How to BE DATA LITERATE with Jordan Morrow

Listen to this episode on Anchor FM

Do you have the ability to consume and use data effectively? In this episode of the DATAcated on Air podcast, host Kate Strachnyi talks with Jordan Morrow, the Head of Beta Design and Management Skills at Pluralsight. Jordan was one of the founding pioneers in data literacy and is known as the godfather of data literacy. Listen to learn from his experiences and how you can develop your data skills.

You will want to hear this episode if you are interested in...

  • Who is Jordan? [04:01]
  • What is data literacy [06:36]
  • Everyone can be data literate [11:49]
  • Determining data literacy [14:31]
  • Bridging the skills gap [18:08]
  • The four levels of data analytics [20:45]
  • Be Data Literate [24:07]
  • Data analytics education [30:15]
  • Finding your next step in data literacy [38:20]

Defining data literacy

Data literacy is the ability to read, work with, analyze, and communicate with data. People need to be able to effectively consume and use data, whether for their personal life or career. Data literacy allows organizations to have strong adoption of data and analytics. While data work is technical for a small group of people, most simply need comfort utilizing data. Data literacy provides people with the skills necessary to have confidence in consuming data effectively.

Everybody is data literate to some extent. Day-to-day things like calculating fuel for a car or determining if an umbrella is needed are reading data. However, in a business context, people become intimidated. Some of that comes down to the biggest roadblock to data analytics success: the cultural aspects of data literacy. At the same time, many people lack confidence in their data literacy. It’s important to remember that this is a progressive journey to become better at the four levels of analytics. Instead of just relying on visualization, maybe digging into coding or other things will help with effective communication and interpretation.

Using data effectively

The top skills desired for most organizations, especially since the pandemic, are data-related such as AI, ML, data analysis, and visualization. Organizations are full-force trying to use data, so data skills make people more marketable. Using data doesn’t mean getting rid of the human experience. On the contrary, utilizing data is a combination of technology and humans. That blend is why data literacy looks so different for each organization.

A starting point to determine data literacy is whether or not a company has a data and analytical strategy that ties to its overall organizational strategy. Before discussing data literacy, a company needs to determine its overall goal with data. Then it can consider data tools and how clean, governed, and managed the data is. Finally, the organization can create a plan to help accomplish the organization’s goals. These are foundational blocks to building data literacy. The key is having a strategy and learning what is necessary to achieve goals.

Technology and data

Innovative tools are bridging the gap to becoming data literate. A chart that used to take three hours to build can take AI minutes, giving people more time to analyze data. Technology is a bridge to other levels of analytics. A visualization only helps describe things. The next level would be diagnostics, which is not just what has happened but why it happened. Understanding cause and effect is what drives business decisions. One key aspect of data literacy is this data-informed decision-making. It brings data to the forefront and balances it with other factors to make a decision. Tools enhance that process, allowing people to spend more time on work driven with that data.

Data storytelling is one of the fastest-growing jobs in data right now. Coding and data visualizations are necessary, but more important are the non-technical skills such as interpreting data and communicating insights with data. A data strategy needs to be a business strategy. Organizations need people who can bridge the technical side to the business side. Finding the diagnostic analytics and making decisions is more of a soft skill than a technical one.

Resources & People Mentioned

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