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Analysis:
Analysis
09 Jun 2020
10 minute read

Why Data Literacy is a key ingredient to success in the age of data and analytics

And how to unlock its value: the essential roles of measurement and data culture

Organizations are amassing more data than ever, yet for most of them getting return on investment from and achieving real business adoption of their data remains an elusive quest.

Recent research shows that only 32 percent of leaders feel able to create measurable value from data and just 27 percent believe their data and analytics projects generate actionable insights. [1] Why are we still just scratching the surface of data’s full potential?

A growing body of evidence suggests that, contrary to common wisdom, the main challenges to unlocking the real value of data and analytics are related not to tools and technology, but to culture and people. [2] In particular, it turns out that most of us are not particularly good at interpreting and making sense of data: a mere 21 percent of the global workforce seems to be fully confident in their Data Literacy skills, only 25% of employees feel fully prepared to use data effectively when entering their current role, and just 24% of senior decision-makers pass standard Data Literacy tests. [1] Even for the Millennials, our digital natives, Forbes indicates that Data Literacy sits at only 22%. [3]

Why should you care?

Many of the articles in this space herald that ‘Data Literacy will be the key to future-proof your business’ – as if the impact is yet to show. The unsettling truth however, is that the consequences are already hitting the fan: according to the Gartner Annual Chief Data Officer Survey, poor Data Literacy is cited as the second-biggest internal roadblock to the success of and the ability to generate business value with data and analytics. [4] 

Why is widespread Data Literacy so key to reap the benefits of data and analytics investments? The answer is simple. Real value from data and analytics investments often only comes with scalability, repeatability and effective integration into daily decision-making. 

Data overload

This is where we move beyond the capabilities of a data office or data specialist team and become much more reliant on the Data Literacy and habits of the broader workforce. If no one outside the analytics department or data office understands what is being said, there can be no effective data or analytics-based decision-making.

Moreover, evidence shows that the current lack of Data Literacy amongst the workforce is associated with lower workers’ productivity. Indeed, many employees report feeling overwhelmed when being expected to work with data, which makes them procrastinate and evokes feelings of stress and anxiety. As a result, companies seem to lose an average of more than five working days per employee each year. [1] 

Furthermore, while people certainly recognize the potential benefits that new technologies such as AI and automation bring with them, they are also fearing that these technologies will make humans redundant. More than ever, people are expecting organizations to ensure that the adoption of new technologies benefits society as a whole by helping humans and machines work together. An important step towards achieving this goal is to train humans to understand data, the language spoken by these new technologies.

Investing in data skills

But here’s the good news: investing in Data Literacy seems to pay off, with research pointing to a direct connection between Data Literacy and firm performance, as measured by higher productivity, market value and profitability. [5] For instance, a study by the Data Literacy Project found that organizations with high Data Literacy levels appear to generate three to five percent greater market capitalization, or a dollar equivalent of $320-$534 million1 in enterprise value. [6]

It is clear from these figures that Data Literacy is an essential component of successful data and analytics transformations, and organizations seeking to get ROI from analytics should invest in improving widespread data skills.

1. This figure is computed on the basis of the average value in the study’s sample, equal to $10.7b.

In their efforts to launch and deliver Data Literacy training programs, many companies have failed to include measurement² as a key pillar. As we argue in one of our forthcoming articles, measurements should be the cornerstone of your Data Literacy program for three important reasons.

First, a baseline assessment of the literacy level of your employees allows you to identify key areas and workforce segments to prioritize. In fact, it helps you to personalize and target your Data Literacy program to various workforce segments, based on their particular Data Literacy profiles and levels. For instance, after conducting a baseline data and technology literacy assessment, a global insurance firm segmented their workforce into either discoverers, explorers, practitioners and ambassadors based on their level of Data Literacy. Depending on the cluster to which a leader was allocated, a set of specific and targeted recommendations was provided on how to deepen their expertise.

Eddy Debrulle, CHRO Ageas: “The results learned us that Data Analytics and AI were demonstrating momentum amongst our leaders, with high readiness scores and knowledge levels moving beyond fundamental awareness. At the same time, enthusiasm and self-reported knowledge about data and analytics didn’t fully translate into application levels. This served as an important wake-up call and helped us to target further investments”.

The same assessment can be repeated on a quarterly basis to monitor how various segments progress along their Data Literacy journey and allows companies to course-correct quickly when and where needed. 
Finally, (continuous) measurements themselves can trigger positive changes in behaviors and attitudes towards data among your employees. They show the workforce your company cares about Data Literacy and – if well-designed – can prompt reflection and trigger action in this area.

2. We use the term “measurement” interchangeably with the word “assessment” in this paper to refer to different types of measurement formats, such as surveys or more cognitive skill assessments, including scenario-based testing.

The results learned us that Data Analytics and AI were demonstrating momentum amongst our leaders, with high readiness scores and knowledge levels moving beyond fundamental awareness. At the same time, enthusiasm and self-reported knowledge about data and analytics didn’t fully translate into application levels. This served as an important wake-up call and helped us to target further investments.

Eddy Debrulle, CHRO Ageas

Some companies already invested quite heavily in Data Literacy training programs - but have been confronted with an unsettling (though not so counter-intuitive) truth: acquired data knowledge and skills do not automatically translate in on-the-job application, new ways of working or structured and impactful interactions between business and data specialist roles.

Indeed, investing in Data Literacy only does not guarantee that data will be most effectively used within an organization. Whereas teaching people to become data literate can definitely be a challenge, it’s far trickier to establish new mindsets and behaviors and embed data-driven decision-making into the company DNA. Hence, data literacy trainings should be embedded in a larger transformation effort to establish a data culture.

Data Culture start at the top and builds upon leaders who are willing to invest in Data Literacy and set expectations around the use of data in driving decisions. Interestingly, leaders not only seem to overestimate the Data Literacy levels of their employees, they also still tend to trust their gut feeling more than data-driven insights when making decisions. [1]

A change of mindset at the leadership level towards data and the value of the insights it can deliver needs to happen, else even a highly data literate workforce cannot be expected to deliver the full potential value from data. In fact, research has clearly pointed to the negative effect a leader can have on the ability to build a culture of data-driven decision-making, this by using “shame and blame” tactics that keep people from feeling trust to ask questions and take calculated risks that may benefit students. [7]

The message is clear. Just like the complex and homologous relationship between language (literacy) and culture in general - Data Culture and Data Literacy cannot be approached separately and the enrichment of either means the further development of the other.

Further reading

Data literacy is becoming an essential skill in a world in which decisions will be increasingly data-driven. To be prepared for this change, be sure not to not miss our forthcoming articles, in which we will explain how to build a robust Data Literacy program based on measurements and elaborate on the need to embed your Data Literacy programme in a broader journey focused on building a proper data culture.

Bibliography

  1. The Data Literacy Project, "The Human Impact of Data Literacy," 2020.
  2. NewVantage Partners, "Big Data and AI Executive Survey 2020," 
  3. 2020.A. Gaskell, "How Data Literate Is Your Organization?," Forbes, 2018.
  4. Gartner Research, "3 Top Takeaways from the Gartner Chief Data Officer Survey," 2018.
  5. E. Brynjolfsson, L. M. Hitt and K. Heekyung Hellen, "Strength in Numbers: How does data-driven decision-making affect firm performance?," 2011.
  6. The Data Literacy Project, "The Data Literacy Index," 2018.
  7. K. Schildkamp and C. L. Poortman, "Factors influencing the functioning of data teams," Teachers College Record, vol. 117, no. 4, 2015. 
  8. Deloitte, "Global Human Capital Trends," 2019.
  9. B. Dykes, "Why Companies Must Close The Data Literacy Divide," Forbes, 2017.
  10. Z. Gemignani, C. Zemignani, R. Galentino and P. Schaermann, "Data Fluency: Empowering Your Organization with Effective Data Communication," John Wiley & Sons, Inc., 2014.
  11. Deloitte, "Pivoting to digital maturity," 2019.
  12. L. Stevens, "Building a Data Culture: Lessons from the science behind habit formation," 2019.
  13. Deloitte, "Analytics and AI-driven enterprises thrive in the age of with," 2019.

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