Visualization is crucial in a People Analytics project, both in the data exploration phase and the reporting. However, from my experience and endeavor to promote People Analytics practices among HR professionals, there is another advantage to compelling visualization.

Most HR professionals are still not data-savvy, and frankly, some may have aversive responses to tasks involving data analysis. However, an appealing and powerful visualization creates attention, curiosity, and enthusiasm that may assist in overcoming those barriers.

To demonstrate how visualization is helpful, I picked a relatively common topic in People Analytics: Employee absenteeism. In the following sections, I present two types of hypotheses regarding absenteeism, specifically, how employee background and work characteristics are related to absenteeism.

I used R to explore the data. However, I don’t expect HR professionals to do that necessarily. But I do stress that they become good clients of data scientists. So I hope that being exposed to such visualization possibilities will help them set expectations and demands. (Readers interested in exploring the R code of the following visualizations are warmly invited to visit my GitHub profile).

Employee Absenteeism Open Data

I found the data of this demonstration in the UC Irvine Machine Learning Repository. The database was created at a courier company in Brazil. It includes records of absenteeism from July 2007 to July 2010. Variables in this dataset encompass time and duration of absence, employee background (distance from residence to work, service time, age, education, social drinking, social smoking), and work characteristics (workload, hit targets, disciplinary failure).

The Structure of this data set is unique. It contains 740 rows and 20 columns. Each record represents an occurrence of absenteeism due to a single reason, measured in hours. Therefore, each employee may have multiple records, all marked with the same employee’s ID and summed up. 

The dataset was used in academic research at the Universidade Nove de Julho – Postgraduate Program in Informatics and Knowledge Management. The data creators are Andrea Martiniano, Ricardo Pinto Ferreira, and Renato Jose Sassi.

A special thanks to my colleagues who wrote the open book HR Analytics in R and brought this data set. However, my approach in this article is different from theirs. I aim my analysis towards actionable insights, as if my clients are HR leaders, rather than simply exploring the data for analysis. Therefore, all variables are considered predictors in creating the following visualizations, while absenteeism is the outcome.

Obviously, an actual project will include additional multivariate statistics and statistical models. Such analysis based on this dataset may become my next article. However, I added in most visualizations a remark for further critical thinking.

Absenteeism and Employee Background

Does absenteeism in the Brazilian courier company relate to its employees’ background? To be more precise, can we point to certain employees groups who are more prone to be absent? And maybe intervein among these groups?

In this part of the analysis, I explored employee background variables, such as age, tenure, body attributes, social behaviors, and family coincidence. Obviously, I tried to leverage whatever variables I could find in the data. However, People Analysts who work with actual data in their organizations may discover many more relevant variables. In addition, other variables in this dataset are not typical in organizations.

Employee Age and Tenure

How absenteeism, measured in hours, is associated with employee age or tenure? To explore the relationship between these three numerical variables, I suggested a plot that captures the distribution of each variable, scatters each pair of variables and presents the correlation.

As clearly shown in Figure 1, absenteeism is not related to age or tenure in the courier company. Notice, however, that age and tenure are correlated in this organization. It may not be the case among other occupations and organizations. Furthermore, the density plots that enable you to get an impression of the shape of the distribution of each variable may also be unique for this case study.

Employee Body Attributes

Does absenteeism associate with employee weight, height, or body mass index? Not at all, according to Figure 2. It may be nonsense to explore this in the context of an organization, and obviously, you don’t collect such data in most occupations. However, since the data set includes those variables, why not explore them? The following exploration is precisely the same as the previous one. Since I already had the code, I could quickly reproduce the visualization.

Notice that some employees in this data set are obese. If we find a positive correlation between absenteeism and weight, it doesn’t necessarily mean obesity causes absenteeism. Correlation does not imply causation. The alternative direction of the relation is possible too: People who tend to be absent gain more weight, for some reason. Either way, it’s not the case here.

Employee Social Behaviors

Do social smokers and social drinkers tend to be absent more than non-consumers? Here is where the results get interesting. To compare these types of employees, I suggested box plots that capture the median, quartiles, and outliers of each group’s distribution. Box plots are very common and useful for data exploration. It would be best to get used to them because there is a higher chance that your data scientist will use them.

As you can see in Figure 3, social smokers tend to be absent less than non-smokers. However, Social drinkers tend to be absent more than non-drinkers, as seen in Figure 4. How can we explain this? Is this because drinking makes you wake up with a hangover and because you prefer to smoke and slack with your coworkers?

Employee Family Members

Does having kids or pets influence absenteeism? Here is where the results get pretty confusing. Let’s explore the visualization first and then resolve the confusion. I suggested the box plots again to compare parents and non-parents, either parents to kids or pets. According to Figure 5, employees with kids tend to be slightly more absent. However, this is not the case for employees with pets, as shown in Figure 6, who tend to be less absent. Why is this visualization confusing or even misleading? As opposed to the former visualization, here it appears that we have a dependency between the two classifications. For example, parents may have both kids and pets. We don’t know how this confounding possibility affects each of the visualizations. I recommend always keeping your analytical mindset, and critical thinking activated. (Hint: ask for additional inferential statistics, e.g., Chi-square test. But this is beyond the scope of this demonstration).

Absenteeism and Work Characteristics

Does absenteeism of employees in the Brazilian courier company associated with work characteristics? For example, are there situations or circumstances in which employees are at higher risk of absenteeism? What should we know about working conditions to prevent or reduce the risk?

In this part of the analysis, I explored work characteristic variables, such as workload, commute distance, hitting targets, disciplinary failures, and seasonality. Again, the variables in the dataset set my analysis boundaries. However, People Analysts should explore many more variables of work characteristics, which the HR department or the line of business may own. 

Absenteeism, Workload, and Commute Distance

How do workload and commute distance affect absenteeism? Do these work characteristics influence the absenteeism of various employee types the same? It is reasonable to expect that both variables, heavy workload and long commute distance, cause increased absenteeism. However, as clearly shown in Figures 7 and 8, there are no such positive correlations in the dataset.

A deeper exploration of the data reveals that workload and commute distance affect absenteeism differently among employees of different education levels (see Figures 9-10). The workload is positively associated with absenteeism, but only among mid and high-education employees. Commute distance is positively associated with absenteeism, but only among employees with high education. Sometimes a trend that may appear in subgroups disappears when groups are combined. This phenomenon is known as Simpson Paradox.

Absenteeism, Hitting targets, and Disciplinary failures

Do less disciplined employees are different from disciplined ones in their absenteeism in various target hit levels? According to Figure 11, they are not. I picked this lollipop plot to mirror or present the two distributions against each other. Frankly, I also wanted to be more creative and sweetly go beyond the standard bar plots.

When Absenteeism Occurs? Months and Seasons

Is there seasonality in absenteeism? Apparently Yes. According to Figure 12, absenteeism peaks in March and July, and its lowest point is in January. Not surprisingly, Mondays are at the highest risk of absenteeism.

Understanding such trends is essential for workforce planning. Unfortunately, the dataset does not include years, so Figures 12-13 represent the total hours of absenteeism in the entire research period. Otherwise, it would be interesting to explore the trend as a time series. However, a heatmap that presents total absenteeism hours by day and month adds an insightful perspective. As shown in Figure 14, each month has a unique pattern of absenteeism over the working week.

The post Visualizing Absenteeism At Work appeared first on Littal Shemer Haim.

The post Who owns the HR data strategy? Not analysts or vendors appeared first on Littal Shemer Haim.

So, your HR data is dirty. It includes issues such as missing values in people’s information, typos that corrupt categorical variables, wrong labeling, duplicate records, errors, or records that you neglect to update. And so, you live with that. You accept the inevitable reality that you’ll never have the perfect data in HR. Sadly, the messy data in HR is sometimes an excuse to avoid People Analytics projects, hence, missing the opportunity to impact. However, HR data cleaning is part of your People Analytics Journey.

Start with imperfect data assets

But what if you start with what you have, i.e., your imperfect data assets? Your data quality must not be a barrier to a project. On the contrary! An analytics project is a practical step towards better and cleaner data. Whether you take a DIY (do it yourself) approach or implement a people analytics solution, experience shows that practicing People Analytics will improve your data quality. After all, no one cleans data just for the sake of it. However, when you have a purpose derived from a business challenge, and your priority to gain visibility into your workforce insights is high, you’ll be motivated to put the time, effort, and resources into data cleanup.

Clean and Tidy data is a milestone in your analytics project

Data preparation is a part of every analytics process in each vertical of your organization. But no one taught you that when you took analytics classes. The datasets you’ve been practicing were perfect and ready for analysis. In real life, you don’t get readymade, tidy data. It is a milestone in your analytics project, but you must get there yourself. From my experience in many quantitative types of research in organizations, this is an effective way of engaging with your data, understanding it, and reaching the most in-depth acquaintance with it.

Systematic errors lead to new procedures for data maintenance

Right after the crucial stage of transforming a business question into the hypothesis that comprises your analytics project and spotting relevant data sources, you pull and merge datasets and start exploring them. The exploration phase starts with finding gaps in data quality and fixing them. You will indeed find systematic errors. However, it will eventually lead you to propose or come up with new procedures to maintain data integrity and new configuration of HR platforms that your HR department will embrace later to prevent or continuously reduce errors.

Therefore, you should not be intimidated by the entire data lake of the HR department. Instead, focus on cleaning the datasets of your analytics project. When I meet HR professionals that have analytics questions on their minds, I am confident that there is a higher chance that they will find a way to improve data quality. So, the win is double! You gain less burden in cleaning up only the data you need, but HR data quality is constantly changing for the better!

How far should you go in data cleaning?

If you reached this point, you are probably raising two additional questions in your heart: how far should you go? And how to do it the right way? The first question is hard to answer; therefore, my obvious answer as a consultant would be: “It depends.” In workforce analytics, data accuracy may be more critical in some cases, e.g., when predicting personal outcomes. However, there are many cases in which you study groups and deal with means as estimates. In such cases, you can handle the inaccuracy and make the appropriate notes and reservations about it. The expectations about data quality should be an ongoing discussion that depends on the context of the data usage. The data quality definition will include accuracy, completeness, consistency, integrity, reasonability, timeliness, deduplication, validity, and more. Again, it is based on the context of your analysis.

The second question, regarding the best practice of data cleaning, is easy to answer. I incorporated the data cleaning best practice into my workshops to teach you how to fix your data and ensure your data handling is well documented and your analytics project is reproducible.

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Did you ever cut an onion while cooking? If you did, I bet it made your eyes tear. The well-known burning sensation in the eyes is simply a reaction to the sulfur that spread in the air when you destroy the onion’s cells. But what if you peel the layers of the onion one by one? There will be less damage to the onion’s cells, and therefore, fewer tears in your eyes.

The hierarchical definition of People Analytics

People Analytics is just like an onion. This domain of expertise has many practical layers. Its hierarchical definition includes at least five perspectives: C-level and business perspective, HR processes, data in HCM and other HR-tech platforms, data science methods, and the daily activities of the People analyst. If you try to cut through the entire hierarchical structure of this multidisciplinary profession, it will be so hard to grasp, that it will make you cry. Well, at least metaphorically. However, if you explore the layers of this definition one by one, you’ll get a thorough understanding that eventually will enable you to impact your organization – Tearless guaranteed!

The variety of roles that are involved in any People Analytics project within the organization contributes to the complexity of this practice. Notice that in effect, each layer in the structure of People Analytics definition is influencing and being influenced by the nature of the layer on its top and bottom. For example, the data stored in HR-tech platform influences but is also influenced by the HR processes that generate it. This complexity implies challenges in People Analytics activities.

The language of business

A crucial part of your challenge in People Analytics is the effort to establish communication between different professionals. In the hierarchy illustrated in the People Analytics definition, you have a C-level perspective above HR processes and data science beneath. Therefore, you must ensure communication between two kinds of professionals: executives and data experts. I consider this communication as an important layer to peel, in the onion metaphor.

The People Analytics journey enables HR managers to become more strategic because they speak the language of the business. Obviously, they must do so, since People Analytics is all about impacting the business by the right questions and insights derived from people’s data. However, they can support decision making, only if the people who are in charge of data science projects can communicate effectively with business leaders.

The role of the People Analytics leader is considered sometimes as a translator, the enabler of this communication. The People Analytics leader must make sure that the data scientists understand the business needs in workforce-related analysis, come up with the right business questions to analyze and return with the best storytelling with data. The People Analytics leader must also make sure that the owners of HR operations, who may be in charge of BI in the domain of workforce, understand the needs of data consumers among the executives.

Demystify People Analytics

On the other hand, you have executives. They need support too, on their journey to the data-driven organization. Perhaps they need you, the People Analytics Leader, to demystify this domain for them. Business leaders may be familiar with quantitative methods in other domains, e.g., Marketing, Finance, and Operations. But how deep is their understanding of statistical models and algorithms in the field of the workforce? Do they really know how to interpret the insights derived from the shiny tools and methods of People Analytics to the right decisions about people, careers, and employee experience? They surely can benefit from learning some new terms, to avoid the inconvenience experience involved in misunderstanding concepts, methods, and technologies.

Democratizing data is a process, not an outcome

Enabling the communication between the data professionals and the data customers is actually a part of the process of democratizing data in your organization – a significant part of preparing a vital portion of your workforce for the future. Though democratizing data is relevant to all parts of the business, the domain of workforce apparently lags. Implementing tools for telling stories with data within the HR department is important, but it is certainly not enough. The gap in communication must be close too. Closing the gap will enable the process of getting the business question right, ensuring data integrity and transparency, iterating to find the best algorithms, and getting people to use the insights in their decision making.

Therefore, the HR leaders, who are also responsible for learning, must lead simultaneously, toward data literacy among the management and toward understanding the business among the data pros. People Analytics is not about software or cloud services. It is a mindset that should become common throughout the entire organization. Closing the communication gap between executives and data pros is an important part of educating the workforce, and it may save a lot of burdens, just like peeling the onion’s layers, one by one.

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 Your journey to People Analytics makes you cry? appeared first on Littal Shemer Haim.

Workforce data are the molecules of People Analytics. No predictive model, diagnostic analysis, or visualization can possibly be created without proper and relevant data. Anyone who appreciates the advantages of data-driven HR should stress quality in HR data. However, when I start a conversation about data with HR leaders, many of them spontaneously respond with a sigh. They know the naked truth: HR data is a mess! Nevertheless, there is so much that HR leaders can do to cope with this challenge, starting today. Let’s start with the following six suggestions, which hopefully will inspire us to face this painful issue.

 

1. Understand the advantage of workforce data access

Workforce data is everywhere in the organization: HRIS, ATS, CRM, LMS, etc. Business leaders need insights, which derived from that data, to improve business performance. A huge variety of technological solutions are available today, which enable HR people and other non-technical professionals to create insights from the data. The missing link is a desire to access the data, and to use it in actionable ways that reveal new opportunities for the company. The ability to access the data and use it properly will empower HR people to have ownership and responsibility of workforce data, and encourage them to maintain data quality in order to support informed decisions in the organization. Data democratization is a demand for many business domains. There is no reason it skips over HR. Therefore, HR leaders should consider the right tools and training to keep their team’s progress on this journey.

 

2. Understand the complexity of workforce data

Workforce data may be scattered on many platforms, both in the HR department and in different lines of business. It comes in many formats. Parts of it are structured, while other parts are unstructured, e.g., text fields from employee reviews. Sometimes, the data is not recorded digitally, due to certain difficulties or priorities. In other times, when the data did get recorded, old records are deleted or replaced, due to database structure constraints. Different users may have different needs, which a shared platform does not support, therefore some of them may keep supplement records, e.g., in Excel sheets. Furthermore, when new needs emerge, relevant data may be recorded elsewhere, in different systems. Hence, one of the most challenging issues is the different unique identifiers in different data sources, which sometimes makes it impossible to automatically combine data by matching field. Understanding the complexity of workforce data is the first step to deal with it. HR Leaders must start to get to know workforce data as much as they understand HR processes.

 

3. Prepare to improve workforce data

The struggle toward data integrity is worthwhile. It yields high-quality data that enable meaningful analytics. HR practitioners should configure their systems in a way that prevents or reduce errors. For example, they may want to eliminate mandatory requirements for fields that are not always available at the time of data entry, consolidate fields with duplicate information, and remove fields with no immediate purpose. When analytic questions are on HR people’s minds, higher the chances that they configure their system in a way that contributes to improved data quality. However, some of them still need guidance in system configuration and data entry processes.

 

4. Prepare to integrate data from different sources

Throwing all the data into a data lake and hoping for an amazing insight to emerge is a nice fantasy that is about to fade away. Instead, you must pick an important business problem to solve, identify and gather relevant data into the data lake, which will include HR structured data, HR unstructured data, and a variety of data from different lines of business. This involves huge challenges: First, you don’t want to disrupt anything in your business processes. Secondly, assuming you found the data, you must deal with duplications, versions, incomplete data, and issues of unique identifiers. And finally, you must do it fast enough, to face managers’ demands, in accordance with organizational and business challenges. You may find out that IT is not available to help with your initiatives, and worse, IT may lack the HR context to understand the data. Therefore, HR leaders should start reassessing their platforms and exploring the ability to integrate them with other solutions, e.g., their ATS and LMS. They must also be aware of other tools that may be needed: blending data tools (e.g., Alteryx), advanced statistics tools (e.g., R programming), and visualization tools (e.g., Tableau).

 

5. Prepare to build stakeholders’ trust

Data scientists and People Analysts usually have a hypothesis about the subject in question. In other words, before they dive into analysis, they acknowledge their expectations about the results. They must start their exploration with a question in mind, otherwise, they would not know where to start in the infinity of analytic directions. However, this is not always the case with other stakeholders – employees and managers. They may be surprised, shocked, confused, or embarrassed when exposed to the findings. Therefore, it is important to know in advance something about their expectations, attitudes, and beliefs. Whether the analysis supports or disproves stakeholders’ expectations, the analyst should dig deeper into the data, to provide supporting details. An analyst who anticipates potential questions and concerns can be better prepared with answers and contributes to stakeholders’ trust.

6. Remember the cause: Serving the organization’s goals

For HR to take a strategic role in management, it needs to broaden the scope of its analytics agenda to business questions. By blending people’s data with business data, HR can provide insights beyond HR metrics and may answer questions such as: How good is the workforce in executing the business strategy? It can start to analyze the connections between employee behavior and productivity, predict business outcomes by competencies, and measure the impact of various training programs.

I believe that any HR leader experiences these six angles in the ride to data-driven HR, but the mixture and volume depend on the phase in the journey. Any other suggestions? Please share it in a comment.

References:

Bernard Marr, “What Is Data Democratization? A Super Simple Explanation And The Key Pros And Cons“, forbes.com
Vangie Beal, “Structured data“, webopedia.com
Stacy Chapman, “Silos in Talent Data – Sigh“, linkedin.com
Alyssa Ruff, “How Smart HR System Design Leads to High-Quality Data“, analyticsinhr.com
“Data lake“, en.wikipedia.org
Roger Nolan, “Digital Transformation: Finding Your Data is Half the Battle“, business2community.com
Eric Knudsen, “3 Rules for Building Stakeholder Trust in Your HR Data“, visier.com
Rupert Morrison, “Five mindsets HR needs to get right to deliver business impact“, http://blog.orgvue.com

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 Workforce data is a mess! What can you do about it? 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.

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The post People Analytics: Your very first step in a long journey 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

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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.