Littal Shemer Haim

People Analytics, HR Data Strategy, Organizational Research – Consultant, Mentor, Speaker, Influencer

Data Literacy in HR: Definition, Measure, and Impact

A review of definitions, measures, and assessment tools of data literacy, and interpretation following the experience of educating and training HR professionals in People Analytics, to support the HR sector's transformation to become a better client of data.
Photography by Littal Shemer Haim ©
(Reading Time: 8 minutes)

Are HR professionals capable of deriving meaningful information and actionable insights from their organization’s data about its people? Are the data literacy assessment tools valid to measure such capabilities among HR? Should we strive for a new and unique definition of data literacy to enhance such skills among HR professionals?

The discussion about data literacy is relatively new in the context of the HR function in organizations. It is crucial, though, as the methods and solutions of People Analytics gain more traction. In this article, I review definitions, measures, and assessment tools based on literature and current practices, which I interpret following my experience educating and training HR professionals in People Analytics. Hopefully, my point of view will support the HR sector in its transformation to become a better client of data. Furthermore, I believe HR professionals can impact their data literacy development, so I conclude this article with some recommendations.

Do HR professionals speak data?

A few years ago, Gartner analysts coined the term speak data, and related it to learning any second language. Unfortunately, the digitization of businesses and the emergence of advanced analytics and machine learning did not make most professionals speak data fluently. As a result, the lack of a common language for interpreting organizational data is a fundamental communication challenge, Gartner analysts stated.

Since then, many organizations have prioritized data fluency and initiated competency development in data literacy to overcome deficiencies in managing, analyzing, and applying data in various business contexts. New conversations around data and analytics focused on aspects of business questions, integration of multiple data sources, and analytical approaches.

The HR department is a late bloomer, at least concerning data literacy. According to Wharton professor Matthew Bidwell, data literacy has become a core skill for HR. Analytics may sometimes feel overwhelming for reasonable HR professionals, but sophisticated tools and techniques are unnecessary, as simple analytics can bring significant value. However, despite the expectations of most HR executives, data-driven practices are standard in less than half of global companies represented in a recent survey.

Beyond the gap between expectations and practices, another research reveals that, on average, HR leaders lag far behind other professionals in their ability to use data to guide business decisions. Sadly, business leaders still tend not to trust HR to use data to anticipate and help them fill their talent needs. Moreover, a third of HR professionals admit significant people analytics and data skills gaps. Therefore, data literacy must be developed within the business context to overcome this.

What is data literacy in the business context?

Data literacy is the ability to read, understand, create, and communicate data as information. However, I find this definition insufficient in the discussion about HR practices because it doesn’t refer to what you should and shouldn’t do with data in the business context and HR role.

Data is crucial to business success, so there is no surprise in organizations’ demand for some degree of data literacy from all their employees. But beyond the ability to derive meaningful information from data, a good definition of data literacy, in my opinion, must consider the complexity of data science as an emerging field in various business verticals, particularly for the HR department.

Data literacy should not be a label reserved for data scientists, and HR people indeed not supposed to become data scientists. However, a good definition must consider the multidisciplinary context of data science, encompassing three general domains: the business domain of expertise, statistical modeling, and hacking skills. Non-data specialists who leverage data must know how to do it right within the business purpose’s boundaries while being critical about the datasets, analytics methods, and tools.

Therefore, data literacy for HR professionals should include the following abilities: knowing what data is appropriate to use for a particular purpose, interpreting data visualizations, thinking critically about information yielded by data analysis, understanding analytics methods and their appropriateness, recognizing misinterpretations and misleading use of data, and communicating information derived from data analysis. 

The overall objective of data literacy among HR professionals is the ability to use talent data and analytics to contribute to business outcomes. By collecting data on employee behavior and attitudes and tying it to business performance, HR can highlight opportunities and risks for the business. Following their conversations with business leaders, HR professionals should be able to transform business concerns into research objectives that data experts or solutions will later conduct.  

Am I data literate?

I’m happy that you ask. But, unfortunately, the odds are not in your favor. According to the Data Literacy Project, 76% of business decision-makers aren’t confident in their ability to read, work with, analyze, and argue with data. This initiative, founded by analytics vendors and thought leaders, offers a short test to discover your data persona. Then, based on your persona, which may be a data avoider, newcomer, apprentice, or guru, the project offers resources to take you to the next level in handling data.

However, your data persona is a descriptive classification that may neglect to measure your skillset in detail. Data To The People offers a competency framework for data literacy: “Databilities” is a research-backed approach that enables organizations to measure data literacy across their workforce and compare it within professions, industries, and regions. This framework defines six levels of progression for reading, writing, and data comprehension. While the lowest competency levels focus on memory, more advanced levels focus on understanding, and the upper levels focus on applying skills.

Based on this framework and a survey among 5,000 employed individuals in northern America, Australia, India, and the UK, a global data literacy benchmark provides an updated view. According to the survey results in 2022, only 6% of the respondent can help their peers with data concepts and culture, reading, writing, and comprehending data. Furthermore, 44% of the survey participants need help with data concepts and culture, and 52% need help with data governance.

Therefore, the researchers concluded that the focus should be on creating opportunities for employees with higher data literacy levels to inspire others to continue developing their skills actively. Moreover, there is a need to provide more significant support to those in the middle levels of the data literacy competency level and more encouragement to those at the bottom to engage with data literacy concepts and seek guidance from their peers.

Suppose you’d like to take a deeper review of measuring data literacy or build your assessment tool. In that case, it is worth mentioning that parts of the “Databilities” framework were created based on the data literacy competencies initially defined by Ridsdale et al. in their report about strategies and best practices for data literacy education. The authors have synthesized a set of skills comprising various levels of data literacy, which they presented in a data literacy competencies matrix. They organized it by core aspects of their data literacy definition, which include data collection, management, evaluation, and application.

The competency framework of Ridsdale et al. is compared to many other assessment tools in a report by the national statistical office of Canada, which reviews the data literacy definition and measures from the point of view of the public service. Notably, the report provides an overview of existing approaches to measuring data literacy, from self-assessment tools to objective measures and from individual to organizational assessments.

Why are there barriers to data literacy in HR?

Did you take any of the abovementioned assessments? Did you find gaps? I’m sure you did, as I occasionally hear the frustration of many data professionals who serve or operate in the People Analytics functions. When People Analytics leaders share their experience dealing with their inner clients in the HR department, they usually describe barriers that hinder HR professionals’ development of data literacy skills.

Some HR professionals may not be fully aware of the importance of data literacy in their role and how it can contribute to evidence-based decision-making. Other HR professionals who have gained such awareness and highly prioritize data literacy in their skills development sometimes get limited access to data and analysis tools. They may also get limited training opportunities, whether formal training programs or professional development opportunities focusing on data literacy skills.

Unfortunately, there is still fear, intimidation, or lack of confidence in using data among HR professionals. It may stem from a lack of experience or training opportunities in data analysis and time constraints, which may limit their ability to invest in developing data literacy skills by themselves.

Organizational immaturity in data may also be a factor in the gap in data literacy. Immature organizations lack a culture that prioritizes data-driven decision-making or may not foster a data-driven mindset among HR professionals. Immaturity can also manifest in concerns about data quality and integrity issues, which can cause HR professionals to hesitate to utilize and interpret the data.

Sometimes I hear accusations that HR professionals are not analytical enough. I perceive HR as responsible for upskilling in this area, but as I stated in some public talks, the current state of HR is more complicated. First, academic programs still neglect People Analytics as a mandatory domain. Secondly, when they graduate, analytical expectations in HR practitioners’ roles are not high, so seeds of skills and interest might decline over time. Third, the entire HR sector relies on consultants and contractors, but unfortunately, many are also not updated, and their interest in changing the state of affairs is unclear. Fourth, tech vendors sometimes prefer to bypass HR, which also makes HR lag. Finally, HR is under-budgeted. It is most prominent when it comes to professional development. Relying on marketing content sponsored by technology vendors is insufficient for upskilling and reskilling.

Overcoming the barriers to data literacy in HR requires organizational support in providing access to data, technology, and training opportunities, fostering a data-driven culture, and addressing data quality and integrity concerns. However, HR professionals must also proactively develop their data literacy skills through self-directed learning and seeking relevant resources and opportunities.

How to become data literate more effectively?

Your curiosity and critical thinking are essential in building your data literacy skills. Like many HR professionals, you probably consider yourself a person with good intuitions about people. However, only a willingness to combine your intuitive judgments with insights from data will enable you to start your journey as a data client in HR. Ask yourself how data analysis can contribute to better judgment and decision-making. Moreover, always use the question “Why?” to explore every phenomenon or experience you encounter further. This mindset will prepare you for the long process of developing your data literacy skills.

Developing your data literacy skills, like learning a foreign language, is not a checklist. It takes an ongoing effort to understand and assimilate it. A systematic approach to building your data literacy skills includes a few principles. Experts recommend distinguishing between data literacy and technical literacy, starting with a baseline of your skills, using a common language, building a culture of learning that rewards curiosity, enabling different learning channels, defining success, and ensuring leaders are also involved.

Remember that while case studies are inspiring and convenient to learn about what others are doing, the only way to understand data is by working with your organizational data. So start to look for opportunities to get involved with analytics or research projects. I promise you that when you get involved, vague concepts about data, e.g., data quality, will become much clearer. Soon enough, you’ll discover that you have become a more sophisticated data client and user and a part of the development of data culture.

What should you do next?

Data literacy is crucial at every stage in an organization’s journey to data-driven HR. From my experience, this journey follows a roadmap of four main chapters, each including four subtopics. To get started, we must charge ourselves with the basics: definitions, job requirements, barriers, and some myths worth dismissing. Then the journey begins with a quick win based on the ability to prioritize business questions. You also develop your orientation in the data world, handle data integrity, and pick some tech tools. Next, you demonstrate capability, apply methodologies, produce processes and automation, and build the team within HR and relationships with your partners outside HR. Eventually, you create a data-driven culture, influence various stakeholders, impact communication, and identify more opportunities for influence while emphasizing ethics in the field.

Data literacy manifests itself differently in each step of this journey. However, it does not stand on its own. The purpose is to support business decisions related to people. Therefore, the path to improved decisions through data requires decision literacy first, then data literacy. In other words, you must be contextually aware of the business’s problems and understand the objectives of the decision. Gaps between reality and business objectives create decision opportunities, e.g., strategy changes or new resource allocation. The most valuable data will connect to a business’s critical problems and gaps. Business leaders will inherently find interest in data and analytics tools within the context of current business problems and gaps. Data and analytics professionals will contribute the most when they understand the data and tools available to address these problems and gaps.

So when I teach my introductory course, The People Analytics Journey, I consider the objective to guide HR leaders to impact business performance by making informed decisions about people based on data. The course covers real-world use cases of analytics. It enables HR leaders to be familiar with data science terms and concepts and understand how to leverage them for decision-making. It contributes to developing a proper analytical mindset and data literacy based on human resource decision context.

The data science entrepreneur Clive Humby coined the famous phrase “data is the new oil.” But, to be used, the oil needs to be refined by people. Likewise, data also need to go through people to be used in business. To gain their seat at the table, HR professionals don’t need to become data scientists, but in the 4th industrial revolution, they need to be comfortable working with data. More than ever, data literacy is a crucial skill for success. It becomes essential in driving business value, as it is included formally in most data and analytics strategies and in change management programs.

Littal Shemer Haim

Littal Shemer Haim

Littal Shemer Haim brings Data Science into HR activities, to guide organizations to base decision-making about people on data. Her vast experience in applied research, keen usage of statistical modeling, constant exposure to new technologies, and genuine interest in people’s lives, all led her to focus nowadays on HR Data Strategy, People Analytics, and Organizational Research.

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The People Analytics Journey, an introductory course for HR professionals, covers real-world use cases of analytics and enables them to be familiar with data science terms and competencies.

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