Planning an AMS Implementation? Start With Your Data

Start With the Data, Not the Vendor

If you’re planning an AMS implementation, the natural instinct is to start by comparing vendors or drafting requirements. That feels like progress. In reality, some of the most important work happens well before those steps.

If I were directing strategy for an association about to invest in a new AMS, I would start with the data.

Many associations do not fully realize how critical their data is until they are deep into implementation. Over time, teams build workarounds in their existing systems to compensate for gaps, inconsistencies, or limitations. Those workarounds may function day to day, but they rarely survive a migration into a new platform. Unfortunately, the consequences often surface late in the project, sometimes right before go-live.

Treat Data Readiness as Its Own Project

A more effective approach is to treat data readiness as a project of its own, not a background task folded into implementation.

Ideally, data cleanup begins a year or more before implementation. That runway allows organizations to clean, de-duplicate, and archive records thoughtfully rather than reactively. It also provides time to document new data rules, identify missing fields or structures, and help staff adjust to better data habits before a new system is introduced.

The goal is not simply to move data from one platform to another. The goal is to avoid carrying old problems into a new environment and instead build something more durable from the ground up.

Why Data Cleanup Cannot Be Fully Outsourced

Data cleanup is often misunderstood as a purely technical task. Many association leaders assume that hiring a data consultant allows internal teams to remain hands-off. In practice, the opposite tends to be true.

Even the most experienced consultants need deep input from the people who work with the data every day. Staff understand context that no external partner can infer. They know why a member marked as “Active” is not actually active, why certain IDs live in unexpected places, or which of several duplicate records is the correct one.

I have seen this play out firsthand. In one implementation, a client shared that they had hired a third party to clean their data while also asking us to scope cleanup services. Ultimately, neither effort moved forward. After go-live, we spent weeks responding to reported “bugs” that were ultimately traced back to data issues, including failed renewals and broken workflows caused by missing or inconsistent records.

A strong data cleanup effort can be led by a consultant, but it still requires meaningful staff participation. Internal teams are essential for answering questions about context and meaning, defining new data policies, and identifying edge cases that only come from lived experience.

Clean Data Is a Strategic Advantage

Clean data is not just a technical win. It is a strategic one.

With well-structured, reliable data, associations can report with confidence, identify patterns in member behavior, launch targeted outreach that reaches the right people at the right time, and track impact over time. Decision-making becomes faster and more grounded, relying less on anecdote and instinct and more on clear signals from the system.

Too often, organizations accept limited insight as inevitable because their data cannot tell a coherent story. Investing in data quality, whether before, during, or after implementation, changes that dynamic.

Data is not solely IT’s responsibility. It is a shared organizational asset. When associations invest in it deliberately, they set themselves up not only for a smoother implementation, but for long-term operational and strategic success.