Best practices to build data literacy into your Gen Z workforce

Data influencer Kirk Borne explains how to equip your incoming workforce with the skills to create business value from data.

Kirk Borne

Kirk Borne

Ph.D., Chief Science Officer at DataPrime.ai

This is a guest post by Kirk Borne, Ph.D., Chief Science Officer at DataPrime.ai, which is a data- and AI-driven jobs platform for the sector. Kirk is also a consultant, astrophysicist, data scientist, blogger, data literacy advocate and renowned speaker, and is one of the most recognized names in the industry.

 A survey of 1,100 data practitioners and business leaders reported that 84% of organizations consider data literacy to be a core business skill, agreeing with the statement that the inability of the workforce to use and analyze data effectively can hamper their business success. In addition, 36% said data literacy is crucial to future-proofing their business. Despite these findings, 78% of organizations say that harnessing the power of their data assets is a challenge, with many saying that they have no strategy in place to overcome these obstacles 

Another survey found that 75% of employees are not comfortable using data. One commentator on this report summarized the problem succinctly: “Lots of people are scared of data.” 

One more quote from another report quantifies the cost in this way: “Lacking digital skills is currently costing UK businesses a staggering 10 billion pounds in lost productivity each year.” That’s about 14 billion US dollars. 

So, what can organizations do about this? More specifically, what can they do to future-proof the long-term success, even the existence, of their business in an increasingly data-drenched world? 

Gen Z: a new workforce

 The response to these challenges does not solely depend on technology, and it certainly must go beyond doing “business as usual.” The solution will rely heavily on people – a new breed of talent that’s eager to innovate and is also tech savvy. Gen Z fills this role, and the cohort is entering the workforce now. 

Consequently, the critical next step is to build data literacy and data fluency in the workforce. So, what is data literacy? One concise definition states: “Data Literacy includes the ability to read, work with, analyze, and argue with data.” 

Data fluency, on the other hand, takes data literacy even further – to include the ability to hold a position in the business in which data access, understanding, preparation, modeling, testing, interpretation, communication, presentation and value creation are prerequisite skills. Data fluency is therefore essential for data managers, data analysts, data scientists, data engineers, machine learning and AI specialists, data journalists, data storytellers, data officers and other similar roles. 

Data fluency is important for many in the workforce in that it helps create business value from data. However, data literacy is imperative for everyone in the workforce in that it enables reading, working with, analyzing and communicating with data – influencing business value creation from data. If you want proof of this imperative, check those statistics in the reports quoted above again. 

The role of data awareness, relevance and discovery

Whether talking about data literacy or fluency, it should all start with the first step: data awareness, which is recognizing data when and where it appears. That includes spreadsheets, visualizations of charts (maps and graphs), images, video, audio, documents and any other forms of information, both digital and analog. 

The next step is data relevance, which is the recognition that information (data) is an integral part of our digitally disrupted and digitally transformed world. Just about everything is now driven by, informed by, inspired by, and/or generating data. That includes smartphone apps, e-commerce, medical devices, internet of things (smart homes, smart cities, etc.), cybersecurity, AI, chatbots, robotics, blockchain, drones, self-driving vehicles, image recognition, gesture recognition, 3-D (and 4-D) printing, blockchain, and extended realities – the list goes on! 

The next step beyond data relevance is data discovery. That includes detecting patterns (trends, groups, outliers, links, associations) in data, learning how to recognize when they occur again and learning the circumstances in which certain patterns appear – as either a cause or a consequence of some events or behaviors in the business. For true discovery, this also includes the ability to discover and recognize business data sets that reveal such events and behaviors. 

To develop data-literate workers, the organization itself must take specific actions. One of those actions is to create a culture of data democratization. This culture is most easily expressed in one simple statement: If you see something (in the data), say something!” (Post that on your webpage, in your coffee room, in the elevators, and on every interoffice memo.) 

These three steps in data career encouragement (data awareness, relevance, and discovery) make one a truly data-literate worker! It also makes one an immediately sought-after and hirable job candidate. 

The next step

To become a data-fluent worker, next comes data exploration, focused on exploring patterns in the data for insights that can bring value to the business. Particularly, the objective is to discover which patterns (descriptive, diagnostic, predictive, or causal/prescriptive) may deliver the best value to the organization at a particular time and place, in a specific context. 

The data-fluent worker can then go deeper with data science. This is accomplished through rules discovery, descriptive and predictive modeling, perhaps with some coding, and even producing mathematical representations of the discovered data patterns. All of this aids in value creation by interpreting the most insightful patterns in the business’ data. 

As I like to say to new data scientists: “Come for the data. Stay for the science!” 

A culture of experimentation

Finally, the data-fluent professional can engage in data exploitation, which is focused on implementing, deploying and operationalizing the data-inspired and data-informed products, services and innovations that produce business value and move the organization forward. 

For these data-fluent workers to truly help the business, the organization must take another specific action: create a culture of experimentation, which celebrates curiosity and the “fail fast to learn faster” scientific mindset. One organization expressed the culture of experimentation in this super-direct and simple statement: “Test or get fired!” 

Data-literate and data-fluent positions therefore span six dimensions: data awareness, relevance, discovery, exploration, (data) science and exploitation. As business leaders consider which (and how many) of these aptitudes are appropriate for different people in their workforce, one thing is clear: data understanding is the ultimate key to unlocking business value from data. The depth and breadth of each employee’s data understanding will influence the tasks and roles that are assigned to that individual as the organization holistically addresses the data literacy challenges mentioned at the beginning of this article. 

Our next article will address those data literacy and data fluency aptitudes in more depth. We will place a bright spotlight on one particularly important aptitude: data storytelling. Come back to learn how data storytelling might be a far more valuable business workforce asset than all the data technology that supports it.