How to Become Data Literate (and Why You Should)

Behind all disruption is the same driving force: big data. Companies like Tesla, Amazon and Netflix have built global businesses that have changed the way we drive, shop and watch television – all using data.

“Data should not merely support your business—it should play a strategic role and provide value as a driver of growth,” write Anthony Stevens and Louis Strauss in their book Chasing Digital: A Playbook for the New Economy (Wiley, 2018).

The challenge for businesses today is how to extract value from big data. The question to ask, write Steven and Strauss, is: “What insights from data will help differentiate your product or service or provide you with a competitive advantage?”

It means that understanding data is no longer just a technical skillset – it’s a necessary business skill too. As Anna Nicolaou writes in Fast Company, “data crunching is no longer just for nerds.”

What is data literacy?

“Being data literate means you are able to understand insights that are derived from data and can translate those finding into business decisions,” says Kevin Kong, a data analyst at real estate platform OpenAgent. “It is more around understanding insights rather than the ability to find insights in data.”

Data has never been easier to obtain, store and analyse and should underpin every decision made in a business, Kong says. “Companies need to start using data; otherwise, they will fall behind the pack.”

Data literacy means critically evaluating insights and considering them in a business context. You should always ask whether your results make sense, advises Kong.

Employees from a non-technical background can learn to navigate data using user-friendly visualisation platforms. “Some of these tools allow for more intuitive exploration,” says Kong. “A data warehouse is required as it serves to be a central repository of data and a single source of truth. Once a warehouse is set up an analytics platform like periscope can fit on top. Most people will then be able to easily build dashboards to gather insights for themselves with a bit of training.”

Making sense of big data

Raw data on its own is of little value. It’s the application of data analytics to identify patterns and gain insights that make data meaningful.

Data literacy allows us to gauge the limits of the data we’re analysing. “Insights can only be derived from the data collected, and data is typically not a perfect representation of what is happening, as it is impossible to capture every single data point,” says Kong. “It is important to understand the insights and put a critical lens on figures in order to avoid any erroneous decisions.”

Fortunately, data literacy is a skill that even the most non-technical of minds can learn. It’s incumbent upon organisations to “provide training programs to help develop technical skills for the team,” says Kong. “Understanding how to use Excel can go a long way in understanding how data works and how insights are developed.”

Data literacy is a top priority at OpenAgent. “The majority of the people who work at OpenAgent are already data literate, and the people who aren’t can easily ask the person beside them for help,” says Kong. “We focus on making sure data is at the centre of any decisions we make. We have provided training courses for data tools, which helps upskill people who are unfamiliar with gathering insights from data.”

Data literacy tools and resources

Big data is Mark Moloney’s bread and butter. Moloney, the Machine Learning and AI Solutions Principal at Telstra, uses programming languages on a daily basis.

Among the most versatile is Scala. “Apache Spark is the most popular distributed data processing framework, and Spark is written in Scala,” he says. “The language involves less typing to get the job done while retaining all the libraries and integration options that come from its ability to closely interoperate with Java.”

Python is key for machine learning, while mastery of JavaScript – or transpilers such as Typescript and Elm – remains important to build end-to-end systems with a front-end.

Today, developers must be multilingual, says Moloney. “It might have been the case once that a developer could pick a language, say C# or Java, and within that ecosystem fulfil their role as a software developer. A polyglot approach is required today.”

A developer might use JavaScript for web apps, Swift or Android (Java) for native mobile apps, Java or Scala for back-end services, and Python for deep learning, he says. “From a craftsmanship perspective, there is value in learning multiple languages. My path to learning functional languages such as Scala was made easier from experience first applying functional techniques in JavaScript apps.”

Moloney recommends software development hub GitHub for anyone interested in practising their programming skills. “GitHub side projects allow me to experiment and try new languages,” he says. “Each new language you learn gets easier because more learning is transferred from previous languages.”

Moloney also recommends the Functional Programming Principles in Scala course offered by online learning platform Coursera. It’s “an excellent introduction to Scala and functional programming in general,” he says.