BI & Blondies

Data Literacy: Power BI Lessons of Adoption #2

In the journey to Power BI success, we need to talk about Data Literacy.
In my previous blog post, I was underlining the importance of training everyone on the consummation of reports. Besides this technical approach, we need to think bigger and considerate the “Data Literacy” level of the people in the company. 

Data literacy is the ability to read, work with, analyze, and argue with data. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data.”  (Wikipedia)

How to give people the Power if they are not prepared to read the books you are giving them?
And by reading the book, I mean to understand a visual on a report, an indicator, and to be able to use it in their own stories.

What if they are Data Illiterate?

A legacy of the traditional approach to data usage persists in many organizations: a small group of specialists benefiting from most of the investment in data tools and training, along with ubiquitous access to data. As a result, many employees don’t have the skills that would help them comfortably and confidently work with data. Just 21 percent of the global workforce are fully confident in their data literacy skills — i.e., their ability to read, understand, question, and work with data. (source:

What should be done?
We can’t complain about the persistence of Excel as the unique tool and the repeated usage of unreadable exhausting worksheets, bad pie charts in the company if there is no road for people to escalate the data mountain. And no map to drive them there. And no clear understanding of the Power of data. Insight should lead to action. 
Concretely, you should have a product-agnostic training to build the following competencies:

  • Understand what analytics is and how it is applied in real-world situations
  • Gain an understanding of data: learn about different types and attributes of data
  • Explain what Data-informed Decision Making: Explore how organizations can turn data into value
  • Understand various types of analytics available to decision-makers
  • Identify use cases where one or more of these techniques can be applied
  • Learn what data and analytical aggregations are
    • specific aggregation types like mean and median
  • What distributions are
    • types and characteristics of distributions
  • Gain an understanding of signal and noise
  • Understand what correlation and causation are
  • Understand the role of point and interval estimates
  • Discover the value of confidence intervals
  • More analytical concepts.

Maybe are you now thinking that every educated people with a degree should have been taught about that. Maybe they were, and due to lack of practice, they simply forgot it.
You can propose it in 1-day training, more like a sensibilization, an update on their knowledge. Or even better, include it into a Bootcamp where you train your future data-knights and their shiny armors with a real-life scenario.
What ever educational approach you choose, at some point, you need to fill the gap in Data Literacy, and the soon the better.

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