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4 Key Tips for Building a Data-Literate Workforce 4 Key Tips for Building a Data-Literate Workforce
Editor’s note: Dominic Bohan is a speaker for ODSC APAC this August 22-23. Be sure to check out his talk, “Building... 4 Key Tips for Building a Data-Literate Workforce

Editor’s note: Dominic Bohan is a speaker for ODSC APAC this August 22-23. Be sure to check out his talk, “Building a Data-Driven Workforce,” there!

Just 21% of employees feel confident in their data literacy skills according to a Gartner survey. We’ve all worked with these “non-data people.” Many folks even feel confused or scared by data. Our “non-data” colleagues are an untapped goldmine of knowledge and experience that can help us solve problems with data. We just need to build up their confidence and understanding of data, in other words, to teach them data literacy. 

We’ll show you how to build data literacy skills based on our experience training over 21,000 people and partnering with some of the world’s largest companies to build Data Literacy Programs. You can use the techniques we’ll share to coach and mentor your stakeholders and to develop data literacy programs at your organization. The 4 Key Tips we’ll cover are:

1. Teach with stories

Behind every data problem is a story about humans. For example, behind a model that predicts employee attrition is a story of a company losing its top talent. If we dig deeper, we uncover more stories, from people disillusioned with their daily work, to irresistible salaries being offered by our competitors.

We’ll show you how we’ve weaved real, compelling stories into data literacy education so our “non-data people” are inspired to share their ideas and expertise and learn more about data.

2. Start with the 3Cs

Data Literacy expert Jordan Morrow recommends starting data literacy education with the 3Cs – Curiosity, Creativity, and Critical Thinking. We’ll show examples of how we’ve taught the 3Cs and used them to get people thinking about what’s possible with data, and how data analytics could be applied to their area of business.

In-Person and Virtual Conference

September 5th to 6th, 2024 – London

Featuring 200 hours of content, 90 thought leaders and experts, and 40+ workshops and training sessions, Europe 2024 will keep you up-to-date with the latest topics and tools in everything from machine learning to generative AI and more.

3. Make it practical

To learn effectively, we must practice and apply what we’re learning to our day-to-day work. We’ll share examples of custom workshops and ongoing project work we’ve designed that get people practicing data skills and feeling a sense of accomplishment quickly.

4. Mentorship

Training is great, but it doesn’t stick without ongoing support and mentorship. We’ll show you how to connect people who want to learn more about data with data experts in the business and how this has led to new ideas and use cases we hadn’t even imagined.

We’ll also share some common pitfalls that we ourselves fell into, so that you can avoid them, namely:

  • A one-size fits all approach where we don’t tailor data literacy education to learners’ needs.
  • Too much math
  • Too much jargon

Finally, we’ll share success stories from our partners that have leveled up the data literacy of their workforce. Not only have they unlocked massive amounts of expertise, but they’ve also been able to attract and retain more top talent by investing in data literacy, and they relieved their data experts of many tedious requests because more people can do basic data analysis themselves.

About the author/ODSC APAC speaker:

A TEDx speaker, Dom brings a wealth of data storytelling experience to StoryIQ from his career at QBE, one of Australia’s largest insurance companies. At QBE, he was a senior leader in data analytics and business improvement, presenting data-driven strategy recommendations to the company’s senior executives and producing reports for the Group Board of Directors.

ODSC Community

The Open Data Science community is passionate and diverse, and we always welcome contributions from data science professionals! All of the articles under this profile are from our community, with individual authors mentioned in the text itself.

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