Because the velocity of data is increasing and will always increase, the need for data literacy is increasing and will always increase. This does not mean that to be a successful executive you have to become a data scientist - quite the contrary. It means that in order to be a successful executive, you need to understand how data is turned into action, be familiar with the methods of data science and data-scientific research, and be able to think strategically about how to use data to create value for your business.
You Need to Be Data Literate, Not Data Fluent
All other things being equal, there is a significant difference between being literate and being fluent. A literate executive will recognize the need for a desired outcome. For example, "We need to understand how these sales numbers are related to these categories." They might even know that a simple linear regression is the proper mathematical technique to accomplish the task. But most importantly, being data literate would enable this executive to ask the right questions and seek the right assistance to accomplish the goal.
Someone fluent in mathematics, statistics or data science would look at the problem quite differently. They would quickly and clearly understand that a simple linear regression was the appropriate technique to accomplish the task, but someone fluent would consider specific methodologies in ways that literate executives might not. For example, "Is the least squares approach sufficient to achieve the desired outcome for this linear regression or will a least absolute deviations approach yield a better outcome?"
To put it another way, you don't need to speak French to recognize that the email you just received is written in French. You just need to be literate to the point where you know that Google Translate is not going to get the job done and you need a highly skilled French translator to help you interpret and respond to the communication.
Data Literacy Basics
While there are countless ways to analyze data, there are three basic steps to the process of turning information into action:
- Transform: Processing, enrichment and aggregation
- Learn: Regression, clustering and classification
- Predict: Optimization and simulation
These steps are explained in detail in a previous article entitled, "What Do You Do with Data?" In practice, you don't need to know how to do any of this; you just need to know exactly what each step is and what it is for.
Get in the Game
Injecting yourself into the process is the first step. You should keep up with tweets that are hashtagged #datascience and #machinelearning. It's a great starting place and the volume of information is far from burdensome.
To stick with our literacy-versus-fluency metaphors: You don't need to know the specific plays in an NFL team's playbook; you just need to know that there is a playbook, that it's filled with plays and that the team knows all of them. That said, it really helps if you know the process by which a play is designed, learned, practiced and executed.
You probably already have the world's most ubiquitous data analysis tool at your disposal: Excel. There are thousands of "how to" videos on YouTube on absolutely every function Excel provides. This is a great weekend project or even a multiweek, one-hour-per-weekend ritual. Just because you are not attempting to become fluent in data science doesn't mean you shouldn't take some of the tools out for a spin. You don't need to remember any buttons you click or the formulas you use; you just need to remember what problems were solved.
Turning Literacy into Invincibility
Turning data literacy into invincibility will evolve from your understanding of how best to combine 1st-, 2nd- and 3rd-party data, data-scientific research and your business knowledge to turn information (data) into action. Actionable data is a primary driver of value creation and it helps increase competitive advantage. Think of it as the ultimate man-machine partnership. Once you know that specific data exists and you know what you want to do with it, everything else (within the limits of technology) is just management.
The interpretation of data is not something that humans can do alone. We need computers and mathematics to help us. No one is expecting you to become an expert data scientist, mathlete or computer geek in order to do your job. But make no mistake; this is not something you can "let other people do." Turning information into action is your job. So go for it (and get there first). The rewards will be well worth your time.
Want help? We offer a bunch of different training programs that may work for you or your company, including Executive Data Literacy Training Courses, Data-Driven Media Sales Training Courses and bespoke Data Activation Forums for the C-Suite.