SaaS Metrics Refresher #8: Data Literacy

It’s more important than ever to ensure that your whole team is well equipped to work with metrics and data.

Just like people don’t need an advanced English degree to be literate, your employees don’t need advanced statistical knowledge and programming skills in Python or R to be data literate. Reading and writing skill levels are often defined by what people can or can’t accomplish in their everyday life—we must do the same for data literacy.

Brent Dykes, Forbes

Definition: what is data literacy?

Simply put, data literacy is the ability to read, understand, create and communicate data as information. Just in the same way that literacy denotes our ability to work with and derive information from the written word, data literacy is all about our ability to work effectively with data.

Margaret Rouse of breaks this down into several key skills:

  • Knowing what data is appropriate to use for a particular purpose.
  • Interpreting data visualizations, such as graphs and charts.
  • Thinking critically about information yielded by data analysis.
  • Understanding data analytics tools and methods and when and where to use them.
  • Recognizing when data is being misrepresented or used misleadingly.
  • Communicating information about data to people lacking data literacy, an ability sometimes referred to as data storytelling.

Key data literacy terms

  • Qualitative data is descriptive – it describes something, e.g. Reason for customer cancellation.
  • Quantitative data is always numerical (involves numbers), e.g. Revenue lost from customer cancellations.
  • Discrete data can only take certain values (like whole numbers), e.g. Number of customers churned.
  • Continuous data can take any value, within a given range, e.g. Customer churn rate.
  • Categorical data can be sorted, according to defined groups or categories, e.g. Industry vertical.
  • Statistical significance is when the observed outcome of an experiment is unlikely to have occurred due to chance. This is an important factor when running multi-variant (A/B) tests on your product or website.

How to question data

From The Ultimate Data Literacy Cheat Sheet:

Aspects of data

Why is data literacy important today?

As I explained in my post the data literacy shortfall:

For years, modern businesses have used data and metrics in almost every decision they make. If you want to argue for more budget for your project, you’d better have some data to back up your reasoning.

But more recently, the use of data shifted. Technology companies started to implement Machine Learning and AI algorithms in tech products, to find highly optimized solutions to big problems humans weren’t so good at. Machine Learning algorithms are incredibly hungry for data — they require training on huge data sets to be effective, and there’s really no limit to what they can consume. The more data, the better. At this limitless scale, and with this technology, you can see why people are hailing data as the “new currency”.

For professionals, there’s no escaping the numbers and statistics present in our working day, regardless of your role — Marketers, Salespeople, Engineers, etc. — these people all use data to make decisions and do their everyday work.

So we are all surrounded by data. But are we equipped with the skills and knowledge required to make sense of it? Do the people using such data for storytelling have adequate skills for effectively communicating and visualizing data for their audience? One could argue that these are all critical skills in business today.

“Data literacy is built upon data democratization and the user experience. If a platform is difficult, it won’t be used. If a platform doesn’t serve all teams equally well, it will not be adopted across the organization. If a platform isn’t architected to bridge the gap between regular people and the data scientists creating the algorithms, then the era of data-driven anything will fail to materialize.”

H.O. Maycotte, Forbes

Steps you can take to improve data literacy in your company

1. Start treating data literacy as a core work competency

Core competencies need deliberate training and embedding within a company. They also need to be highlighted in job specifications, probed in job interviews and included in employee objectives. Adoption of data literacy within your business will not happen without a deliberate approach.

2. Empower employees with the right tools

Fortunately, there’s a never been a better time for supporting data literacy with accessible, affordable tools. Check out The data literacy shortfall where I mention some recommendations.

3. Democratize your company’s data and insights

A company with democratized data is a company where anyone can discover insights that help them make decisions — but getting to this point involves more than just software tools. Map out your “road to data democratization” and figure out how to put valuable data at the fingertips of your entire team.

Resources and Further Reading


The Ultimate Data Literacy Cheat Sheet [PDF] (ChartMogul)


The data literacy shortfall: Are we data-driven, or data-duped? (ChartMogul)

Data literacy : A critical  skill for the 21st century (Tableau)

Data Literacy — What It Is And Why None of Us Have It (Forbes)


Data Literacy Report [PDF] (Qlik)

Ed Shelley

Former Director of Content


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