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.

The post HR data cleaning is part of People Analytics appeared first on Littal Shemer Haim.

Background

LSH: Thanks for joining us, Yael. Tell us a little about yourself, your background, your role in PA.

YE: The 1^st phase of my career was in health organizations. I have a Masters in Public Health (MPH) from the Hebrew University. My role as the coordinator of the non-clinical quality improvement program in a large tertiary hospital included many aspects of HR including change management, training, and recruiting. This led me to further my education in HR and to explore opportunities in the HR profession. In the 2^nd phase, I worked for a few years in a placement agency, and then, in the past 11 years, I worked at Microsoft. I started as a staffing specialist in the R&D center in Israel. 5 years ago, I moved to an HR analyst role in a new global team within the HR function (HRBI).

LSH: The People Analytics function at Microsoft is considered to be one of the leaders in the field. What can you share regarding the vision, mission, and principles?

YE: The vision is, in short, #DataDrivenHR. The mission is to enable Microsoft to make evidence-based decisions about workforce and culture. The principles of driving this mission include delivering insightful research and analytics, providing robust and consistent reporting tools in partnership with engineering teams, delivering timely and accurate measurement of companywide business and HR priorities, ensuring data quality, and upholding employee data privacy and security.

The role of technology

LSH: From your experience, what was the role of technology in the evolution of the People Analytics?

YE: Technology supports all aspects of people analytics. There are many examples: It promotes data security and privacy by ensuring the data is used only by authorized people. It enables the use of data by all HR professionals by an accessible format that is easier to understand and communicate even if you are not an analyst. Self-service data solutions, i.e., Microsoft PowerBI saves time and enable us to focus on deeper analysis rather than providing customized data needs. It also enables us to integrate data from various sources and create powerful data models, using visualizations during the analysis and for communicating insights and recommendations. We also can leverage data that wasn’t accessible before, by Workplace Analytics, a company product that enables us to identify collaboration patterns that impact productivity, workforce effectiveness, and employee engagement, based on data from Office 365. We also use text analytics to leverage a huge amount of data from responses to open-ended questions in employee surveys, objectively, and in several languages. More useful products of Microsoft are Yammer, which is an enterprise social networking service that helps us to communicate learnings and ideas, and Teams, which helps to manage resources.

LSH: Tell us more about your role: who were your clients, how did you support them?

YE: People analytics is an evolving field, and I was fortunate to partner with my colleagues in the HRBI team and with HR leaders and managers across the globe. Over time, I partnered with HR teams in both engineering groups and the sales organization. We leveraged an analytical approach to enable the business and HR to execute data-driven decisions in many aspects of the employment cycle, including hiring, headcount trends, diversity, retention, rewards, compensation, as well as candidate and employee sentiment. We partnered with HR to support ongoing HR processes as well as answering specific questions and hypothesis which were raised from their work with the business. One of the best practices was to set milestones during the analysis process in which we shared the work we did thus far, got the perspective and thoughts from our partners before we continued. This ensured that the deliverable answered the needs. Another major aspect of our work was promoting a data-driven approach in HR through one-on-one consultation, standard training on a data-driven approach, and tailored training on specific subjects.

LSH: What do you consider as challenges in your role with respect to technology?

YE: While it is a positive challenge, the ongoing development of knowledge in this field in general and of technology, in particular, requires ongoing learning. Ensuring that you take the time to learn with a very busy day to day work is challenging but also essential and very rewarding. Some other challenges are connected to the fact that technology helps with having more data available for use, thus increasing the need to make sure that we are using the data in an accurate way and to avoid bias throughout the analysis process. The availability of more data also raises the challenge of prioritizing work. One aspect of this is balancing between doing interesting analysis versus doing important analysis. Another aspect is balancing between the sense of urgency that is always driven by the business and the time it takes to do a thorough analysis.

LSH: What do you consider as a success story with respect to technology?

YE: These a great feeling of accomplishment when your HR partners share how they leveraged the tools and knowledge to promote a data-driven approach and seeing our work impacting business decisions. And of course, every time you succeed in a tough technical challenge with your data model or an effective visual you feel a great success. Personally, some of the meaningful cases of success were the use of technology, e.g., text analytics, to help in promoting general values of the unbiased approach, inclusiveness, and collaboration.

Data Ethics 

LSH: How do the people analytics team handle data ethics? Are there processes, partners in the organization, or outside?

YE: Microsoft has a volume of Privacy Standards dedicated to employee data, Data Use Framework for employee data, and Data Protection Notice for employees. Other internal tools and projects have more detailed communications. The company has created an Employee Data Governance Board to provide consistent company-wide direction and oversight on the legal and corporate policy issues reflected in the company’s privacy standards for processing employee personal data. This board is made up of a core team of privacy managers and attorneys for HR, Finance, and IT. Due to the need to combine employee data with business data, a data analytics governance framework was created and is used when embarking on a new people analytics project, to ensure that the right people are involved from the beginning, including legal, HR and any business stakeholders in addition to the people analytics team. We have a privacy manager on the team, who focuses on people analytics data privacy and security, and partners with other roles outside of HR on their use of employee data. We also have mandatory annual training for HR on privacy and data use, which is updated on a yearly basis. Training is helpful in framing the definitions and aspects of ethics, e.g., ensuring a purpose behind each data element and anonymization in reports.

LSH: What would you advise your colleagues whose employers are in an early stage in the field?

YE: Choose as your 1^st project subjects that are both important for the business and easy to succeed. Data is available regarding many aspects of HR. Use it to help with important business questions and with building the trust of the business in the data-driven approach. As long as you are aware of the limitations of the data you have, don’t be afraid of doing analysis with partial data. If we wait until we have the “perfect dataset” we will never start doing analysis. Also, showing the value from a partial analysis while being transparent regarding the limitations is a great argument for investing in more resources. As in any other aspect of HR, People Analytics requires ongoing continuous learning. Make sure you leverage resources and collaboration opportunities to continue learning.

LSH: Thank you, Yael! We are fortunate to have your perspective in our course and professional community!

The post The role of technology in the evolution of People Analytics appeared first on Littal Shemer Haim.

Educating and mentoring HR professionals to embrace the practices of People Analytics is a challenge. There are barriers, and it takes time and effort to overcome them. However, one issue remained unsolved for years: The lack of open HR data to practice on. Although there are many inspiring case studies of People Analytics, obviously, organizations don’t share their people data for the sake of learning. Simulation-based data may be an alternative, though usually it is oversimplified and lacks real or interesting patterns to explore.

 

A Practice with Open Data

In my recent teaching initiatives, e.g., the People Analytics session in Lahav Executive Education at the University of Tel Aviv, I wanted to demonstrate HR managers that their academic background, professional experience, and their common sense, is enough for exploring organizational occurrences and effects based on data. HR managers don’t have to become data scientists in order to conduct People Analytics projects. But they do need to communicate with Data Scientists, bring them business questions to study, and request research outputs. For that reason, I constantly search for open HR data and use it in learning sessions. Fortunately, I could present a case study of Gender Equality, that theoretically and methodological was based on a real project, but the analytics part was conducted on open data that was offered by other organizations.

For the Analysts and Data Science enthusiasts among my readers, it is worth mentioning that although it is not the first time I demonstrate People Analytics practices based on open data, this time my objective is a bit different. I did not use practical Machine Learning in this case study. The analysis process was based on research methodology and Statistics that a Bachelor of Social Science, i.e., someone with a B.A. degree, should understand and can comfortably communicate. Nevertheless, I used R for my analysis, because I believe that HR people who may not have learned or used R and manage to receive analytics from an inner supplier or an outsource service, should have a grasp on how a desktop of a Data Scientist looks like, and what in the functionality of R Studio makes it so popular.

My source and inspiration for the dataset was Montgomery County Maryland’s employee salaries in 2017. The open data included annual salary information such as gross pay and overtime pay for all active, permanent employees, and some demographics. The reason for opening this dataset to the public is the Digital Government Strategy of Montgomery County Maryland which aims to serve residents, employees, and other partners better. In this case, it serves the purpose of education, in an open-source community of People Analytics students, professionals, and enthusiasts. However, the dataset used is anonymized and randomized.

 

Gender Pay Gap

Pay transparency is among Global Talent Trends in 2019, according to LinkedIn. But “Transparency isn’t the goal. The goal is paying everyone fairly”, as Anil Dash, CEO at Glitch was wisely quoted in the report. Transparency forces Organizations to make sure they keep the compensation balanced across genders and other groups’ characteristics. Although people share salaries on sites like Glassdoor and LinkedIn, only 27% of companies are transparent about pay. The first step to establishing pay transparency, as recommended in LinkedIn’s report, is to conduct an internal audit, and explore how the company’s pay compares to competitors and whether it has a major pay gap across gender, race, and those in similar roles. If significant inequities are found, a detailed plan to fix them is recommended.

A pay gap audit or exploration may be a People Analyst’s task. However, in the People Analytics project, descriptive statistics is not enough. We need to go deeper into understanding the reasons for our findings and the directions for a solution. In the following analysis, I included some diagnostics and Inferential Statistics, to understand the reasons for the patterns in pay data. I assumed that as any American public organization, Montgomery County Maryland is subjected to some kind of strict regulation regarding equal pay. But only going beyond the basic descriptive statistics enabled me to find some interesting patterns. So, without further ado, let’s explore the findings.

Gender Pay in Montgomery County Maryland

“Telling a story with data” is almost a cliché in our field. Nevertheless, there is no substitute for the exploration of data visually, before moving on to test the hypothesis. There are plenty of visual tools out there. The great thing about R, however, apart from its price (free!), is the flexibility it enables in creating the story and reproduce it again and again as the data is updated. In the following description of my analysis, I did not explain every term in statistics, since I assume the readers learned them on their undergraduate studies. But “no one remembers”, right? So, the links in every statistical term may walk you through a “memory refreshment experience”, if you choose to follow them. 

I started my exploration, as shown in Figure 1, with the pay distributions. I intended to present, in a single slide, both common and separated gender pay distributions. I also wanted to explore both indications for center and dispersion, without losing information about outliers. So, I placed a boxplot near a histogram with a density plot and ordered the genders vertically, one on the top of the other, so the comparison would be easy for the bare eye.

If you look closely in Figure 1, you’ll notice a little difference between men and women, both in the deviation of histograms from the shared distribution, i.e., that normal approximation curve, and the center of the boxplot, which represent the median. Running t-test resulted in a p-value below 0.05, which means that on average, the pay differences between men and women are statistically significant. This significant result is impacted by a large number of cases in the dataset (about 9400 employees). The average yearly pay gap is about 4.5k US$. (I repeated the visualization and t-tests for all pay variables I had in my dataset, but for the purpose of simplicity, let’s remain with only one variable).

 

Figure 1: Gender Pay Distributions

Obviously, the average pay gap is not the whole story. Additional variables should be added, to deeply understand the source of the gap. Adding background variables, e.g., full vs. part-time job and tenure may change the story. For the analysis presented in Figure 2, I had to create new variables based on the raw data. I mention it because it is important to take into consideration that, usually, the data you download from your systems won’t be ready for analysis. A significant part of the Data Scientist time will be invested in cleaning, mounting, and preparing the data for the analysis.

Exploring gender pay averages across tenure ranges reveals that while both genders are promoted while gaining tenure, men are promoted with higher rates, as the different slope indicates. Running ANOVA reveals that the interaction between the gender and tenure variables is significant, meaning that the different slopes are not a random occurrence. Such interaction was not found between gender and full/part-time. However, we do witness full-time employees promoted at a higher rate, in comparison to part-time employees, as slops indicate. This interaction, between full/part-time and tenure, is also significant.

 

Figure 2: Gender effect, Tenure effect, Full/part-time effect

 

But who holds most of the part-time jobs? Apparently, the proportion of part-time employees in Montgomery County Maryland is significantly higher among women (18%), in comparison to men (3%). In other words, the accumulative gap between men and women throughout their careers, as they gain tenure, may stem from their assignment in full and part-time jobs. In a Linear regression model that explains the annual salary by gender, assignment, and tenure, the gender is not a significant predictor, as opposed to the other variables: tenure and assignment. Together these variables explain 37% of the variance of annual pay, which is a fair result, but still, other factors impact it too. Positions and occupations may be among those factors.

Indeed, a critical reader may raise a question about the male’s and female’s occupation. The dataset includes some occupations with both genders and other occupations with only men or women. I repeated the whole analysis after screening out those male and female occupations, and I got similar results. Yes, analysis within each occupation is also needed. However, there are 390 occupations in this dataset, so I prefer to leave this task to People Analysts in Montgomery County Maryland. (For dynamic charts of this case study, by departments for example, please visit my GitHub)

 

Additional thoughts

The gender pay gap analysis in this article is straightforward. Most HR managers with a B.A. education can handle it, with a little help from a data scientist on some occasions. I encourage HR practitioners who start their journey in People Analytics to practice this analysis. The data is available, and the insights may be vital. According to Gartner’s Digital Employee Experience Survey in 2018, #1 in the top ten memorable experiences that affect employee experience is “Being discriminated against at work”.  No doubt that transparency and closing the pay gap is crucial for employee engagement and indirectly to employer branding.

My last note may be the most important. Women still don’t get their fair share, according to an analysis by Visier. Data from this People Analytics platform reveals that the gender pay gap widened in 2017 rather than becoming smaller: In 2016, women made 81 cents to the dollar a man-made, but in 2017, women made 78 cents to the dollar, according to Visier data. Organizations still have a long way to go to close the gender pay gap, so why don’t you start by analyzing the situation in your organization?

(To explore the R code used in this article, check my GitHub).

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 Gender Pay Gap and People Analytics: A Practice with Open Data appeared first on Littal Shemer Haim.

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.

The post Key takeaways from People Analytics World, London 2018 – Part 1 appeared first on Littal Shemer Haim.

Mark is the CEO of a fast-growing company. He had fund-raised millions of dollars and received a lot of recognition in the press. From the outside looking in, Mark is a success story waiting to happen. Yet, he is having trouble sleeping. His company is about to release a major product, but many of his best engineers are leaving the company. He lost 24% of his R&D department in just two quarters. How did this happen? And how was he so unaware of the way his employees were feeling?

Getting to the root of employee turnover

The costs of employee attrition are high, especially when losing a high performing employee. They take with them extensive knowledge of the company, relationships within and outside the organization, specific business practices, and much more. The important questions Mark must ask himself now are: What is happening in the company that is making people want to leave,? Who can he go to for help? Is this something he should discuss with human resources?

HR and People Analytics

Unfortunately, many HR leaders have not acquired yet the tools needed to analyze their people data in the right way. They can hold conversations with their employees and try to make sense of certain trends, but in the end, they would have trouble providing Mark with evidence-based answers. This is where People Analytics is becoming increasingly important to growing companies, and particularly to HR. People Analytics expert explores, infers and communicates significant data patterns to initiate and support strategic business decisions related to people in the organization.

Common questions that people analytics can answer include: How can we find more top performers? How can we better engage our highest performing workers? Which qualities do amazing teams have? Which people are most likely to stay in the company or leave it?

Two things are happening in the domain of HR and People Analytics: First, it is being used to predict future behavior based on present data. Secondly, it is being used for the sake of both HR and other departments in the company. Gaining answers to these types of questions can save a company from significant loss.

Why many CEOs need the People Analyst?

In an increasingly competitive space for top talent, a new profession has emerged: The People Analyst. This analyst is in charge of combining all the data a company has on its people to tackle business challenges. Much of the work of the People Analyst can be supported by machines. Many HR-tech companies understand the importance of data in knowing how to manage and engage people effectively. For example, bob, an all-in-one, cloud-based HR and benefits platform, is making headway in this field. This platform consolidates all the rich real-time data of a company into one system and gives decision-makers valuable insights into their employees from this data.

What about privacy and data protection?

As more people are becoming aware of the importance of protecting their data, privacy and data protection plays an important role in People Analytics. Regulations are being put in place to offer rights over employees’ data: Employees must be informed about data usage, data transferring and period of storage. Anyone who analyses employee data will need to follow these regulations.

People Analytics can help

Had Mark hired a People Analyst who can access the right platforms and tools, he would have been able to track how his top performers feel and behave and could have taken preventative measures to stop them from leaving. If you are a company in growth mode, you know how crucial is to retain your employees. People Analytics serve as an important way for you to stay on top of everything that is happening in your company.

The post People Analytics in fast growing companies: Lead start-ups to data-driven HR 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

"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

Subscribe to the weekly newsletter to receive more featured samples and pre-order opportunities

Please leave this field empty

Check your inbox or spam folder to confirm your subscription.

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

Subscribe to the weekly newsletter to receive more featured samples and pre-order opportunities

Please leave this field empty

Check your inbox or spam folder to confirm your subscription.

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