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 4^th 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.

The post Data Literacy in HR: Definition, Measure, and Impact appeared first on Littal Shemer Haim.

I simulate working with a data scientist in my workshops for HR and People Analytics leaders. The audience contributes the domain expertise while I support them in a hypothetical project, offering the methodology and technical side. I discuss the analytics process from data exploration to hypothesis testing to conclusions, challenge the critical thinking skills of the audience, and explore some data fallacies. During such sessions, I face many questions about leveraging data science in HR.

In this article, I answer three critical questions: First, what should HR professionals expect when working with a data scientist? Secondly, what should HR professionals do for a successful data science project? Third, how should you deal with insignificant results in the analysis? These are critical because data literacy means beyond reading your dashboards and reports. It also means being proactive and leading the conversation with data professionals. It is the key to your success, creating impact through the data’s insights.

What should HR professionals expect when working with a data scientist?

Maybe you are thinking right now – I work with a data analyst, or I am a data analyst. Is there any difference between a data analyst and a data scientist? Though some role descriptions may overlap, a data analyst generally spends more time on routine analysis, providing reports regularly, and typically using BI tools or Excel. However, a data scientist may design how to integrate data from different sources, then manipulate, analyze, and sometimes productize it, leveraging advanced analytics and typically using programming languages like R.

The practice of data science is multidisciplinary. It encompasses three general skills – the business domain of expertise, statistical modeling, and programming. Therefore, a crucial part of your challenge in People Analytics is the effort to establish communication between professionals with different skills.

You heard a lot about the People Analytics journey that enables HR professionals to become more strategic because they speak the language of the business and impact using the right questions and insights derived from people’s data. But they can support decision-making only when they communicate those questions to data scientists.

What should HR professionals do for a successful data science project?

If there is one message I hope you would take from this article, this would be it: Make sure that the data scientist understands the business needs in workforce-related analysis. In addition, it would help if you articulated the right business questions so the research findings yield the best data storytelling you can leverage to impact.

Beyond that, let me shed some light on all data science projects’ processes while taking the data scientist’s perspective. First, you always start with a business question, sometimes titled research objectives. Then, based on a specific concern, goal, or challenge for the business, you create hypotheses about how human attitudes, behavior, or performance impact that key concern.

Only when you define what you need to measure to test your hypotheses can you source the data from any department that holds it. But then, you must ensure that there are no missing values in people’s information, typos that corrupt categorical variables, wrong labeling, duplicate records, neglected records that were not updated, or any other issues with messy data.

Then you reach the phase of Exploratory Data Analysis (briefly, EDA), which sometimes proceeds with selecting variables for prediction models and then modeling. These steps beyond EDA are called feature engineering and practical machine learning. We’ll skip these steps for now. Eventually, you communicate the results, focusing on actionable insights from the findings, and sometimes implement models into products.

As an HR professional, you have a crucial role in this process. The data scientists can’t maintain the data for you. Also, remember that while you may lack experience in data science, your data scientists may lack an understanding of people’s processes. Your responsibility is to ensure no gap between the analysis made for you and the business questions and actionable insights.

How should you deal with insignificant results in the analysis?

As I previously wrote, occasionally, findings are boring. There are cases in People Analytics where statistically insignificant results are the desired outcome. As with equal pay, sometimes organizational groups shouldn’t be significantly different. However, these insignificant results may only be the beginning of the exploration. You can always try to enrich your analysis and reveal additional insights.

For example, you can explore the interactions of variables. Multivariate statistics can raise new perspectives. You don’t have to go back to your notebooks of statistics fundamentals. Instead, ask a data scientist about interactions. So, if a comparison between groups does not reveal striking differences, adding a single variable to the analysis may uncover some hidden patterns.

From a data scientist’s point of view, I think your proactivity is invaluable. When you ask “why?”, suggest hypotheses, and challenge explanations, you leverage your domain expertise and complete the data scientist skills.

The post Data Science for HR: Critical Questions appeared first on Littal Shemer Haim.

The Employee Lifetime Value (ELV) is a helpful scheme for various People Analytics topics, such as compensation and retention projects. It enables a presentation of the business case of HR interventions. This review of ELV is based on my guest lecture at Stanford University’s People Analytics program in June 2022. (Read also my list of speech topics and public speaking engagements)

How do HR practices create business value?

HR practices create business value by connecting HR processes and business results. HR manages various operations throughout the employee lifecycle: workforce planning, recruitment, onboarding, learning and development, feedback and evaluation, recognition and reward, promotion and internal mobility, employee experience, safety and welfare, and retirement. These processes create aggregated workforce capabilities: engagement, culture, efficiency, leadership, innovation, and so forth. Those capabilities enable the organization to achieve its business goals: productivity, quality, and customer satisfaction, resulting in business outcomes, e.g., revenue growth and stakeholders’ return.

People Analytics means that HR uses employee data from HR processes to impact the business. Therefore, our research approach moves from focusing on human resource processes, which we have traditionally measured, to the focus on business performance indicators we want to impact. Indeed, People Analytics initiatives start with a conversation with business leaders. Such a conversation enables you to prioritize projects based on real business questions that affect your company.

How does ELV demonstrate a business case for HR interventions?

The Employee lifetime value is a scheme that connects the people processes to the business outcomes. You probably heard in your career that “people are our most important asset.” It makes sense. People plan and execute the company’s strategy, create competitive advantage, maintain customer relations, and bring innovation to meet future challenges and needs. But how can this statement be quantified? Employee Lifetime Value refers to the expected value the organization gains in the entire time an employee in a particular role spends working.

We see in the chart here the employee output throughout the working period in a company. The chart refers to two employee groups, the blue and the orange. The X-axis represents time, and the Y-axis represents the output of a specific role. The output is negative initially because of the costs of recruitment, onboarding, and training, and when the employee is still not productive enough. Then, a positive output rises and stabilizes until the employee gets burned out or decides to leave. The area under this curve represents the entire employee lifetime value, that is, what the employee produces, subtracting the employment cost. To increase these outputs, we want to shorten the time to productivity, reach a higher result, continue improvement for a longer period, and extend the time that the employee remains in the organization. How can we do all this? These are questions that we can explore in an analytical project.

Why is Employee Lifetime Value a helpful scheme?

Employee Lifetime Value is a helpful scheme. First, it enables an understanding of the employee’s journey in the organization regarding investments and returns in each employment stage. Secondly, analyzing employment stages in costs and outputs enables a business case for workforce processes and interventions since workforce costs and investments are related to business results. Lastly, analyzing the Employee Lifetime Value enables a comprehensive understanding of the workforce from a financial perspective that contributes to decision-making related to human capital ROI.

Any professional field has a different employee lifetime value because the outputs are different and measured differently, and the employee lifecycle is unique, including other processes and compensation. But if you choose a critical role and analyze its lifetime value, you can justify changes and improvements in human resources processes.

It is worth noting that Employee Lifetime Value is not a formal financial metric. It is not used for estimating an individual contribution either. But it is a valuable scheme. First, it provides an economic perspective of the workforce processes. It is also communicated well with the management. Finally, analyzing workforce challenges and opportunities in financial terms allows you to evaluate the long-term value of investments in people, just as you would expect any other investment to be considered in the company.

A case study based on Employee Lifetime Value

One of the opportunities I took to leverage the scheme of Employee Lifetime Value was a quick win for an HR group early in its journey to establish People Analytics Practices. Potentially, we can have many alternatives for analysis. So, why the employee lifetime value? A Quick Win can be described on two axes: complexity and impact. We should choose a project which is expected to be most impactful, but such a project that is not too complex. That was the case in this project.

The entire story, People Analytics in SMBs: small data huge impact, was published as an interview. It demonstrates that with the right attitude, HR leaders can successfully overcome barriers such as knowledge gaps, data sources, and shortage of analytics tools. The case study is about the Israeli site of GIA, the world’s foremost diamond authority. GIA is a nonprofit organization and leading source of knowledge, standards, and education in Gemology. The company has set global standards, and every diamond merchant worldwide visits the local branch for the diamond rating before marketing. So, buying a diamond comes with a quality certificate, usually a GIA certificate.

The global company management inquired how GIA Israel develops and retains talent. The answers that the Israeli site had to present could not be based on gut feelings but on numbers to prove that the investment in talent gained the desired results. So, they needed to understand the relations between the people processes and the business results.

While trying to understand employee retention, HR in the Israeli site realized, for the first time, that they should not analyze people’s data on a yearly or quarterly basis but instead use the employee lifecycle for the analysis. Since they recruit in cycles, and employees go through an extended training plan till they become expert diamond graders – a program that may vary in each recruitment cycle – the data should be explored separately for each recruitment cycle over the years. Periodic metrics of retention or attrition make no sense if you mix different cycles which experience other processes in recruitment, onboarding, learning, etc.

Focusing on recruitment cycles enables the team to link improved recruitment processes to decrease attrition numbers. They also found a positive correlation between improved processes and productivity. It was a quick win because, after only a few weeks, they could present these findings to the global management and demonstrate how the people processes in the Israeli site supported the business goals. They used the employee lifetime value of diamond graders to visualize and point to the impact of improved HR processes. In addition, they proved the return on investment in new processes because, for the first time, they described everything in terms of money – budgets and revenues.

The team could also spot the point in time when break-even happened. This point is when total investment in people and total return are equal. After surveying the compensation in the Israeli labor market, regarding competing employers for such workforce segment, they changed the compensation plan by setting different times for financial benefits and even improved benefits to be more competitive, which eventually increased the pay budget in the Israeli site.

Demo with fiction numbers and a hypothetical case

I can’t share the data, but in my workshops and lectures, I continue the story with numbers and visualization, in a demo of a similar analysis, but with fiction numbers and a hypothetical case. Here’s a sneak peek: Imagine a plant with two recruitment cycles: the blue group in the year’s first half and the orange group in the second half. These are the same groups represented in the chart above. We can integrate data from various sources to analyze the employee lifetime value: core HR platform, payment platform, and the business unit.

The fictional company could not succeed in retaining employees in the blue group for more than a year. So, the HR team decided to change some processes to deal with this challenge. The changes made are represented by their costs. First, they improved the recruitment process with better and more expensive assessment tools. The idea was that better and more suitable candidates would eventually contribute to workforce stability. Then, onboarding and training activities provided within one month to the blue group were split for the orange employees. The idea was that more extended support would be better for onboarding success till reaching the point of fully contributing. So, the training budget remained the same but was used for a more extended period. Finally, the performance review was the same in the two groups after six months.

The employee value in each month is the sum of outputs subtracting the costs: base salary, bonus, and HR processes costs. Suppose we organize the groups’ numbers on the same time axis, tenure in months. In that case, we can explore how the employee lifetime value changed after the changes in people processes. 

Although the company invested more in recruitment in the orange cycle, the break-even point remained the same after three months because the orange employees gained better outputs. Moreover, the orange employees remained at work for a more extended period. As a result, the area under the orange curve is larger than the area under the blue curve. The change in HR processes contributed to the business results.

The post Employee Lifetime Value appeared first on Littal Shemer Haim.

(This article is based on my Hebrew “TED” talk at the conference People Geekup Tel Aviv in June 2019. I dedicated this talk to HR professionals who make their first steps on their data-driven journey. Read also my list of Public Speaking).

How to change the analytic mindset of HR for good? I deal with this question for years. I’m a People Analytics consultant and mentor. I help HR teams to leverage people’s data and HR technology to drive insights that contribute to business success. A nice side effect of my activity as an advisor is making HR professionals heroes in their organizations. I’m an applied researcher for more than two decades now – a multidisciplinary professional with a background in Economics, Business Strategy, Psychology, Statistics, Programming, and more. Connecting all dots into a diverse role is not only a millennials theme. It is the reason I started my own business many years ago.

Connecting the dots

 

Here’s a fun fact about me: I’m a photographer artist. I’m also an eternal student, a curious character who learns about ideas and human experiences, and an autodidact in wide-ranging fields of interest – Positive psychology is one example. A few years ago, I realized that principles of Positive Psychology, which I learned from books and lectures, are nicely reflected in my personal experiences as a photographer artist. I started to document those reflections, and soon enough, I introduced to the world a new therapeutic photography method, that was proved to be effective to my students and audience. I called it Focus on Happiness.

If there a single sentence that sums up my entire therapeutic photography method, this would be it: “The view is an interaction of ability and opportunity”. Every picture or frame of our lives is a combination of the things we can do and the circumstances that enable us to do so. As simple as that. This insight is most relevant for me today, as I mentor HR managers on their data-driven journey. The impact of HR professionals in their organization is a combination of what they can do with data, and the business needs, i.e., the circumstances in which they express this ability. Today I’ll share the three key practices (and a bonus one too) that enable such a combination, between ability and opportunity. If HR professionals follow them, their success in impacting the business by People Analytics is guaranteed.

Computers are useless

 

Why not start with the bonus, for a change? The first quote I added to my diverse inspiration list has been attributed to Pablo Picasso, the most vital artist of the 20^th century: “Computers Are Useless. They Can Only Give You Answers”. Obviously, Picasso had no clue about People Analytics, but his idea is applicable to all of us in this domain. There is no point in running the most sophisticated analytics or building a shiny dashboard, without the attempt to answer a business question. Start your analytical journey with a business question that involves actionable insights. Picasso was right! Computers can only give us answers. We are the ones who must come up with the right business questions in the first place. Only then, we can proceed with the data, our people data.

Experiment with data – our own data

 

In my recent article, People Analytics – Build the Value Chain, I mentioned that there are plenty of online courses for People Analytics. Some are pretty good; others are nothing but excellent because they enable students to be exposed to the invaluable experience of experts, respected colleagues in this field. However, all online courses lack the opportunity to exercise actual business questions of your company and real people data from your own HR-tech solutions. This can be done only in Intra-organizational training and mentoring. Such training enables up-skilling HR to be more data-driven, and moreover, it may also be the actual foundation of People Analytics projects.

So, whatever you do to educate yourselves, make sure that your learning opportunities include experiments with your own data. Master business questions in your organization and your own data, so you’ll be able to build your company HR data strategy in the near future – move from business question to actionable insights by owning your data sources, storages, analytics and visualized outputs.

Hack #1 – when you afraid to fail

 

I was recently a panelist in HackingHR Tel Aviv. For me, the most exciting moment in the event was when half of the audience raised hands after I asked, “Who works today or is going to start working soon in People Analytics?” I knew most of those faces in the audience, and I realize my contribution to establishing this profession in my country. But it doesn’t mean I didn’t fail. And personally, I know a lot about the fear of failure.

When you start your journey to data-driven HR, be prepared to fail. However, if you create psychological safety in your learning environment, you won’t be afraid to start again. Your failure will only mean that you are not there, yet, but you are getting there. Take Adam Grant’s advice – always question your default solutions and try other options. Or, in the word of a mentee testimonial, in a case study of people Analytics is SMBs – “We could afford to experiment with data, and making mistakes, knowing that we had the support of a professional framework… In our mentoring sessions, but also between sessions, each of us could comfortably ask any question, raise ideas, and make a mistake. Thanks to the openness that was created within the team, everybody felt that we were able to cope with the challenge.”

Hack #2 – when change is difficult

 

Change is inevitable, and that is also true in the HR domain. But the experience of leading a change is hard. Why? Scientific evidence connect the challenge of change to the way the human brain is wired and explains why most change initiatives fail. As I previously mentioned, “A core driver of the brain function is maintaining safety and stability. Therefore, even a beneficial change can be perceived as a threat. When you lead a change in your organization, you directly conflict with your brains’ core needs.”

To overcome this barrier, and help against the reflexive resistance, you need to create new rituals within learning sessions, that would generate a sense of security. While mentoring HR teams, I discovered that rituals are effective: “When meeting agenda and pace of learning are predictable, and when new social norms such as asking questions and thinking out loud are created, people practice openness and curiosity. Familiarity with the setting gives them a sense of certainty and stability.”

 

Build the Value Chain

 

I followed all these key practices – Practice your own data, psychological safety, and rituals – As I built my training program for data-driven HR, which I offer now to organizations worldwide. I help HR professionals to build the People Analytics value chain through sixteen lessons and four milestones. Each HR team can create, with my guidance, its unique rituals. The team members learn by using their own data to solve their business questions, in an experimental environment, where mistakes and failure are welcome as an opportunity to learn. I’m very excited to mold my experience, both my failure and successful case studies, into a structured course that suits each organization, no matter how large or small.

 

 

Data Makes you fly

 

When I started this blog, I chose one of my crane’s photographs for the main page slider. As I previously wrote, cranes are a great metaphor – always on a worldwide journey, with their large flocks, dynamic roles, and inter-dependencies. They are just like us, people in organizations, who are on their journey to data-driven HR. When I wrote on that slider that “data makes you fly”, I couldn’t imagine that in three years I would be recognized as one of the global influencers in the HR-Tech industry, and have such a fascinating career opportunity. In my mind, I only had my career path up to that point. But I also thought about HR leaders who embrace analytics and become heroes in their organizations.

I hope that the HR journey in the data world will last, as the cranes’ endless journey. However, we face such dramatic change now, that may turn everything to other directions. The demand for new skills in the HR role, i.e., the Procurement and Ethics of HR-Tech breathe down our neck. HR people can’t procrastinate their own change. They must up-skill and be more data-driven. I call you to join this journey today.

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"You will find in this book sixteen lessons, organized in four milestones that, from my experience, build the People Analytics value chain."

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The post Changing the Analytic Mindset of HR for Good appeared first on Littal Shemer Haim.

HR people must acquire better analytics skills. There is no question about that. When HR role vacancies are analyzed, this orientation is listed among the most important competencies. The data-driven part of HR practitioners’ work has emerged rapidly in the last years. All HR sectors must now leverage their data assets to make better decisions, and support all kinds of stakeholders, from employees to executives. They must also understand ML (machine learning) and AI (artificial intelligence) to have ownership of procurement and ethics in the implementation of HR-Tech. But how can HR practitioners close the gap? How can they up-skill and become more analytical?

 

A secret factor in learning programs

 

While most academic programs in the HR field still lag, and only a few exceptional programs focus on People Analytics, many other learning solutions can be found online. Wise and agile entrepreneurs, with a strong background in HR, analytics, and tech, already offer learning platforms and an excessive amount of content. But I believe that all of them still lack the secret factor that guarantees up-skilling HR.

Yes, a secret factor. But I’m going to tell you. After few years of training and mentoring HR people in the domain of People Analytics – with different levels of success, I must admit – I think I understand now how to prepare the HR team to embrace a data-driven mindset and People Analytics practices. By tracing the way HR groups in a variety of organizations have built their value chain in People Analytics, I discovered a new ingredient of success.

If you follow my work for quite some time now, you already know that I promote psychological safety in a learning environment to bypass the resistance for change. Or, in the word of a mentee testimonial, in a case study of people Analytics is SMBs – “We could afford to experiment with data, and making mistakes, knowing that we had the support of a professional framework”. However, crucial as it is, that psychological safety is not enough. The keywords here are “experiment with data”, our own data.

 

Experiment with data – our own data

 

I honestly believe that HR people can overcome their analytics barriers when they exercise. Online courses do offer a lot of exercise. However, only when HR people practice with real data, their own organizational data, they can bypass the obstacles. Only when they define business questions that are related to people in their organization and practically use methodologies that help them to make key employee decisions, they can succeed in developing the desired skills, and start interpreting and present analytics for people-related decisions.

Practice your own data. As simple as that. It doesn’t mean that HR people should become data scientists. In their training programs, they only need to follow the value chain of People Analytics, i.e., make first steps toward a quick win, with respect to business questions, acquire in-house resources and abilities to executing the first analytic project within the organization, and use the first analytic project to impact the organization.

From my experience, exercising based on real questions, challenges, and data is an important motive. Moreover, such training does not only enable up-skilling but may also be the actual foundation of a data-driven HR.

When done in groups, in which each member contributes according to actual role or aspirations, and when collaboration is established with People Analysts or other relevant roles within the organization, HR up-skilling in the analytics domain is guaranteed.

Featured in the book

"This book is not a typical textbook about People Analytics practices. It offers readers an opportunity to learn and change while enjoying themselves, taking time to contemplate, absorb ideas, and, hopefully, overcome barriers."

"You will find in this book sixteen lessons, organized in four milestones that, from my experience, build the People Analytics value chain."

Littal Shemer Haim

Author

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If you randomly select an HR leader and ask about her progress in the journey into data-driven HR, the chances that she would tell you that she is already on the track, according to a recent survey, are about 70%. However, if you dig deeper into your conversation, you might find out that you and your interlocutor mean completely different things when pronouncing the words “People Analytics”. What your partner sees as progress might not be at all a progress in your eye. No wonder that this might be the case, when the definitions of this field are vague, and we are still struggling to form its practices.

So how can we describe the status of People Analytics, better than a single number in survey results? In one of my public talks about People Analytics this year, I challenged my self to describe the state of our practice in five sentences only. My qualitative effort worked, I guess, at least according to the audience response. Looking back at my list, I realize now that each point I made implies a myth about People Analytics. HR leaders should be aware of the following five misconceptions, or otherwise, continue to let these false ideas about People Analytics inhibit their advancement.

Myth #1: People Analytics is an established practice within HR management

No. Although we witness higher adoption rates every year, there are barriers, and overcoming takes time. Among companies that presented case studies at a recent conference in Europe, many mentioned a time span of one or two years until gaining a return on investment from People Analytics activities. I was fortunate to hear dozens of lectures about People Analytics this year. Those case studies presented in conferences are just a handful. Conferences curators will not pick organizations that have not reached significant milestones. 

However, other organizations, those who are still struggling, shuffling, freezing, or just learning from their mistakes, are not less interesting, nor less important. If you experience difficulties in your People Analytics journey, you are certainly not alone. In a recent study published by Visier, obstacles of organizations include the lack of connection between analytics and business results, basing analytics HR system data only, insufficient “data-driven” skillset among HR people, data quality issues, unstandardized metrics and over-dependence on IT for analytics.

Myth #2: People Analytics is professional research about HR practices

No. People Analytics is a multidisciplinary profession, that aims to support business decisions related to people on data. HR leaders are not using People Analytics to measure the efficiency of HR practices, but rather to understand the impact of their practices on the business results. HR people manage a variety of processes throughout the employee’s life cycle: planning, recruitment, learning, evaluation, recognition, reward, mobility, promotion, safety, welfare, and more. These processes create aggregated workforce capabilities: engagement, culture, efficiency, leadership, innovation, and so forth. Those capabilities enable the organization to achieve its business goals: productivity, quality, and customer satisfaction, which, in turn, result in business outcomes, e.g., revenue growth and stakeholders return. 

People Analytics means that HR focus on the use of people data, derived from their processes, to impact the business. In this context, it is worth to mention, again, that the HR dashboards are not People Analytics. Both People Analytics and HR dashboards deal with Performance. However, each practice has a different approach. Dashboards enable us to present different HR KPIs, but can’t answer the question: Why? For that purpose, we need People Analytics, which enable us to understand the factors that drive those KPIs presented on our dashboards.

Myth #3: Traditional research is outdated in the era of People Analytics

No. People Analytics practices combine new data sources and technologies with good old practices. Mentioning tradition, let me share the puzzlement I’ve experienced in a People Analytics conference, this year in London. I chose in advance to participate in those sessions that seemed most innovative. However, I discovered that some speakers relied on quite traditional research methods, that actually, I’ve been practiced my self in organizations for years. How does it fit in with new technologies, and with plenty of new sources of people data? 

The bridge between traditional research methods and innovation are two trends. The first is data integration. We no longer settle for analytics based on HR data sources, but rather combine many types of data about people, both from HR and business units. The second is our new perspective in the future. We refer to people data at different stages in the employee life cycle: candidates, employees, and former workers, and we focus our analytics efforts at forecasting outcomes related to business questions.

Myth #4: People Analytics is great only if you are C-suite

No. People Analytics is about different objectives and questions, of old and new stakeholders: Executives, HR, managers, and people. Yes, the people! Despite the emerging trends, new technologies, and data sources, leaders in organizations still ask the same old and basic question: Who? In the past, this question was quite general: Who are the people with the skills, work habits, knowledge, experience, and personal qualities that drive the organization to meet its goals? Today they still ask “who?”, but more specifically, and with a focus on business metrics: who create the best new products, make the most revenue, find the greatest efficiencies, build great workplaces, adapt to changing business conditions, delight customers, attract others to join the organization? In other words, we put the question “who?” with things that are outside the traditional territory of HR. Traditional research, e.g., employee engagement or training effectiveness, which was already out there, for decades, is now connected directly to the business. 

However, HR and business leaders are not the only ones who raise questions. Today employees expect to receive personalized service in the organization, just like they do in any other context of their lives. We all live through our smartphones, and there’s no reason why employees should expect it to be different at work, and regarding significant questions about career and wellbeing. The People Analytics function should address these needs.

Myth #5: People Analytics means having a data scientist in the HR department

Not necessarily. Although it would be nice to have such a professional in every HR department, we do witness a shift from a research perspective and data science projects to analytics products. Part of the progress in People Analytics is the implementation of HR-tech solutions that enable real-time analysis instead of research cycles. Organizations implement analytics solutions throughout the entire employee’s life cycle. My HR-tech classification includes Workforce Planning and Mobility, Sourcing, Selecting and Hiring, Onboarding and Culture Fit, Employee Experience and Sentiment Measures, Employee Wellness, Health, and Safety, Employee Growth, Learning & Development, Goals Tracking, Performance Review, Productivity, Organizational Design, Networks, Teams, and Collaboration. But of course, there are so many other ways to capture the HR-tech ecosystem. 

People Analysts have a lot to offer in the processes of HR-tech implementation. They help using technology to amplify, not overtake, the influential role in humanity in organizations. They can do so, mainly due to their ability to embrace two new responsibilities: procurement and ethics.

I truly believe that awareness of these five misconceptions contributes to faster progress in People Analytics. However, there may be more false ideas about this new practice. Did you tackle more Myths? Please share it in a comment.

Featured in the book

"This book is not a typical textbook about People Analytics practices. It offers readers an opportunity to learn and change while enjoying themselves, taking time to contemplate, absorb ideas, and, hopefully, overcome barriers."

"You will find in this book sixteen lessons, organized in four milestones that, from my experience, build the People Analytics value chain."

Littal Shemer Haim

Author

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Please leave this field empty

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The post Five myths about People Analytics that inhibit your progress appeared first on Littal Shemer Haim.

When I talk to a typical prospect, i.e., an HR leader who is interested to start the journey to data-driven HR, our conversation always starts with an important distinction: Are we talking about “People Analytics” or “HR Analytics”. These two terms are confounding, but they are certainly not synonyms. Understanding the differences between the two terms is the key to the successful discussion and plan of our joint mission.

 

People Analytics vs. HR Analytics

People Analytics refers to the exploration of employee data patterns, and communication of significant results to business leaders, in order to support decisions related to people in the organization and improve business performance. HR Analytics, which sometimes is presented as a dashboard, is not aimed to improve business performance directly, but rather to serve the efficiency of HR functions.

The distinction between “People Analytics” and “HR Analytics” is clearly explained by Guenole, Ferrar and Feinzig, in their book “The Power of People: How Successful Organizations Use Workforce Analytics to Improve Business Performance”. The authors define People Analytics as “the approach of measuring behaviors in organizations and knowing how to knit them together to improve business performance. The approach is similar to that taken with customer behavior, but this one concerns employee behaviors”. Their definition of HR Analytics is “the functioning of the HR team itself—for example, analyzing HR key performance indicators (KPIs) such as time to hire. Such analytics are about holding the HR team accountable”.

 

Why do you need both?

Practically, both People Analytics and dashboards of HR Analytics deal with Performance. However, each practice has a different approach: Dashboards enable us to present different KPIs, and to answer questions such as: Did we reach our goals? How far are we from achieving our goals? However, by using dashboards, we actually can’t answer the question: Why? For that purpose, we need People Analytics, which enables us to understand the factors that drive those KPIs that we presented on our dashboards. We can do so with different levels of Analytics: Descriptive, Diagnostic, Predictive, and Prescriptive. (See illustration: Gartner analytics ascendancy model). In other words, the company needs both dashboards and People Analytics practices because it must be aware of its KPIs, and it also needs to understand how to improve those exact KPIs.

Let’s take an example, to demonstrate how dashboards and People Analytics are complementary. Many organizations deal with the challenge of employee retention. Suppose that we have a dashboard that contains a yearly employee attrition rate. A presentation of this KPI separates different kinds of voluntary and involuntary turnover, includes some comparisons between employee sectors, displays metrics trends, and even points to outliers. A People Analytics solution for the employee attrition challenge may be a predictive model that enables one to point to certain characteristics of employees that are prone to leave or stay in the organization. Such insight may lead to different approaches toward different employees, which will eventually result in better outcomes in the long run.

 

HR leaders prefer Dashboards first

When my prospects understand the distinction between dashboards and People Analytics they usually express enormous curiosity about the second. For example, many of them are fascinated when I describe how to predict employee attrition. However, when we get to the more practical ground, in order to start a project, we go back to discuss dashboards. Although HR leaders are very interested to get insights from People Analytics, their immediate need is usually a tool that integrates data from multiple sources and displays them uniformly and clearly, in order to monitor and control their operations. After all, dashboards have become an important part of other business departments, and HR should not be different in that sense.

 

An effective HR Dashboard

HR dashboards are just like any other BI dashboards. When they are well designed they can tell a whole story at a glance. They connect data and analysis that are most needed to specific business questions, i.e., KPIs, in a simple and clear way. Their layout and data visualizations enable the users to access the data they need to get answers from and get exactly those answers — correctly and completely.

However, HR dashboards are distinctive. They are not created only for HR leaders, but rather for business leaders in the organization. Business leaders and HR leaders should cooperate to define the right KPIs, and monitor the right data, aligned with the company strategy and goals. Effective HR dashboards provide a concise and clear display of those workforce KPIs, which are relevant for the business performance, and assist in decision making. They rely on meaningful data, which can be linked to future actions. This challenges HR to fit a dashboard to each line of business. HR must understand the unique workforce needs of every unit, and then determine what metrics to present. For example, if a dashboard shows that top performers in one sector are found via LinkedIn, and at job fairs in another sector, recruitment can be planned, executed, and measured accordingly.

 

In my future articles, I’ll review the best practice of building and publishing HR dashboards. But to conclude this discussion, let’s remember, that since business questions always evolve, HR dashboards are, and will always be, developing tools. It is important to make sure, once in a while, that the HR dashboard still provides actionable information.

Related Course

The People Analytics Journey

An overview of future role of HR leaders in improving business performance by informed decisions about people based on data. People Analytics transforming HR; The Role of People Analytics Leader; Case Studies and Simulations; Emerging trends of HR tech.

The Syllabus

The post HR Dashboards are not People Analytics – but you need both! appeared first on Littal Shemer Haim.