When you buy a tool you have to learn how to use it, or you’ve wasted your money. Our team understood this when we implemented a new CRM system: If frontline staff used it and used it well, the investment would deliver on the promise of facilitating advancement of the mission.
Data is also a tool. Managers and decision-makers will succeed if they know how to use data. The question is, what have we done to maximize on that investment?
During our CRM implementation, we had more than 50 working sessions with frontline staff – focus groups and training sessions that involved nearly everyone in configuring the software and applying it in their work. So many hours!
CRM was big, but our investment in data, spread over years, is much bigger. Like other advancement shops we have staff employed in the collection, creation, and management of data, staff who design and maintain the infrastructure for securely storing, assembling, and preparing the data, and staff who use the data to develop reports and business insights.
That investment far exceeds the cost of any CRM, yet has it been matched with an equivalent degree of training in its end-use by managers and decision-makers? For us and many other organizations, the answer is no.
Operations can get very good at translating between the data and the business, but staff across Advancement must be able to speak the language. Author and advisor Bernard Marr says, “… organizations that fail to boost the data literacy of their employees will be left behind because they are not able to fully use the vital business resource of data to their business advantage.” (1)
Organizations large and small, in every sector, are coming to this realization. A 2019 Gartner survey found 80 percent of organizations now plan to start developing staff competency in data literacy. (2)
Data literacy simply means the ability to understand data in the context of one’s business knowledge. It includes knowing where the data comes from, how it’s defined, the methods used to analyze it, and having a view to applying it to achieve an outcome.
You don’t need to be a mechanic to drive a car. You don’t need to be an analyst to make decisions with data. The next big leap forward in data-informed decision making might lie in helping more and more staff across the organization learn how to drive.