Data literacy is creating a comfort in people so they are able to utilize data. For some people that may look like becoming a full-fledged machine learning engineer or data scientist. But, for many others, creating comfort just means learning to interpret data, ask questions of data, and communicate effectively with those who specialize in data science and who are technically sound. It’s just about creating comfort and confidence in utilizing data, and it doesn’t mean everyone has to become super technical or be something they are not.
Historically, businesses have always seen the technical people as the ones that need to utilize and understand data, and while self-service analytics and business intelligence tools streamlined data democratization, most people don’t have a technical background in data, so they are trained on how to use the tool, but they still lack training on how to properly understand, interpret, and utilize the data itself these tools give them access to. We need training on the tools and technology, and we need training on the data and analytics.
Being data literate does not mean you have to become a full-fledged data practitioner. You just need to be comfortable interacting with data in a meaningful way so you can understand, interpret, and utilize it.
When it comes to data, everybody has a seat at the table, no matter what their background or role is, because everyone has personal experience that can be applied to the data.
Successful data literacy programs educate executives, secure their buy-in, benchmark throughout all stages of the program, and continuously iterate.
About Jordan Morrow
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