AMS come with different features and functions to support your membership work. There are two features that really set the next generation of AMS from the previous generation: built-in business analytics and support for artificial intelligence. But as we make these digital transformation moves, there is one aspect that is vital but currently overlooked: data literacy.

So, how does data literacy connect to AMS? To answer this question, let's start with what data literacy is. The thought leaders at OpenDataSoft define data literacy as "the ability to read, understand, work with, analyze and communicate with data, turning it into meaningful, relevant information." Moreover, data literacy needs both practice and support to get to the point where associations can derive data-driven insights that matter for their association.

Unpacking the definition, we need internal stakeholders, both staff and executive decision-makers, to have the ability to read not just data in tables but data presented in visualizations. 
Thus, two areas to consider to build data literacy are: 

1. Do associations have the staff who can correctly analyze and create accurate visualizations? And, 
2. Do associations have decision-makers who can accurately interpret the visualizations? 

Just because an association’s AMS has analytics capabilities doesn't mean there is the capability to use the feature well and gain the data-driven-decision benefits that are at the core of these features. 

So, where do associations begin to build your data literacy? I suggest two areas: replicate a previous well-respected report and then move on to something small and build by adding more data complexity from other systems (e.g., events, education, or sales). 

By taking these steps the internal stakeholders build trust and learn together to read data information accurately and well. Additionally, it allows associations to identify the areas where they need to improve the data 'conveyor belt'. All business analytics work needs to attend to data collection, quality, multiple system connections, and appropriate use of transformations to be able to analyze and display visualizations.

Finally, once staff and decision-makers are well grounded in these early data literacy steps, they can begin to move into routine automations followed by artificial intelligence. AI shows a lot of promise for operational efficiency and needs time to grow on an association’s own data to provide benefits that are more targeted and personalized for your members. 

Make data literacy key to drive better AMS selection and use. Hope your AMS FEST is insightful and successful.​