly defined, your teams need the capability to adopt new ways of working, and your governance must support responsible scale. These elements together create the operational maturity that turns AI from a series of experiments into measurable outcomes. If you’re building this maturity within your institution, whether a nonprofit, a university, or an enterprise, I’m happy to share what I’m seeing across the sector and what’s actually working. Book a free consultation call. Link in comments.