Curiosity Management is the Next Era of Nonprofit Data Strategy

Reflections from a June webinar on nonprofit data strategy and discussions with Tracy Kronzak and Jake Martin White.

Introduction

In June, I joined Tracy Kronzak of TK Endeavours and Jake Martin White of Purple Otter for a webinar on nonprofit data strategy. The conversation was primarily planned around building a modern data architecture and nonprofit data strategy—specifically, the shift many organizations are making from treating CRM systems as the center of their technology ecosystem to treating data itself as the strategic asset.

We covered all the expected topics. Data lakes. Data governance. AI. Nonprofit analytics. The opportunities created by emergent data platforms and the challenges of managing ever-growing volumes of information. But as often happens in good conversations, the most interesting idea wasn’t on the agenda.

At one point, Tracy observed that for years, nonprofit technology leaders have focused on change management. We implemented systems, trained users, redesigned processes, and helped organizations adapt to technological constraints. Increasingly, they suggested, the challenge is something different. We’re moving from an era of change management into an era of curiosity management.

The phrase has stayed with me ever since because it explains not only what’s changing in nonprofit technology but also why.

The Limits of the Change Management Era

For much of the last two decades, nonprofit technology projects were fundamentally exercises in helping people adapt to systems. Organizations implemented CRM platforms, fundraising tools, marketing automation systems, volunteer management applications, and countless other technologies. Success was measured by adoption rates, training completion, workflow compliance, and whether staff were using the technology as intended.

That approach made perfect sense for the environment we were operating in. Technology, even enterprise platforms, was relatively rigid, and organizations often had to shape their processes around the capabilities of the systems they purchased. If you were implementing a CRM fifteen years ago, the challenge wasn’t discovering new questions to ask. The challenge was getting everyone to consistently enter data, follow the process, and trust the system.

Over time, the CRM became the center of the nonprofit technology universe. Fundraising data lived there. Volunteer information lived there. Membership records lived there. Event participation lived there. Marketing engagement increasingly found its way there. Whenever a new technology challenge emerged, the default response was often the same: put the data in the CRM.

In many ways, this was an extraordinary step forward for the sector. Cloud-based CRM platforms helped organizations move beyond disconnected spreadsheets, departmental silos, and fragile on-premise systems. For the first time, many nonprofits had a centralized, accessible, and secure location for organizational knowledge. The problem wasn’t the CRM itself. The problem was that as data volumes exploded and analytical needs became more sophisticated, we began asking CRM systems to do jobs they were never designed to do.

We wanted them to be operational systems, reporting systems, analytics systems, forecasting systems, and increasingly organizational intelligence systems. The result was predictable. We accumulated enormous amounts of data, but not always more understanding.

From CRM as Hub to Data as Hub

This challenge sits at the heart of a conversation Tracy and I have been having for several years. We’ve argued that organizations should stop thinking about the CRM as the hub and start thinking about data as the hub. We connected with Jake last year at the Bridge Conference, and he’d been thinking the same thing.

That distinction may sound subtle, but it’s significant.

Tracy mentioned that data is the lifeblood of an organization because it outlasts staff, technology platforms, and even organizational structures. Applications come and go. Vendors rise and fall. Technologies that once seemed indispensable eventually become legacy systems. Through all of those changes, however, the institutional knowledge contained within an organization’s data remains.

Historically, CRM platforms became powerful centralization engines because they represented the most accessible place to aggregate information. Today, organizations have options that simply didn’t exist ten or fifteen years ago. Modern data platforms allow nonprofits to preserve information in its raw form, integrate data from multiple systems, and create analytical models that aren’t constrained by the architecture of a single application. This shift is even more profound as organizations leverage agentic AI to conduct research over distributed knowledge, build applications with AI that meet discrete needs, and seek deeper and more meaningful engagement with both constituents and donors with AI.

During the webinar, Jake Martin White offered a metaphor that perfectly captured the shift. Traditional approaches often require organizations to decide in advance exactly how data will be used. It’s like baking a cake and putting it on the shelf, hoping it will still be the cake you need years later. Modern data platforms allow organizations to preserve the ingredients instead. Rather than committing to a single use case, they preserve the raw materials and create different outcomes as organizational needs evolve.

That sounds technical, but its implications are strategic. Organizations no longer need to predict every future question before they build their data strategy. Instead, they can create the flexibility to explore new questions as they emerge.

And that’s where curiosity enters the picture.

The New Problem Isn’t Access to Information

For years, nonprofits struggled because they couldn’t get access to information. Data lived in disconnected systems, reporting was difficult, and meaningful analysis often required significant technical effort.

Today, many organizations face a different challenge. They have more information than they know what to do with.

They can tell you which emails were opened, which web pages were visited, which events were attended, which forms were completed, and which donations were made. The problem isn’t the absence of data. The problem is deciding what matters.

Should we focus on donor retention or donor engagement? Should we focus on lifetime value or acquisition? Should we focus on volunteer conversion, program outcomes, operational efficiency, or some combination of all of them?

Technology can increasingly help us answer any of those questions. The harder challenge is deciding which questions deserve our attention in the first place.

AI Has Created an Abundance Problem

This tension becomes even more apparent when we talk about AI.

Every nonprofit leader I know is feeling some level of pressure around AI right now. Boards are asking about it. Vendors are selling it. Conferences are filled with sessions about it. Strategic plans increasingly include it. The conversation often begins with a question like, “What is our AI strategy?”

But increasingly, I think that’s the wrong place to start.

During our discussion, Tracy pointed out that organizations are often told to “go experiment with AI” or “become an AI organization” without first defining what they’re actually trying to learn, improve, or accomplish. That’s a dangerous place to be because AI excels at producing answers, not all of them relevant or actionable. What it cannot do is determine which questions matter most to your organization. They also warned that the “just use it” attitude was a moral equivalent of technological redlining: telling someone to simply buy a house without consideration for historical access. Nonprofit technology capacity and infrastructure access remains stunted and inconsistent across the sector.

For years, the challenge was finding answers. Now the challenge is determining which questions deserve answers. When technology becomes capable of producing almost limitless insights, organizational success becomes less dependent on the availability of information and more dependent on the quality of inquiry.

In other words, curiosity becomes a strategic capability. And, according to Tracy, it also becomes the starting point of liberating organizations from co-dependence with Big Tech.

Curiosity Starts with Capability

One of Jake’s most important observations during the webinar was that organizations often begin technology conversations in the wrong place. They start by asking what technology they need when they should be asking what capabilities they need.

That’s an important distinction.

An organization managing a file of 20,000 donors has different needs than a national organization managing millions of constituent records. An organization built around major gifts requires different capabilities than one focused on broad-based community fundraising. The tools may be similar, but the outcomes they’re trying to achieve are not.

By focusing first on organizational capability, leaders create a much clearer framework for evaluating technology decisions. What do we need to become better at? What information do we need to support that capability? What processes, skills, governance practices, and technologies are required to make it successful?

Those questions force organizations to think beyond products and platforms. They connect technology investments directly to mission outcomes. In many ways, curiosity management begins there.

What Curiosity Management Actually Means

As Tracy described it during our conversation, we’re moving from the era of change management into the era of curiosity management. For decades, nonprofit technology leaders focused on helping organizations adapt to systems. Increasingly, the challenge is helping organizations focus their questions.

In Tracy’s formulation, curiosity management begins by asking the right questions of our organizations and constituents and ends by asking the right questions of our data, while having an adaptable framework for technology decision-making and governance.

That doesn’t mean encouraging endless experimentation. It doesn’t mean chasing every new technology trend or implementing AI because everyone else is doing it. Curiosity management is the practice of connecting organizational questions to organizational outcomes.

Before implementing an AI solution, what insight are we trying to gain? Before building a dashboard, what decision are we trying to improve? Before collecting additional data, what action will change if we learn something new?

Those questions sound deceptively simple, but they force organizations to move beyond technology for technology’s sake. They require leaders to define success before selecting tools. They encourage organizations to begin with purpose rather than capability. Most importantly, they shift the conversation away from hype and back toward mission.

Governance Is What Makes Curiosity Productive

One of the realities of this moment is that the more sophisticated our technology becomes, the more important the fundamentals become.

Every discussion about AI eventually circles back to the same topics: data quality, governance, definitions, identity resolution, source-of-truth decisions, and human oversight. The reason is simple. Technology amplifies whatever foundation already exists. Tracy would point out that AI is an accelerant on this entire dynamic, much like pouring gasoline on a fire. The question is, what kind of fire do you have?

If your organization has inconsistent definitions, AI will generate inconsistent answers. If constituent records are fragmented, AI will analyze fragmented information. If data quality is poor, automation will simply help you make mistakes faster.

Good governance is not the enemy of curiosity. Good governance is what makes curiosity productive.

The organizations seeing the most success with modern data strategies and AI adoption are not necessarily the ones adopting technology the fastest. They’re the ones building trusted foundations that allow curiosity to be explored responsibly.

The Future Belongs to Better Questions

One observation continues to stay with me from that conversation with Tracy and Jake.

For years, nonprofit technology leaders helped organizations manage change. That work mattered, and it still matters. But the next chapter may be different.

The next chapter may be about helping organizations manage curiosity.

Technology will continue to evolve. AI will improve. New platforms will emerge. Today’s innovations will eventually become tomorrow’s legacy systems. The organizations that thrive won’t necessarily be the ones with the most data, the most dashboards, or even the most AI.

They’ll be the ones who know what they’re trying to learn, why it matters, and how to turn that understanding into action.

The future belongs to organizations that ask better questions—and build the people, processes, governance, and technology needed to answer them.

You can watch the recorded webinar here: https://www.youtube.com/watch?v=EEORz_QMejw. We kept this one PG-13 for language.

In case you’re interested, here’s a previous post that gets to the heart of nonprofit data strategy: https://roisolutions.com/resources/the-importance-of-data-strategy-the-data-we-have-the-data-we-want-and-the-data-we-need/

And if you’d like to explore this topic, Let’s Talk!