What is Data Augmentation?

Data analytics provides a complete understanding of your customers so you can provide better, more relevant products and services. However, the value of analytics is limited without rich data on customer characteristics, interests, purchases, and behaviors.

Associations typically have lots of data in their business systems. From membership data in your Association Management System to registrant data in your Event Management System, your critical business systems are flush with data.

Yet, there are instances when data is not complete or would be more meaningful if combined with an external data source. This is where data augmentation can help.

Data Augmentation is the process of improving base data with information from internal and external sources.

How to Augment Data

So what’s the process for data augmentation?

  1. Identify gaps in your data. Look for missing demographic and psychographic information. For example, do you know the job titles of your members? Do you know your members’ educational history? What are your members interested in?
  2. All activities should support the mission of your association and data augmentation is no exception. Once you have identified missing data, prioritize gaps based on how that information would help advance the mission of your organization if you had it.
  3. Identify where you can get missing data. You may need to conduct a survey of members. You may need to purchase a third-party data set. See our list of free data sources.
  4. Evaluate data. You should evaluate potential external data sources for cost, completeness, and the level of complexity and effort for integration.
  5. Plan for time and resources to obtain data. Third-party data sources, like Hoovers, may require an investment. Be sure budget appropriately.
  6. Plan how you will collect the data. Make sure you have a plan for how to acquire the data. You should also evaluate the ROI of the data to ensure that you have a strong business case for collecting the data. Consider both the direct and indirect costs of surveying members and purchasing lists.
  7. Determine where data will be stored or appended. In some cases, it may make sense to add data to a field in your AMS or another system. There are also going to be instances where it doesn’t make sense to add data to the AMS. For example, U.S. Census data or prospect lists may not be appropriate to include in your system of record.

Before collecting data, it’s important to define how the data will be used and how (and if) it will be included in the data warehouse. This will help ensure the data is not only collected, but usable. Happy hunting!