Data Dilemmas: The Top 3 Concerns Associations Face Before Turning Data Into Insights

By Margaret Kuon

IN THE DIGITAL era, associations recognize that technology plays a pivotal role in identifying industry trends, driving member engagement, and launching new initiatives. Technology is a powerful engine for deepening understanding and pinpointing priorities. Generative AI (Gen AI), especially, is seen as a gamechanger. However, the value of even the most advanced tools and platforms hinges on one critical element: data. Without clearly defined, accurate, and well-managed data, technology is unable to deliver the insights that associations need to thrive. For associations to leverage their data assets, it’s essential to understand three core concerns that can make or break these efforts.

1 The State of the Data

Associations are beginning to recognize that their data is incredibly valuable, but it’s often not yet organized or refined enough to be used effectively. Starting with fragmented and inconsistent data creates an uphill battle. For example, exporting a list of your members with demographic information such as address, company size, specialty, and length of membership sounds like a straightforward task, but here’s the catch: these pieces of information may exist in different pools of data within your organization. Your legacy membership database, the member company profiles in your online buyers’ guide, or your marketing department’s latest survey responses are all potential sources of data, and each produces a report in its own format with slightly differently named fields and varying dates of freshness.

Turning this data into actionable insight simply isn’t possible until each piece of information has been defined and parameters for each classification are established. Maybe you find yourself reorganizing this multi-sourced data on an annual basis. You export the information from different sources and piece together the fields you need to create a new dataset to get an annual snapshot of your overall membership. Not only is it inefficient to repeat this every year, but you may also inadvertently introduce errors or misidentify a trend due to differences in how you define an item from year to year.

Feeding all this data as-is into Gen AI will not yield an accurate analysis either. Gen AI is a powerful tool, but it cannot fix issues with low data quality and lack of consistency. Asking the right questions from the start to reach an organizationally unified understanding of each piece of information is more important than any tool.

2. Ethical Data Management

Associations know their industries are changing and are interested in understanding the why and how, as well as how it’s impacting their members’ needs. However, they may be held back by concerns about ethical uses of data and their members’ privacy. One reason is the fragmented sources of data within an organization. When information exists in multiple locations, questions arise about who in the organization should have permission to access it, what filters should be set up in pulling a report, and from where. The results may be inconsistent and the origin uncertain, making associations reluctant to use the data. Associations are also concerned about privacy legislation such as the General Data Protection Regulation (GDPR).

Because protecting and managing data ethically is a responsibility and a strategic imperative, it’s vital to establish organizational procedures around data governance. At the staff level, this entails defining data ownership: clarifying the personnel responsible for data accuracy, access, and updates. To help address privacy legislation concerns, association staff are not alone: their software as a solution (SaaS) partners can offer expertise. For example, an association that’s only U.S.-facing doesn’t need its career center to be GDPR-compliant.

On the other hand, when their international expansion efforts become successful and they now have global members, their SaaS partner can demonstrate how to make the necessary privacy-control adjustments in the career center to become GDPR-compliant.

Data privacy concerns extend to using Gen AI tools as well. Associations must ensure that staff use the organization’s enterprise-edition Gen AI, which allows the organization to opt out of data sharing for training purposes. Opting out ensures that data stays private and isn’t inadvertently used to train models that could expose the data in future outputs.

When data governance standards are in place, associations can confidently explore ways to leverage their data to better understand and serve their members.

3. Building a Data-Driven Culture

An association might ask, “How do we figure out if what we’re doing matters to our members?” Because data and strategy teams are the most understaffed areas in associations, they may struggle to realize the full potential of their data. Nearly half of Naylor’s 2025 Association Benchmarking Report survey respondents report a lack of resources to effectively leverage data. Nonetheless, a lack of in-house data expertise doesn’t mean progress is out of reach. Building a data-driven culture begins with a shift in mindset, not with advanced tools or technical hires. Associations must recognize that data isn’t just a byproduct of operations; it’s a strategic asset, and empowering staff to be curious and ask questions increases the usefulness of that asset.

The first step is to start asking questions. For example: What do members think about the services offered? Does the pricing for a particular service make sense? Are our preconceived notions on what members want correct? Even simple surveys based on a member’s comment can help identify trends, especially when conducted regularly. These efforts help associations move beyond anecdotal feedback and begin to identify patterns that can inform strategy.

Equally important is the willingness to act on what the data reveals. A data-driven culture encourages staff to test assumptions, measure outcomes, and adjust the course based on evidence. It empowers teams to move from “we think” to “we know” and to make decisions that are grounded in real member behavior and sentiment. When staff are equipped to interpret and apply data, the entire organization becomes more agile and aligned with its mission.

As associations consider how they can leverage data to drive their strategy, they will increasingly recognize the need to evolve their thinking about data. This includes moving from a “good-enough” siloed data system toward a more defined, purposeful structure; establishing a data governance policy and becoming comfortable with the privacy tools available; and cultivating an environment in which everyone shares findings regularly and assumptions are validated as accurate. With thoughtful investment in strategy and structure, associations will be better positioned to uncover insights, anticipate trends, and lead their industries with the power of data-backed decision-making.

Margaret Kuon is the Directory Production Manager at Naylor, where she leads the development of data-driven directories and resource guides that support association growth and non-dues revenue for trade and industry association partners. Drawing on her background in content strategy and technical communications, she collaborates with association partners to curate meaningful, member-focused information that strengthens engagement and elevates the member experience.