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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.

People analytics is gaining special interest in the Israeli HR community these days. HR leaders in all kinds of organizations start to prepare their plan for 2018, and many of them finally set the major goal: Use people data to support business decisions. However, they share a common barrier, which is embedded in the simplest question: How should I start?

Plenty of “first step” advice. Which to follow?

If you are an HR leader who looks for advice, don’t start with Google search for “People Analytics first step”. Or do start with these search terms, and deal with more than 10 million search results. Any HR consultant that you can think of, and even any HR tech supplier, have already dedicated at least one blog post to this subject. The consequence is a flood of advice, tons of paragraphs about data integration and platform selection, and so many buzzwords about data science and machine learning, which will probably make you feel aversion in regards to the whole journey.

Instead, I recommend starting your journey by following only one step: Listen to prospective project sponsors. You can read about it in detail, in the new book: The Power of People: How Successful Organizations Use Workforce Analytics to Improve Business Performance, by N. Guenole, J. Ferrar, and S. Feinzig. In a nutshell, these authors suggest that in your first few days of the People Analytics journey, you should explore prospective project sponsors. “A project sponsor is a person or group who provides support (through financial means or personal endorsements) for a workforce analytics project or activity”, they explain.

Start interviewing the influential leaders in your organization, and identify the source of support and possible projects for your new People Analytics function. “The more conversations you have with prospective project sponsors early on, the more likely you will be able to separate good projects from really exceptional projects”, the authors conclude. These interviews will help you to prioritize the projects, based on real business questions that impact your organization.

Prepare for a conversation with influential leaders

Are you comfortable to start a discussion about People Analytics in your organization? Most HR leaders that I’ve met so far would respond negatively since they are not familiar enough with the complexity of the People Analytics domain. It may be clear that People Analytics goes beyond the HR department scope, as opposed to old-school organizational researches. HR leaders might have heard that it is all about business performance. They also might have a clue about different kinds of data, which can be used not just for inference but rather for prediction. However, without a deep understanding of this new field of practice, they may not feel comfortable starting conversations with influential leaders in their company. They surely need a short, yet thorough, preparation. A parsimonious definition of the People Analytics domain, that close the gap between c-level business questions and the everyday tasks of the people analyst, is probably most useful at this stage.

For that purpose, I’ve suggested, in a previous blog post, a definition for People Analytics, which contains five perspectives. It has a top-down structure, describing People analytics through different organizational perspectives, which emphasize not only the complexity of this field but also its vast influence in different aspects of the activity in the organization. Explicitly, I suggest to describe this domain, starting from C-level and business perspective, go through HR processes that derived from it, and through IT and HRIS that enable to manage it, and end up with a data science perspective and the role of the People Analytics leader:

Understanding this scheme, and specifically, the way each level in this structure is influencing and being influenced by the nature of the level on its top can help HR leaders to get prepared and feel more comfortable in their first communication with business leaders in the organization.

Make your own interview guideline

A conversation with business leaders should not be spontaneously handled, but rather planned in advance. Particularly, it should be clear what topics to bring up and how to develop each topic during the conversation. An interview guideline is a useful tool for that purpose.
When I approach a new organization, with the aspiration to start a People Analytics project, I base my interview guideline on the aforementioned five perspective definition. But before I start designing my guideline, I write down a shortlist of objectives. Having a clear list of objectives ensure that the right kind of information actually raised during the interview. In other words, it keeps the interviewer from getting lost during the interview.

For example, here are two possible objectives for an interview:
“Explore metrics of desired business outcomes that may lead to a funded analytic project.”
“Discover sources of data, people with access to data, people who assist in data preparation.”

The structure of an interview guideline is straightforward: Start with a short introduction to present yourself and the purpose of the conversation, and follow with some warm-up questions that establish rapport. Proceed with open-ended questions about each of your topics and probe to get specific examples whenever needed. End with some wrap-up questions that conclude the subject, and give the interviewees an opportunity to ask you anything in response to their experience, before you thank them and complete the interview.

For example, here is a question from a guideline based on the five perspectives (C-level and business): “What are the most important outcomes in your line of business? How does it measure? Probe for metrics: sales, customer services, safety, etc.”

Qualitative research is beneficial

Communication skills are considered crucial for a People Analyst, mainly due to the need to communicate research findings and “tell stories with data”. However, the ability to conduct a conversation with business leaders is important as well. When everyone in the field of People Analytics is obsessed with data and predictive models, it is worthwhile to remember the benefits of traditional qualitative research methods. After all, the key to success in People Analytics is asking the right business questions. It must come first, long before analyzing data sets, using sophisticated machine learning models, or creating an amazing visualization. These “right questions” are actually the outcomes of good conversations with business leaders.

I encourage any HR leader to find those right questions using the principles of in-depth interviews and scan the organization with a methodological tool. Sometimes it maybe even a good idea to consider a professional interviewer for this task. Such an interviewer may expand the guideline questions, in order to achieve a deeper understanding of certain subjects, lead the discussion to various directions, and probe for detailed responses to each question.

The whole point in an in-depth interview is to create the right atmosphere for rich content responses: open-ended questions enable participants to express themselves and tell “their own story” with unique terms and words. This, in return, may reveal unexpected and unusual themes and subjects. Moreover, during a professional interview, participants are encouraged to express themselves freely, share opinions that may be deviant from consensus, and get a sense of importance, which may lead to commitment and involvement.

Have any thought or question about your first steps in the journey of People Analytics? Please share it in a comment.

Featured in the book

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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 People Analytics: Your very first step in a long journey appeared first on Littal Shemer Haim.

(Reading Time: 4 minutes)

When I’m asked about my job title, sometimes I joke and say, “Eternal Student”. Practically, it is quite precise, considering the fact that most of my career consists of learning opportunities, which I happily grab. Aren’t we all? However, reflecting on the years, I’m pretty sure that the times I studied the most were when I was teaching.

Honored to be a statistical instructor at the local SPSS representative ten years ago, I guided corporate analysts to use SPSS software modules to deal with business questions. I recall my lectures, which included Statistics fundamentals, research methods, and data analysis practices needed to solve business challenges, as a rich learning experience for me. Being exposed to excellent learning materials, but mostly being open to students’ questions, which forced me to deal with different perspectives and illuminated dark areas of every subject I taught, all ensured that my teaching was a great way to learn.

Learn by teaching someone else

Obviously, my experience was validated by science: Teaching someone else, or even just pretending to do so, can help you learn. Studies revealed that when teachers prepare to teach, they tend to seek out key points and organize information into a coherent structure. Students also turn to these types of effective learning strategies when they expect to teach.

Encouraged by scientific proof, I’ve been sourcing the web for quite some time, searching for “People Analytics” colleagues who actually teach. I hoped to understand others’ experiences and figure out how teaching may enhance a career path in this field. Lately, I stumbled upon Sam Hill’s LinkedIn article, which summarizes what teaching “People Analytics” has taught him. Hill states the importance of generously sharing knowledge across the discipline. “Think and act open-source”, he says. In his writing, he kindly encourages practitioners to join conferences, events, and online communities, to advance their learning further, share their learning, and celebrate the success of others.

“Think and act open-source”… These words echo in my mind.

Share knowledge across the discipline

I was actually fortunate to experience the open-source culture when I started my journey with R programming. I was amazed to find answers to any of my peculiar yet common questions on Stack Overflow, a community of millions of programmers who really help each other on a daily basis. In addition, I found valuable help in R-bloggers, a blog aggregator of content contributed by bloggers who write about R. I would certainly not be able to practice R without this community. I can only imagine how super it would be to be able to ask and answer any question in the domain of “People Analytics”! How great it would be to practice coding on HR open data!

Indeed, there are excellent “People Analytics” groups on LinkedIn (e.g., People Analytics: Data-Driven HR), brilliant blogging platforms (e.g., Analytics in HR), and of course, I must mention, with respect and gratefulness, David Green, whose inspiring and comprehensive analysis are well known in this field. But all of this is not even close to the R community yet. Could open-source culture ever work in “People Analytics”?

Open-Source is where innovation happens

I believe the R community will outnumber the “People Analytics” community for a long time. But numbers are not the only condition for success. According to Matthew Mascord on OSS Watch, open-source communities may be extremely small. They start out either because someone wants something new to be built or someone intends to meet the future needs of others. In other words, open-source communities are where innovation happens.

It appears that this is exactly the case with the relatively new HR open-source initiative. Established by Ambrosia Vertesi and Lars Schmidt, this knowledge-sharing community brings open-source learning approaches to the global field of HR and recruiting. They seek to accelerate learning, close the gap between those who lead and those who lag, share best practices, learnings, and failures, and give permission to try, fail, and share.

However, it is reasonable to expect companies to object to such sharing. In today’s competitive environment, organizational data is strictly confidential. Nevertheless, there may be no real harm for business if someone shares with his professional community some “what, why, and how” with no data involved but rather pointing out general results, potential pitfalls, and key takeaways. Actually, Google does precisely that in re:Work, “a curated platform of tools and lessons, designed to help others use data and science to make things better”.

Focus on demand rather than supply

The open-source state of mind is much more complicated in “People Analytics” since practitioners must deal with a developing and immature field and internal clients within the HR department and business units. Although dozens of public case studies exist, our clients still need extra help defining the right questions and asking for the right solutions. Should open-source efforts be focused on educating the business community?

In the “People Analytics” field, focus on demand rather than supply was mentioned recently by Andrew Marritt, who develops empirically-driven HR leaders. Marritt suggests that instead of building a small team of highly capable analysts to provide a ‘supply’ of People Analytics, organizations can build ‘demand’ by making analytics-understanding a core skill in HR. Some of his activities are intended to show that techniques, such as predictive modeling or social network analysis, exist and what they can do. According to Marritt, “All analytic techniques require you to think about the workforce and HR in possibly different structured ways. Understanding them can even challenge your existing beliefs about how organizations work”.

Engage with local practitioners

Taking Marritt’s point of view into the potential open source arena of “People Analytics” led me to understand the importance of open discussion about the foundations of this field between analysts and their internal clients in HR. This can certainly be done at conferences and professional conventions. But for many, these events are too expensive or too far away. As an alternative, learning, and sharing within local meetups are more affordable and available opportunities. Meetups are quite common today for bringing people together to discuss things that matter to them, explore, teach, and learn.

Are there meetups for “People Analytics”? Absolutely! All over the world: New York, San Francisco, Boston, Detroit, Washington, Singapore, London, Zürich, Budapest, Antwerp… My next step was to find a meetup in my little corner of the world: Tel Aviv. Frankly, I really hoped to find one, but Alas! There was none!

So, would anybody in the Tel Aviv area like to pick up the gauntlet and join me in establishing such a meetup? Or, would anyone in the world who has already established a meetup for “People Analytics” like to share his experience?

The post Will “People Analytics” Be “Open-Source”? appeared first on Littal Shemer Haim.

Employee engagement is defined as an “emotional attachment” of employees to the company, and the efforts they are willing to offer as a result. Measuring employee engagement in a survey may determine how avid employees are, regarding their jobs and roles, how valued they feel at work, and how they consider the company’s values and missions. Employee engagement is associated with desired business outcomes, including productivity, creativity, customer satisfaction, and profitability.

Employee Engagement Surveys are conducted annually, among the entire workforce in the company. Traditionally, the survey analysis and results include a measurement of employee engagement and organizational practices that may elicit or influence it. In addition to causes, the analysis provides insights in regards to certain employee groups (organizational, sectoral, or demographic groups).

As a diagnostic tool, the questionnaire of the Employee Engagement Survey is relatively long, in comparison to other pulse surveys or ad-hoc organizational surveys. Since the survey objective is to identify employee engagement issues and determine where in the organization they arise, all metrics, analysis, and finding reports are more complex and sophisticated in comparison to other organizational researches.

A successful administration of the Employee Engagement Survey will include three essential processes after the data collection phase is completed: Multi-variate statistical analysis, Visualization as a foundation of organizational discussion, and Follow-up procedures and practices.

1. Multi-Variate Statistical Analysis

An incomplete or improper analysis process might produce incompatible outputs. Therefore, the results might never get discussed or used. The following short and non-inclusive list of procedures is intended to assist the survey sponsor as a systematical survey analysis checklist.

• Noncompliance: Analyzing survey and item response rate, identify noncompliance among certain employee groups, and within certain item subjects (missing values).
• Demographics: Analyzing respondent characteristics, in comparison to the organization, examining how overall participants represent the organization.
• Re-coding: Combining response categories when applicable, content analysis of qualitative responses.
• Measurements: Analyzing the validation and reliability of items that theoretically comprise metrics (using analytical tools, e.g., Factor Analysis), computing metrics, analyzing central tendency, and variation of new metrics.
• Organizational norms: Comparing metrics of certain employee groups (departments, sectoral or demographic groups) to overall organizational metrics, in both central tendency and variation.
• Metrics associations: Exploring correlations, and inferring relations by Regression and other statistical models.

2. Visualization – Foundation of Discussion

The most important benefit of survey results visualization is access to complex findings in a simple and “digestible” manner. Rather than simplifying issues, the visualization presented in a findings report enables a more powerful demonstration of relations between different variables and metrics. A visual presentation of survey findings encourages the audience to compare results, and notice trends and patterns using the eyes, and think about the meanings. This is the best way to “tell the story”, to create an impact, and start a debate. Visualization may include charts, perceptual maps, and a combination of qualitative and quantitative results (“info-graphics”).

3. Follow-up procedures and practices

A survey is a tool of communication between employees and management. The employees, who have already completed their perceived part of the communication, are expecting now a managerial response. The company’s managers, who were active in the survey planning, and have supported employee participation in the survey, are expecting now to gain reports and managerial tools, and may also want to be active in discussions, conclusions, and decision making. The following outputs are essential to fulfilling the expectations of all parties in the organization. Avoiding them may cause disappointment, resentfulness, anger, and other negative sentiments, that would harm the implementation of the survey results, and even reduce future participation in surveys and other voluntary organizational processes.

• Managerial reports: Combining survey results with other company’s metrics and outcomes, and offering reports to C-level managers, according to the organizational chart (by departments).
• Employee communication: Presenting a short summary of relevant findings, according to the management intentions, preferences, and plans, using infographics.
• Follow-up survey plan: Tracking plan, to explore changes in most important issues and/or employee groups, by pulse surveys.
• Improvement of questionnaire design: Following the validity and reliability measures previously conducted, and according to the best practice of questionnaire design (namely, avoiding biases in the questionnaire wording and item order), an improved version should be presented for the next year Employee Engagement Survey.

The post Employee Engagement Survey: 3 essential processes that follow data collection appeared first on Littal Shemer Haim.

The “People Analytics” domain is gaining a lot of attention worldwide, though most of the people I meet in the Israeli HR arena have not decided yet to formalize such a role within HR departments. I believe that times are changing, and new practices will surely shake the state of affairs. Meanwhile, data scientists can keep offering external consultancy for HR analytics in the Israeli market. Realizing that at least 40% of the workforce will be freelancers in the next few years, as many studies predict, data scientists can continue to develop their skills to work and manage without borders, particularly in the domain of People Analytics.

The decision to outsource HR analytics activities has been long discussed. There are many reasons why a business should consider hiring an external data scientist, e.g., quality of work, tools, costs, and schedules. But the key consideration is the fit of the analysis project with the long-term strategic planning of the company. Unfortunately, HR analytics doesn’t play a key strategic role in many businesses YET. Hence, many companies choose not to develop internal HR analytics capabilities, but rather buy the expertise, and thus save tremendous resources.

 

 

HR ability to run analytics

 

However, had a local HR group decided to include a People Analytics position, would it be capable to expansively run the analytical processes in-house? I doubt. Apparently, even among large corporations, which spend huge amounts on people analytics, the progress of HR analytics is pretty slow. DDI Research, for instance, found that HR tends to focus on metrics with little meaning outside the HR function. Therefore they lack credibility to create models that connect talent metrics to financial outcomes. Moreover, HR is less adept at communicating business terms and using storytelling and visualization in its messaging, essential skills for exploring and explaining any outcomes of an analytics project.

Nevertheless, with the perspective of traditional Job Analysis in my mind, I keep encountering some new people analytics positions here and there, mostly within local representatives of leading companies in tech industries. Enthusiastically, I explore the combinations of skills and responsibilities, and as much as possible, the processes which this role is involved in. As Josh Bersin stated, “People Analytics, as a business discipline, has arrived”, and there must be a growth in this market. But what kind of leaders are emerging?

The People Analytics leader is in charge of combining all the data of people in the company, in order to deal with business challenges, e.g., sales productivity, retention, and customer satisfaction. This leader must understand all employee data and its impact on business performance. It goes far beyond HR kinds of soft metrics. The leader must understand not only data management, statistics, and visualization, but rather the professional language of partners within the company, who can assist in implementing the analytics insights. So, should companies start looking for Unicorns?

 

 

A multi-disciplinary role

 

Obviously, the People Analytics leader is a multi-disciplinary role. In that view, new discussions, among talent acquisition professionals, are emerging. In a new podcast about building the People Analytics team, the People analytics leader was described as a person who has a combination of strong qualitative and analytical skills, along with emotional intelligence and behavior insights. Data Science background, a solid understanding of Statistics and programming skills, are only part of his qualifications. As a “knowledge worker” the People analytics leader must have the abilities of conceptual thinking, forward-thinking. He must also handle ambiguity and complexity. Those abilities enable him to define the right questions, understand the information needed, structure problems in terms of factors and variables and anticipate outcomes of different choices of action.

While People analytics leaders aren’t all over my professional environment yet, I believe that my modest contribution is to preach the gospel to HR departments. Awareness is a primary step for change, and the change I hope to see is many new professional partners, in the domain of People Analytics, within HR departments. Hopefully, my own experience, along with what I learn from leaders in the field, would be a useful resource for those who are struggling with the recruitment and the starting activities of the first People Analytics leader in their organization.

 

References:
Dan Schawbel, “10 Workplace Trends You’ll See In 2017“, www.forbes.com
Erik Van Vulpen, “The Myth of the full-time data scientist“, www.analyticsinhr.com
Evan Sinar & Rich Wellins, “Gaps in Both Will and Skill Explain HR’s Struggles with Analytics“, www.ddiworld.com
Josh Bersin, “People Analytics Market Growth: Ten Things You Need to Know“, joshbersin.com
Gil Press, “The Hunt For Unicorn Data Scientists Lifts Salaries For All Data Analytics Professionals“, www.forbes.com
Paul Edelman & Michael Housman, “Episode 011: Paul Edelman – A conversation with the Founder of Edelman and Associates” soundcloud.com

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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The post Who are you, my fellow “People Analytics Leader”? appeared first on Littal Shemer Haim.

I’ve witnessed a lot of interest in the domain of People analytics this year, among HR and other C-level managers that I met in Tel Aviv. At some point in any networking conversation, I’ve always been asked to explain how different People analytics is, from the traditional Organizational Research that I’ve conducted for more than two decades now. A comprehensive definition of the “People Analytics” field would be useful to clarify the difference. Unfortunately, I keep finding myself lost in vague and exhausting explanations since it is still hard to describe the complexity of People Analytics in one or a few clear sentences.

People Analytics is a growing field. Yet, it is not mature. Many terms in this domain are quite fluid, especially among HR practitioners. I believe that we still lack parsimonious definitions that would lead us through many blurred concepts in this professional raising area. Yet, I keep looking.

A search for a definition

Definitions are found all over the web, you may say. Certainly, Google will give you 27 million results in less than a second. The first in my google search would be “Technopedia”, which refers HR analytics to “applying analytic processes to the HR department, in the hope of improving employee performance and therefore getting a better ROI.” According to this definition, the objective is to provide insight into business processes through data analysis, and base relevant decisions on data, for improving these processes.

However, this short definition is neglecting many features of People analytics in practice. For instance, it ignores the massive amount of employee data, which is not directly related to business processes and performance but may raise an important understanding of it. “Bersin by Deloitte” uses the term Talent analytics to clarify “the use of measurement and analysis techniques to understand, improve, and optimize the people side of the business.” Its definition details many kinds of data, e.g., demographics, job history, training, assessments, and compensation, which “can be correlated and matched to many different types of business data to help companies to understand profiles and behaviors which create high performance”.

Bersin’s definition is indeed broader. Nevertheless, it implies almost nothing about the time point of view. Specifically, do we use data elements for inference (i.e., explaining past or present results), or do we use them for prediction (i.e., learning something about future results)? Although Bersin suggests a framework to measure the analytics maturity, starting low at “operational reporting” and ending high at “predictive analytics”, its core definition actually does not include the time perspective. The importance of time perspective is nicely stated in the book “People Analytics in the Era of Big Data: Changing the Way You Attract, Acquire, Develop, and Retain Talent”, by J.P. Isson and J.S. Harriott. As Isson and Harriet put it, “People Analytics has the most impact on the organization when it is forward-looking – not backward-looking. In other words, it is most useful when it is predictive and provides a lens into the future regarding likely business outcomes”.

Five perspectives of an expanded definition

So far, just by scratching the surface, it is clear that People Analytics is much beyond the HR department scope, as opposed to old-school organizational researches. It is about business performance in general, it is involved with different kinds of data, and it is relevant not just for inference but rather for prediction. However, these definitions of People analytics are not sufficient, in my opinion, to fully distinguish between Organizational Research and People Analytics.

The complexity of the People Analytics domain, let alone the complexity of its parsimonious definitions, is clearly demonstrated in an inspiring article, recently published by our colleague Lyndon Sundmark. Trying to answer the huge question of “how do I get started in the field of HR Analytics”, Sundmark lists the building blocks of People analytics, which domain experts must be familiar with. These building blocks include HR functions and business processes, Information technology and HR information systems, Business statistical analysis, HR measurement and metrics, HR operations, HR decision making and policies, and Data Science framework.

Indeed, a multidisciplinary long-long list. Reading Sundmark’s article, I suddenly realized that the definition which I’m looking for should have some kind of a top-down structure, describing People analytics through different organizational perspectives. A top-down structured definition may emphasize not only the complexity of this field but also its vast influence on different aspects of the activity in the organization.

Explicitly, I suggest to include five perspectives in any definition of People Analytics: Starting from C-level and business perspective, go through HR processes and IT and HRIS, and end-up with a Data science perspective and the role of the People analytics lead:

I believe that such a scheme may serve as a diagnostic guideline when approaching a new organization (and not only be of assistance next time I try to explain the differences between People Analytics and the traditional Organizational Research domain). Notice that in effect, each level in the suggested structure is influencing and being influenced by the nature of the level on its top. Hence, such a guideline may deepen the understanding of the organizational challenges in regards to People Analytics, and point to the core opportunities for different roles in the HR team.

So, how would you define People analytics? How does this definition structure would facilitate it? I welcome your suggestions in comments.

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 The Complexity of People Analytics Resolved: 5 Perspectives of Definition appeared first on Littal Shemer Haim.