What is the right productivity measures: time or outputs? The traditional HR department knows how to accountably measures its activity. The innovative idea behind People Analytics practices is not measurement, but the associations between HR activities and the business KPIs. Productivity is one of the most critical business results, and therefore relevant to projects and products in the domain of People Analytics. However, when I present these basics ideas of People Analytics to HR professionals at all levels, I often hear rejections.

The productivity of knowledge workers

I recall that a global VP of HR once mentioned that since she works in a tech company, she can not possibly hypothesize about the association between HR activities and line of business productivity. I advised her to raise the subject in discussion with R&D leaders in her organization. Like any other business leaders, they must be accountable, and for sure, they measure their productivity. The only question is, How?

However, I do agree with her that measuring the productivity of knowledge workers is more complicated. When we explore employees’ outputs in sales or customer success (for example, in a call center or a field role), outcomes are more straightforward. It’s relatively easy to derive goals and rewards from them. But to measure the productivity of someone who works in a team, in which skills, knowledge, and creativity of the entire team members serve together to reach a goal, is naturally more daunting. And yet, tech organizations do that.

One interesting example was presented this week in an Israeli panel, obviously in a virtual event, by Eldad Maniv, President & COO at Taboola. The panelists discussed “the good, the bad, and the ugly” of measurement in working from home in Covid19 times. But I’ll take only the good from what Maniv shared, as a remarkable example of measuring remote workers. For those of you who are not familiar with Taboola, this company employs about 1400 people in 18 locations worldwide and helps people find relevant content online. They have all worked from home since mid-March. Maniv demonstrated how working from home impacted company measures in R&D, such as Deployed Package and Resolved Tickets.

Time is a common denominator

Clearly, such productivity measures enabled the company to cope with the crisis that severely affected it. And so, we can conclude that R&D organizations know how to measure productivity, and People Analytics leaders can integrate such data into HR data and bring valuable insights. But it also emphasizes the idea that People Analytics practices serve the entire management, particularly Finance. CFOs should use talent data, especially in crisis times, to target the best ways of capturing ROI from people processes and well-being solutions.

 Although Maniv emphasized the outcomes in his discussion, it doesn’t mean that the company neglected to measure the facet of time. Indeed, and like many other case studies about working hours in covid19 times, the company experienced increased working hours during the day. For many reasons, people worked longer hours: the need to juggle between work and parenting duties, the lack of leisure activities outside, the pressure to demonstrate engagement and keep the employment status, and more.

Measuring outcomes is crucial, but time is, and always will be, the common denominator that enables us to objectively sum up the productivity of different roles in the organization. People at different levels of the organization should understand what proportion of working hours contributes to the company outcomes. We don’t spend the entire time creating direct contributions. Sometimes we’re socializing, other times learning, and many other human activities are essential to both individuals and the company as a whole but are not associated with outputs. If this time proportion, in general, is sufficient, we should keep it, otherwise improve it. Within it, we can be more productive and produce even more.

I’m a productivity prodigy!

Personally, I strive to boost my productivity both in outputs and time, and I continuously learn new hacks and use new tools for that. But I must also prevent my own burnout, and there is no one other than me to be in charge of my well-being. Working as self-employed makes me a unique case because I’m both the employer and the employee. However, it emphasizes the mutual responsibility of the employers and the employees. Both should discover the relevant means to enhance productivity without the burnout trap. Engaged employees are considered a blessing for any organization. But even employee engagement has its hazards.

What keeps me productive both in terms of outcomes and time? I want to mention two kinds of digital tools that everyone can embrace this way or another. My outputs as a writer and speaker are my content. While writing these words, I use Grammarly. It keeps my writing correct and precise, and also speeds it up. Grammarly counts each word that I write and reports the number of words checked weekly. It compares my result to previous weeks and other users. Over time, I get better (last week, I was a productivity prodigy!), and I can decide whether to adjust my goals and plans or take a rest.

If you read my blog, you know that it’s hard for me to take a rest. But to be productive over time, I control my calendar in reverence. That’s why I insist people in my network, with whom I’m thrilled to interact by Zoom these days, will use my Calendly.  My friends, clients, students, and colleagues book me when I’m less productive in creating content. It works for me and many others in SMBs and enterprises. For example, if you haven’t done so before, check the Microsoft case study that proves that you get more productive and happier if you wisely book your meeting. Indeed, in that sense, I sit on giant shoulders.

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Imagine the highest degree for sophisticated data usage. If there was such a degree, which organizations would be nominated to hold it? Undoubtedly, the Israeli intelligence corps would be at the top of the list. Could you imagine People Analytics practices in such an organization? Personally, I would love to have a sneak peek into the People Analytics function of this organization. Wouldn’t you?

I was excited to talk with Limor Pinto, a Lieutenant Colonel in the Israeli Military intelligence, who will retire in a few weeks. In her last role, she served as a Head of the Behavioral Sciences Branch in the Intelligence Corps Headquarters. I met her for the first time four years ago, when I talked to the IDF Behavioral Sciences department about People Analytics, and later again in another learning opportunity of the Intelligence Corps. Fast forwarding the years, Limor was generously shared with me some of her experiences. Here’s a rare chance to explore the most secure organization in our country and probably the entire world. How do People Analytics practices look from an insider perspective? It’s a lucky day! Let’s find out.

Untypical career steps

LSH: You are an expert in Organizational Behavior Studies, but you ended up as a Workforce AI Leader. It is not a typical career leap forward. How did your service enable it?

LP: One of the critical challenges that the Israeli intelligence corps face is identifying patterns in individuals’ behaviors and predicting their intentions. We must excel in doing so, in preventing events such as terrorist attacks. We leverage AI to predict enemy plans. However, we have similar predictive needs when we handle our workforce. Just as we can spot on a suicide bomber using AI, we can alert out talent intentions to carry a particular behavior, e.g., leave the organization. As a strategic advisor to the high command, I have recommended leveraging our intelligence experience in workforce challenges and adopting AI to predict workforce behaviors. 

LSH: Experts in Behavioral Science can impact organizations in many ways. Why did you decide to focus on People Analytics?

LP: Indeed, behavioral scientists are engaged in research methodologies, like surveys and focus groups, to understand groups and individuals in organizations. However, such methods lack the predictive ability, namely, to associate attitudes and motives to actual behaviors. We tend to interpret research findings based on our experience, but we may be wrong in our judgments and professional gut feelings. People Analytics, and particularly predictive analytics, can cover us. 

Innovation in HR

LSH: Can you give an example of a wrong judgment that the predictive analytics project contradicted? 

LP: Consider, for example, a typical attitude of commanders towards young women officers. They interpreted attrition related to these women’s work-life balance challenges, who handle both career and young families. However, women who did not participate in career succession interviews at the right time, were entirely practical in managing their careers and initiating their next step elsewhere since no one in the army discussed it. Their commanders referred it to these women challenges to cope with the intense routines of Intelligence units. Interestingly, such a pattern was not characterizing male officers.  

LSH: The Intelligence units’ reputation in technology and analytics is well known. But what is it like to lead innovation in Human Resources, which may be considered less glamorous?

LP: My team partnered with tech units and experts. However, innovative leadership was owned by our behavioral sciences practitioners. Some tech experts thought they should own the project and challenged our leadership. But eventually, we established an advisory board that represented all parties, and we were extremely sensitive, so we managed to make everybody feel that they are the owners. This board had an essential part in funding the project. We also had an additional committee of users, comprised of volunteers who contributed to data munging, hackathons, implementation, and even ethics discussions. 

Challenges and wins

LSH: We’ll certainly get back to ethics later, but first, let’s discuss other challenges. It sounds like a part of your challenge in leading workforce AI wasn’t technical, but rather political. 

LP: It was complicated. It’s not easy to do the fundraising internally, but when you succeed in that, the expectations for quick wins are high, while the implementation takes like forever. We experienced the tension between the tech experts and HR practitioners, who were actually on their reskilling journey. The AI experts considered the joint venture as their own and insisted on managing the conversation with programmers and data scientists. The HR practitioners thought it’s an organizational project or intervention to help individuals and commanders. We end-up in assigning a senior officer who basically handled the conflicts daily and prevented the parties from political dead-ends.

LSH: Tell me more about your approach to finding and prioritizing business questions.

LP: Priorities were determined in command discussions, after extracting business questions from a comprehensive organizational diagnosis. We focused on questions related to talent retention, high command promotion, and workforce reaction to upcoming changes in geographic locations of units and compensation.

Actionable workforce insights

LSH: What data sources you had and how did you leverage the integration of data from different sources?

LP: We retrieved structured data about people’s backgrounds and activities from their long journey in the military serves, e.g., psychometrics, demographics, sociometric, and unstructured data from evaluation processes and interviews. We also purchased relevant data about the Israeli labor market and received data from other Israeli army units, concerning commute times, attitudes among tech talents, and more. When we integrated the data from those different sources, we succeeded in offering insights, and particularly, alternative explanations. 

LSH: How did you transform your findings into actionable insights?

LP: We gained a new understanding of daily phenomena and realized that some of our former responses were completely irrelevant. For instance, in the case study of women officer attrition that I mentioned earlier, commanders were required to discuss career paths with their officers right after signing their first contract with the army. We also re-generated academic programs and compensation plans modularly to offer more tailor-made career solutions. Our impact was significant, and eventually, we won the Commander-in-Chief Award for creative thinking. 

Ethics questions

LSH: Let’s go back to the Ethics questions. What kinds of issues were raised, and how did you handle them?

LP: Yes, we had plenty of ethical issues, e.g., notifying individuals about using their data, limiting our sources of data in the appropriate way for the workforce, as opposed to the enemy, limiting permissions to access the data, and more. The senior board handled most of the discussions and decisions. However, we consulted layers, content specialists in the Intelligence Community, and academic researchers in AI ethics.

LSH: In a glance into the future, how this project will mark your career path?

LP: undoubtedly, this project was essentially a start-up within an organization, or should I say, the most institutionalized organization in the State of Israel. It was an opportunity to explore and express myself as an entrepreneur and innovation leader. But most of all, we managed to solve complex problems in the intelligence corps, which we tried to solve for years by our HR strategy. The breakthrough emerged when we transformed HR strategy into an HR data strategy. Predicting workforce behaviors become even more crucial nowadays in Covid19 times. I’m confident that this career-shaping experience will provide value to civil organizations in the public and private sectors in my next career journey as a citizen. 

LSH: Thank you, Limor, for sharing your fascinating experience.

I look forward to following your journey as a citizen expert in People Analytics, and to continue collaborating in educating the next generation of People Analytics leaders in Israel and globally!

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So how do you enter a People Analytics Leader role, when you are the one who establishes it? Let’s put our datasets aside for a moment, and discuss the thoughts, plans, experiences, and hopes, at this crucial phase of the role. I was privileged to further explore this process at one of my old clients – Amdocs. Although organizational researches that fall within the category of People Analytics have been conducted in Amdocs long before, this global company, which operates in over 50 locations, has a new People Analytics Leader – Gal Mozes. I had the pleasure to interview Gal lately on stage, on one event of “Amdocs Career Week”.

During “Amdocs Career Week” both employees and managers in Amdocs were invited to participate in activities, such as lectures from keynote speakers, workshops, Hackathon, and panels with leaders, with the main objective of promoting the dialogue around their development and future career path at Amdocs. This special week was a great opportunity to take the first formal step on the journey to data-driven HR within the entire Amdocs HR community, which encompass a few hundreds of HR professionals. In my session with this audience, I offered an introduction to People Analytics. But my introduction could not be completed without Gal’s interview, which shed light on her onboarding. I’m happy to share this interview with our community, and I’m sure it will inspire other People Analytics Leaders who take their first steps and establish the role in their organizations.

Background

LSH: Tell us about yourself, Gal, and why did you choose to move to the field of people analytics?

GM: I’m an organizational psychologist, and until a few months ago, I was working as an organizational development consultant for the past 8 years. I was leading the sensing domain meaning I was responsible for the annual engagement survey and pulse surveys, so you can already see I had a flavor for the mix of numbers and people. Also, I have a Ph.D. in social-organizational psychology that included creating a questionnaire about behaviors and a lab simulation, so I also have a soft spot for psychological research, and I’m no stranger to social science statistics. And last but not least, I love to uncover insights and help others do the same, so when I started hearing about this new domain called People Analytics, I was quite intrigued. Locating myself in the intersection between organizational psychology and data was the next obvious move, and luck was on my side at me when a new people analytics role was created in my company, and my passion for the area was a well-known fact. A real match made in (workplace) heaven and so I found myself moving to this role.

Challenges

LSH: How would you describe the challenges that your company faces these days in regards to data strategy?

GM: I think that in the past 2 years, we took very important steps to promote ourselves when it comes to descriptive data. We worked hard to create a ‘one-stop-shop’ dashboard for leaders and HRs so that they will have a place to see key measurement in the different people related areas such as recruitment, performance management, talent mobility, burn out and more. I think we still have a way to go when it comes to turning it into a decision-supporting tool and showing the value it brings. But descriptive data is just the first step of the journey when becoming data-driven. However, it’s not people analytics just yet, for which the diagnostic part is the holy grail. When we learn to identify key business challenges and then use clever tools and approaches, such as predictive analytics, ML, planned experiments, etc. to tackle them, then we will be where we strive to be at, which is data-savvy.

LSH: Your HR partners, where are they in the journey of becoming data-driven?

GM: I keep reading articles about how HR is not data-oriented, and the worst thing is they say it about themselves! I believe that where there’s a will, there’s a way and I’m happy to say that I’ve seen a great deal of willingness to go on that journey. People understand that this is a key future capability in HR, and they are willing to step out of their comfort zone and acquire these skills. Yes, I know some might lack the tools and the experience, but this should not hold them back. I’m here to support them, and part of my job would be to provide the tools, training, and consultation that would help them during the journey. I hope this can be a first step in creating their confidence in this data-driven approach, knowing that they’re not alone.

Development in HR

LSH: Why do you think it’s crucial these days for an HR leader to base their discussions on data?

GM: In order to make an impact on the business, you need to talk “the language of the business”, which is numbers, money, analytics, and data related insight. So if an HR leader wants to be a key partner, speaking in the same language and terms is a must. Also, I think that using a combination of their experience and intuition together with the data and analytics would be so powerful that they would be practically invincible. But seriously though, I don’t think data solves everything, but it’s much harder to argue with, and it’s a strong tool to make a point and initiate a change.

LSH: What do you expect from HR leaders as your partners?

GM: It’s easy; I want them to be my partners in crime and go on this journey together. We work in a large organization of 25K employees in more than 50 countries and diverse business units and roles. So the challenges are plenty and quite diverse, it’s never boring, but it’s also a lot. My HR partners are required to be able to raise the business questions in relation to people’s data, as they are there in the field and they know the needs and challenges in a way I could never know. So my request to them is data-minded so we can work together to identify the opportunities to utilize data and uncover the insights that can bring value to the business and the employees. This means the HR leaders should be able to review data, hold a conversation around data points and analytics, and most importantly, find the relevant business questions.

Success

LSH: What would you define as a successful first year in the role?

GM: That’s a tough one, much easier to say what others should do! Honestly, I understand It’s a huge mission, and so much more than being “analytic” and knowing how to work with data. That’s why I would define success in the first year relating to two main stakeholders: First, let’s start with my partners in HR. I would love to see a change in HRs mindset so that they would feel comfortable with data. Again, I see the approach towards HR in relation to data, and I truly don’t think it’s justified. If we will move out of our own way and desert this perception, then I think this can become one more valuable tool in their already impressive toolbox. Also, based on the wise words of people analytics leaders the duty of the people analytics function is to make the HR more data-driven, and I’m not going to argue with the experts but rather take this advice and learn from their experience. Second, If I want to earn my keeping, then there has to be a contribution to business success. And while knowing this is a key element of this role’s essence, I also know it’s very ambitious. So while I already started looking into a few interesting leads, I am aiming to find a quick win people analytics project. This means easy to access and use data in an area it would be possible to do a real change that is valuable to the business. And for that, I need to know that I’m not alone and have my trusted HR partners so that together we will find the right opportunities to make an impact.

LSH: Thank you, Gal!

I look forward to seeing you spreading your net in Amdocs’ huge HR group, and lead it to first successful case studies in the coming year. I’m honored and fortunate to be partnering with you in educating HR professionals to be more data-driven!

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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

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Gender diversity in tech

It is a well-known fact that women are a minority in tech, especially among programmers. LinkedIn, for instance, studied the professional gender gap and explored the rate at which men and women have been hired across 12 industries worldwide. The research took a detailed look at leadership positions and software engineers. The findings include some depressing statistics for people who care about gender diversity in tech: In 2016, the rate of women’s new hiring was 18% in software engineering and 30% in leadership roles. And this trend might get even worse: A study from Accenture and “Girls Who Code” warns that, unless action is taken now, the percentage of women in the computing workforce will shrink over the next 10 years to 22%.

This forecast is not surprising since, according to researchers from the University of Wisconsin-Madison, almost 40% of women with engineering degrees either quit or never enter the tech industry. Indeed, women comprise only a small percentage of the biggest tech companies, based on diversity reports released by organizations such as Apple (20% of tech, 35% of non-tech, 28% of leadership jobs), Microsoft (29% of the workforce, 17% of tech, 23% of leadership roles), and Twitter (10% of tech, 21% of leadership positions).

The consequences of women being a minority

What is it like to be the only woman in a tech company team? How does a woman’s work in a man’s world influence her daily routine, colleague relations, work-life balance, job performance, manager reviews, promotion, and compensation?

Some recently published findings are truly disturbing. According to the “Elephant in the valley” survey, 60% of the women working in Silicon Valley have experienced unwanted sexual advances. About two-thirds of the women surveyed said that these advances were from their superior. Moreover, 90% of women interviewed had witnessed sexist behavior at company off-site events or industry conferences, and about 87% of them had heard demeaning comments from their male colleagues.

Although serious in and of itself, sexual harassment is not the whole story: 40% of the women interviewed felt that they ought to talk less about their families in order to be taken more seriously and about 52% of those that took maternity leave, cut it short so that it would not hurt their career. Slightly less than half (47%) of the women had been asked to do lower-level tasks that were not expected of their male colleagues, such as taking notes or ordering food. Additionally, two-thirds of women felt excluded from networking opportunities because they were women. In short, many women experience distressing workplace situations while most men are simply unaware of the issues facing women in the tech workplace.

This “bro culture”, this immature frat-boy behavior, is only one part of the sad story. At a much more fundamental level, the tech industry offers lower salaries to women in comparison with their male colleagues. According to data released by Joint Venture Silicon Valley, men in Silicon Valley earn up to 61% more than their female peers. Women are also offered fewer opportunities for advancement.

Companies actually lose

The women’s minority status in tech and the disturbing organizational culture for women in some companies are not merely social issues. It can impact negatively on company performance. Studies have shown that gender diverse teams are more successful: A research summary published by the National Center of Women and Information Technology (NCWIT) reveals that gender diversity at top management levels improves financial performance and that gender-diverse work teams demonstrate superior team dynamics and productivity. Likewise, companies that are at the top quartile of gender diversity are 15% more likely to outperform financially than those at the bottom quartile, according to McKinsey and Co. Researchers at Carnegie Mellon University have found that including women increases the collective IQ of teams and makes gender diverse teams smarter.

As Josh Bersin nicely summarized it “…companies that build a truly inclusive culture are those that will outperform their peers.” Thus, there is a clear economic incentive for technology companies to do something about gender diversity. But what concrete steps can they take?

Simple steps forward for HR

The first step is to identify where the company’s diversity gaps are. HR leaders can easily use analytics to look at the current employee population and examine headcount by gender. Once the baseline metrics are known, HR leaders can work with business leaders to determine gender diversity goals and allocate budget for these initiatives. Data-driven organizational processes, e.g., data-driven recruiting, enable continuous monitoring of metrics to see whether diversity increases or decreases as people move inbound, outbound, or within the organization.

But monitoring diversity metrics is not enough. If improving workforce diversity is a business objective, it is essential to keep track of performance metrics and financial metrics by gender groups. Here too workforce analytics can easily pinpoint gender differences, show rates of success within different groups, indicate bias, and keep an eye on promotions. The results may not only move the company forward in terms of gender inclusion, but it may also attract high potential employees – of both genders – in the long run.

Case study: Women in Taboola

One tech company that followed these steps and seriously studied the status of its women employees is Taboola. Taboola provides a web discovery platform, serving up 360B recommendations to over 1B unique visitors every month on some of the web’s most innovative publisher sites including USA Today, Business Insider, Chicago Tribune, and The Weather Channel.

The research, which aimed to explore the status of women among ‘Taboolers’, was conducted in 2016 by Neomi Farkash, global head of HR, and myself, Littal Shemer Haim, a people analytics consultant. Based on Taboola’s employee reviews and HR financial data, four comparisons were made between women and men:

1. Organizational Distribution:
What is the gender distribution within units, locations, roles, etc.?

2. Compensation and Promotion:
Across different roles, how are women compensated and promoted in comparison to men?

3. Performance Review:
How are women evaluated in comparison to men? Are they perceived differently in general and regarding performance, self-management, relationships, potential leadership, etc.?

4. Evaluations and Promotion:
What is the correlation between yearly reviews and promotions for the two genders? Do men and women who received similar reviews get a similar promotion?

Neomi Farkash affirms that ”the research was essential in order to start a discussion, which contributed, among many other activities, to address and reduce any gender gaps in the workplace.”

Creating awareness is an easy first step that every tech company can undertake. The data needed for such research exists and is accessible to HR. No complex analysis is necessary, and any HR analyst can handle it by simply comparing the four factors described in the Taboola case study. I hope that many HR leaders will take this initiative and thus make their small but important contribution to enhance gender diversity in the tech industry. The benefits will be reaped not only by our own daughters and sons when they join the workforce but by society as a whole.

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