Spring Cleaning for Your Data: A Strategic Approach for Associations

by Gwen Garrison, PhD, High Sierra Insights 

As we continue this month’s theme of spring cleaning, let’s talk about tidying up—not with brooms and dustpans, but by refreshing and refining our organizational data. How can we clean up our data in ways that go beyond the usual fixes?

At its core, data cleaning is one of the most important activities we can undertake to enhance the value of our data and improve cross-operational usage within an association. Clean, well-maintained data allows us to streamline automated integrations, tell more compelling stories through business intelligence platforms, and foster a stronger data-driven culture for better decision-making.

As a first step, many associations start their data cleaning efforts with the obvious—updating member and vendor contact information. This is a highly useful exercise and technological tools like address verification services certainly help to make the process relatively simple. 

But I want to propose two broader, more strategic approaches to data cleaning that involve two things that are at the heart of Strategico work: people and processes.
 
Strategic Data Cleaning Phase 1: Identifying What’s Truly Valuable

Before diving into data cleaning, association leaders and managers must determine which data points are truly valuable and understand their role in value creation. Let me share a story to illustrate this.

When I led a unit in an association, I managed five major data systems. One of these systems housed hundreds of data points—from content details supplied by local partners to time-stamped changes. 

After collaborating with internal and external stakeholders, we identified 10 critical data points that required absolute accuracy. These data points were essential for local and regional certification processes, meaning they needed to be 100% correct.

To achieve this, we collaborated with IT to develop reports that allowed us to verify these data points daily if needed. Once that was in place, we shifted our focus to the next set of variables, evaluating the required level of accuracy. 

We determined that this second group of data points needed to be 90% accurate. Pushing beyond this threshold would have required excessive manual investigations, with little return on investment (ROI). After careful discussion, stakeholders agreed that a small margin of ambiguity (about 5%) was acceptable given the effort required to reach perfection.

This approach allowed us to focus our resources where they mattered most, ensuring that the data supporting critical decisions was as accurate as possible without wasting effort on diminishing returns.

Strategic Data Cleaning Phase 2: Improving Data Quality from Entry to Reporting

Next, we needed to look at data variables that we considered valuable but problematic. Many of these 20 variables related to key member characteristics, yet the data was often incomplete, incorrect, or conflicting across different systems.

To tackle this, we first asked: What is the business need for this data?

Our education and events teams, for example, relied on member data to expand key resources. Missing or inaccurate data hindered their ability to conduct research and develop effective programming. Addressing these issues required a team-wide effort.
We took a systematic approach:

• We traced the data journey from input to transformation to reporting.
• We focused on improving data capture by standardizing and validating entry points.
• We set incremental improvement targets rather than expecting perfection overnight.

At every step, we asked: Is our ROI aligned with our effort?

For some data points, we achieved 85% completeness—short of perfection but still tremendously valuable. This level of improvement unlocked new opportunities for education and event programming, demonstrating that even modest data quality improvements can have a significant impact.

The Takeaway:

Data Cleaning is About People and Processes While IT can implement fixes, data cleaning is ultimately about people and processes. Associations must collectively decide which data is truly valuable and take a step-by-step approach to improving quality at every stage.
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Let’s resist the urge to say that everything is valuable—because if everything is valuable, then nothing truly is. Instead, by strategically prioritizing data quality efforts, we can maximize impact while making the most of our time and resources.