09 May 2025 How Associations Can Use Data Analytics for More Confident Decision Making
Large companies and enterprise-level corporations have been using data tomake more solid business decisions for years. Now even small nonprofits and associations can employ data analytics for the same confidence with their organizational choices. Read on to learn how.
What Is Data-Driven Decision Making?
In the past, association decisions were usually made using intuition, guessing, and human memory. However, today we have hard data to guide decision making. This information includes:
● Member feedback
● Industry trends
● Financial performance
● Competitor intelligence
Not only do we have these pieces of data saved online for easy access, we have AI (artificial intelligence) and machine learning to perform analytics, saving organizations hours of time and manpower. This analysis — aka data-driven decision making or DDDM — removes fear and bias, replacing it with objective assessments that provide many advantages.
What Are the Benefits of DDDM for Associations?
For associations and nonprofits, data-driven decision making is particularly valuable, as typically staffing and budgets are lean. Also, these types of organizations don’t have the ability to test and experiment with different choices the way big businesses do.
Other benefits of DDDD include:
● Improved member and volunteer satisfaction and retention
● More successful fundraising and donor drives
● More confident long-term strategic planning
● Proactive approaches to economic and other trends
● Better inventory, supply, and logistics management
● Smoother operational efficiency and staffing
● Increased opportunities for growth and revenue
Data analytics transform raw data into trends, patterns, and similar information that you can use to make decisions for your organization. For example, you might see that over the last decade, associations in your industry are evolving to gain more members in January, whereas they used to increase their ranks during summer months.
This takes the data from a static state to a dynamic one, illuminating changing member behaviors. Let’s look at some best practices for data analytics and association decision making next.
Best Practices for Nonprofit and Association Data Analysis
Start with a Goal in Mind
First, you must agree on the reasons why you’re employing data analysis.
Otherwise, it’s too easy to get bogged down in tons of extraneous data and not
get the results you need.
Are you trying to increase donations or membership? Or is your association
looking ahead to the future to make changes in its offerings or leadership?
Set a Budget for Using Data Analysis Tools
What is your group’s budget for this process? While many large companies have
their own proprietary software or custom installations, that’s not practical for most
nonprofits and associations. However, you’ll find affordable solutions in cloud-
based and subscription platforms. These should integrate easily with your
existing programs, like your CRM or accounting software.
Understand the Different Types of Data Analysis
There are four basic types of data analysis for different needs:
1. Descriptive analytics – this answers the question “What happened?” or “What is currently happening now?”
2. Diagnostic analytics – this tells you why something happened, e.g., membership dipped because of a recession.
3. Predictive analytics – this helps you make decisions for the future based on what is most likely to happen.
4. Prescriptive analytics – this answers the question, “What do we do next?” and is tied closely to predictive analytics.
There are also four newer kinds of data analysis:
1. Text analytics that evaluate the subtleties of language
2. Spatial analytics that deal with geographic locations
3. Cluster analysis that uncovers hidden patterns in data sets
4. Social network analysis that examine personal connections and interactions
You can probably already see how different types of analytics would apply to your unique organizational needs. For instance, social network analysis may be ideal for finding out why members are drawn to certain thought leaders or competitor associations.
Collect and Cleanse Data First
You must have a certain amount of data before you can perform an analysis of it. Most likely, you have a repository at your fingertips with your membership files, event records, and the like.
Before working with the data, it should be cleaned for errors, inaccuracies, and irrelevant entries. This is different from removing outliers, which may still be important in the analyzing process.
Consider Hiring a Consultant to Assist in the Beginning
If you feel overwhelmed at the start, that’s normal. Many organizations hire a consultant (or obtain a volunteer) to get them going. The right expert can help you choose software, merge data from different sources, and derive meaningful analysis from your raw information.
Ready to dive into data-driven decision making but don’t know where to start? Jaffe Management can help with some functions or recommend resources for your special needs. Call us at 212-496-3155 or reach out at info@jaffemanagement.com to let us know how we can assist your association.