For today’s most strategic associations, data is central to long-term viability. “After your people and your cash, data is the third most important asset,” says Tori Miller Liu, FASAE, CAE, president and CEO, Association for Intelligent Information Management (AIIM).
Data should inform decisions about all aspects of an organization, including programs and services, governance and advocacy, and nonmember outreach. Leaders who embrace a data-driven mindset recognize that data science plays a critical role in identifying patterns and trends.
“Research shows the one constant in business today is change. Some even argue that the pace of change has accelerated in recent years,” explains Jennifer McNelly, CAE, CEO of the American Society of Safety Professionals (ASSP). “We face increasing complexity in member needs, technology, and competitive landscapes. Data provides insights into trends, member behavior, and market dynamics, allowing associations to be proactive rather than reactive.”
And it’s not just staff who reap benefits from data. “If you’re asking any committee or board group to make a decision, you’ll need to give them data,” regarding costs of a project or program, expected revenue, past participation, and any expressed interest, suggests Nikki Golden, CAE, strategist at research and consulting firm Association Laboratory. “You can’t measure success without data.”
Build a Data Culture
Building a data culture starts from leadership, with the association board, executive leadership, and frontline staff driving business decisions, according to McNelly. “The data-driven mindset needs to be owned at every level, and the most powerful way to do that is to model data use.”
But it’s OK to start small, says Liu. “Appreciate what your team is already doing. It’s very likely that data is already a major part of your team’s work lives.”
The three most important considerations for a data-driven culture, according to Liu, are data, people, and technology.
The data that an association aggregates and references should be high-quality, secure, protected, governed, and disposed of when it’s no longer useful, according to Liu.
The people—i.e., association staff—should understand how to leverage data correctly: to test theories and make decisions, and to repurpose data so that it’s used to inform overall decisions, and in compliance with data regulations.
Finally, associations need appropriate technology, Liu suggests—systems to facilitate storage and processing, as well as data backup and recovery tools.
Golden recommends aggregating and sharing key information via a dashboard that can be accessed by some or all staff members, to provide a high-level overview. Basic information, such as member counts, renewal counts, and participation in key programs and conferences, could populate the dashboard and equip staff with information to assist in planning.
“You also need an API-first methodology,” adds Liu. “Any software you’re using should have an application programming interface—API—available so that the data is readily available, extractable, and capable of integrations with other systems.”
Collect Relevant Data
Intention matters when determining the types of data to gather. Most impactful, according to McNelly, is demographic-related data, which assists in understanding the makeup of the membership; purchasing and behavioral data; association engagement data, which can inform volunteer roles and leadership positions; and even some forms of company-specific public data. “All this data can give a holistic approach to customer engagement.”
ASSP staff use data in the business planning process and investment decisions, governance, design of events and member experience, benefits and services, and market positioning. “We also added a research and data competency into the leadership team’s competency expectations,” McNelly says.
Of utmost importance is aggregating relevant data. “Make a conscious decision to determine how you will use” any data that you collect, says Liu. “A legitimate reason, so you are not defying data privacy regulations”—mandated in the U.S. or internationally.
Both implicit and explicit data are necessary to make the most informed decisions. Take member engagement data, suggests Liu: What are people interested in? What are they worrying about? From an implicit standpoint, associations can access data from surveys, where members self-report their interests. “But if you want explicit data—to find out what they’re really interested in,” you can combine data from each member’s event participation with data from their e-newsletter usage and click-through actions, she says. “You get a really interesting story about what members actually care about.”
The data an association collects should be relatively clean and easily available, notes Golden. “Bad data ‘in’ is not going to be helpful.” For associations that host multiple data repositories—an AMS, an LMS, a separate registration database—it can be challenging to know where the “cleanest” data is housed. Golden suggests leveraging technologies that work together across systems to standardize data. She also recommends that staff input information across all systems in a consistent manner, using a “data dictionary” to gather and input distinct information in a standardized format.
Some associations employ on-staff researchers. At ASSP, “we have an amazing researcher that always looks at the questions we ask, why we ask them, and how words matter in design,” McNelly says.
With or without dedicated research staff, associations should infuse the data-driven mindset among all employees. At a small association like AIIM, data-driven decision making is pervasive, says Liu. “We set an expectation that we use and reference data when making decisions and that staff are data literate,” she says. “We’ve also built data management into staff job descriptions. There’s not one person who’s solely responsible for data quality—it’s spread across several positions.”
Prepare for a Data-Dense Future
Expect data to evolve as more associations embrace artificial intelligence for various tasks. “AI is revolutionizing data collection by automating processes, analyzing trends in real-time, and providing predictive analytics,” says McNelly. “AI is also a tool to help pull out themes from large sums of data.”
The emergence of AI elevates the importance of clean data, says Liu, especially for generative AI. This is particularly critical as associations organize some of their data to monetize it: “Associations should be thinking about the quality of their data and content, especially its metadata, to ensure quality and traceable inputs for AI.”
As you gather and leverage increasing amounts of data at your own organization, remember that data is a tool: “It is not good or bad, and it is not an answer in itself,” McNelly advises. “Creating a culture that values data alongside experience and intuition can lead to more balanced, effective decision making.”
