Keep in mind, the definition of data literacy was about assessment of data presentations, not about data expertise: “Data literacy is the ability to extract meaning and insights from data. Carlie J. Idoine, a research director at Gartner, wrote, “If data and analytics leaders simply provide access to data and tools alone, self-service initiatives often don’t work out well. For example, a common mistake I see is when companies publish research on year-over-year performance and ignore that the mix of respondents is different each year, which can dangerously skew results. Visualization Finally, take some time to brush up on your data visualizations skills. Understanding how to visualize data – to influence decision-making internally as well as to present information for an external audience – is a mission-critical skill for marketers. Jamie Stober, Airbnb data scientist, explains the power of Data University: “Post-training, employees on these teams built their own dashboards and developed localized solutions using data, which (the team in) data science never would have had the bandwidth to create. Participants in the program felt empowered to explore data on their own and use data tools to start measuring their work, which increased their impact and scale.” The program’s benefits accrue not just for those who learn and use their new skills, it also frees time for in-demand employees. As Jamie writes, “When business partners can answer their own questions using basic SQL queries and dashboards, it frees up significant time for data scientists to work on higher impact projects which are crucial for the strategy and direction of their partner teams.” Data education if you can’t attend a data university Not every organization can build a data university, of course. I highly recommend Naked Statistics as a starting point. If you’re interested in more than the basics, Scott Berinato (editor at Harvard Business Review) published an excellent guide called Good Charts.
Over the last decade, organizations have invested heavily in marketing analytics engines – from predictive analytics to performance management and data visualization tools. These tools help users surface insights from massive quantities of data, but perhaps more compelling, they bill themselves as “self-service” or accessible to marketers who may not have advanced quantitative skills.
Are those who are not data scientists or analysts equipped to make sense of the output from these powerful engines? Plenty of research points to the contrary.
Mynewsdesk surveyed 1,050 marketers, PR professionals, and business owners. (Disclosure: My company helped design the survey.) The results were eye-opening.
Just 18% of the respondents rated themselves as having a high degree of data literacy. (The average for marketers was only 19% — not significantly better.) Keep in mind, the definition of data literacy was about assessment of data presentations, not about data expertise: “Data literacy is the ability to extract meaning and insights from data. A person who is data literate is comfortable interpreting data graphics, analyzing and critiquing data presentations, and recognizing when data is being used to mislead.”
To reiterate, just one in five marketers say they’re comfortable interpreting data graphics, critiquing data presentations, and understanding when data is being used to deceive. These are numbers that need to be fixed.
Asked what prevented them from becoming more data-driven, respondents cited time (and it’s what always stands in the way). Lack of time ranked highest followed by lack of skills and budget. And it’s important to read between the lines what’s not being said. Most response options didn’t even merit half the votes, which amounts to a big fat “meh.” We can safely say becoming more data-driven simply isn’t a priority for many.
What this study shows is that while the availability and power of analytics tools are leaping forward, the teams deploying them may not have the skills to interpret the data-rich reports and visualizations produced. Not only are companies failing to extract full value from their data investments, in some cases they’re being led astray by misinterpreting findings.
Gartner predicted this growing gap – an explosion of analytics tools and a dearth of people who could use them proficiently – in early 2018. Carlie J. Idoine, a research director at Gartner, wrote, “If data and analytics leaders simply provide access to data and tools alone, self-service initiatives often don’t work out well. This is because the experience and skills of business users vary widely within individual organizations … Training, support, and onboarding processes are needed to help most self-service users produce meaningful output.”
What you need to know about data literacy
What can you and your brand do to address the issue? First, let’s unpack what I mean by data literacy as it applies to general marketing. I group marketing data literacy in three categories:
- Understanding basic statistics
- Knowing how to interrogate findings
- Visualizing data for transformation
Statistics
If you don’t feel confident about your math skills, I bet your challenge is statistics. As non-quant marketers we aren’t often called on to use college-level mathematics except when asked to do basic statistics. (God help me if someone were to call on my calculus skills.) Understanding statistics means getting the concepts and understanding how to pick apart research or statistics used at best incorrectly or at worst to deceive.
Interrogation
As important as it is to understand basic statistics (and how some people use statistics to elude the truth), it’s also critical to learn how to interrogate findings – how to poke holes in the data. All your fancy dashboards and summary reports do not arrive 100% complete.
You need to question what you see. Does it make sense? Is there a hidden factor influencing performance? What else could explain the change? What should you be tracking? Is something too good to be true?
Learn to patiently and methodically interrogate those beautiful reports produced…
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