Another cycle of the introductory course, The People Analytics Journey, is about to end. This training program is unique because it covers the fundamentals of the domain and demonstrates them with real career stories and experiences of HR and People Analytics leaders. Thus, the course contributes to a new professional community in Isreal. In previous sessions, we hosted Gal Moses, People Analytics Lead at Amdocs, who shared her onboarding experience and shed light on some challenges and opportunities. We were also honored to have Michal Shoval, HR manager at GIA, who shared her case study. The last session of the course will be a special one. We’ll discuss the future of People Analytics as a profession, and the importance of new skills, e.g., procurement processes and ethical considerations. Our guest will be Yael Epstein, former HR analyst at Microsoft, who will talk about the role of technology in People Analytics, base on her experience. Here is the interview I had with Yael before the learning session.

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!

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(Reading Time: 4 minutes)Organizational charts are everywhere, and we’ve all seen them. Within any organization, they show the hierarchical chain of command. This common visual representation of the organizational structure depicts departments or positions, illustrates relationships between them, and makes it clear and easy to understand how authority, responsibility, and information are flowing within formal groups in organizations. But how relevant these Org charts are, in the realm of modern organizations? How loudly should leaders question them?

The shift to “Networks of Teams”

Redesigning the organizational structure is the #1 issue on leaders’ minds, according to Josh Bersin. “Today’s digital world of work has shaken the foundation of organizational structure”, Bersin argues, “shifting from the traditional functional hierarchy to ‘network of teams’.” Managers need to explore new ways to coordinate and align teams, to get them to share information and cooperate, and to find new paths to leadership, alternatively to ‘upward mobility’.

Changing the organization chart is only a small part of the transition to a ‘Network of Teams’. In their article “Organizational Design: the rise of teams” McDowell et al. claim that “more important, and more urgent part is to change how an organization actually works”. Therefore, the focus of organizational research should shift from individuals and teams to networks of teams. Moreover, HR practices should address the shift to ‘networks of teams’, and embrace new analytics tool for that purpose.

An interesting attempt, to assess the extent of focus that HR practices aimed at teams, was made by Tom Haak. He concludes that “most current HR practices still tend to focus on individuals, less on teams, and probably not at all on networks of teams”, and he lists some available tools that can support this shift. Some items on his list are ONA – Organizational Network Analysis tools.

What is organization network analysis?

A comprehensive overview of Organizational Network Analysis is offered by Rob Cross, Professor of Global Business at Babson College. Cross explains that informal networks of relationships within organizations “have become central to performance and strategy execution”.

According to Cross, “Organizational network analysis (ONA) can provide an x-ray into the inner workings of an organization”. It makes invisible patterns of information flow and collaboration in strategically important groups visible. The network analysis can reveal the contrast between the organization’s formal and informal structure. For example, it can point critical people in terms of information flow, it can help to identify peripheral people who may be an underutilized resource, and it can demonstrate the extent to which a certain team becomes separated from the overall network.

The traditional way to study organizational networks is through a survey. A sample question of such a survey could be: “to whom do you turn to for information to get your work done?”. Information collected from a survey can be used to create network diagrams that illustrate the relationships between members and groups in the organization, e.g., who is a source of information, who are the most prominent within a team, who are only loosely connected to a network or completely isolated, which teams are split by functions, etc.

Innovation in organization network analysis

Indeed, a survey is a reliable, valid, and very traditional tool to explore informal networks in organizations. For example, I’ve studied the collaboration between groups in an Israeli credit card company, back in 2010. The first part of my research, not surprisingly, included a data collection phase, using a survey. However, a survey can reveal network representations only one at a time. It does not point to events, trends, and changes as they occur. Moreover, it requires a tedious analysis of relations mapping. Therefore, the survey phase will probably be followed by work done by algorithms. Some examples of software that use survey inputs to create organizational network maps are OrgMapper, Synd.io, Keynetiq, and Orgvue. There are many other social network analysis tools, including open-source platforms.

Nevertheless, in my opinion, a solution that does not involve the responses of the employee is much more appealing. Not only instant, saving time and efforts of a survey, it also saves the inner buzz that comes with any organizational intervention. Are there innovative solutions that can be directly interfaced with organizational systems and reveal the organizational invisible map?

Of course, there is. Although it may be categorized in the business domain of client relations, here is a company that offers an interesting use case for People Analytics. Its solutions enable automatic and continuous organizational network analysis, for different purposes of data-driven HR:

TrustSphere applies ONA across organizational communication and collaboration systems on a real-time basis. Without exposing contents, it analyzes digital interactions across the organization (e.g., text messages and e-mails). Its proprietary algorithms generate a range of actionable insights that include: identifying and measuring the strength of relationships within the network, quantifying levels of collaboration, pinpointing key influencers, and observing network activities and changes in employee engagement.

TrustSphere solution enables HR to supply the business with data-driven insights for talent management. The software reveals the strength of employees’ relationships, the size, and reach of their networks, their ability to influence, collaborate, and lead. It enables us to measure collaboration based on real-time activity, and understand how teams collaborate internally and with other parts of the organization. It can also help new employees to be productive from day 1 by leveraging the network map of their predecessor, in the company and outside. Furthermore, the data enables ongoing measurement of diversity program impact, through network development, mentoring, and influence.

Certainly, TrustSphere’s vision is a great example for alignment with the shift to “Networks of Teams”: The solution is relevant for the individual, teams, the network of teams, and the organization as a whole. Any other examples? Please share in a comment (edited: and read my own comments, about worklytics, Humanyze, Starlinks, StepAhead, and others).

References:
Josh Bersin, “The New Organization: Different by Design“, joshbersin.com
Tiffany McDowell, Dimple Agarwal, Don Miller, Tsutomu Okamoto, Trevor Page, “Organizational design – The rise of teams“, dupress.deloitte.com
Tom Haak, “From individuals to networks of teams“, hrtrendinstitute.com
Rob Cross, “What is ONA“, www.robcross.org
Devendra Desale, “Top 30 Social Network Analysis and Visualization Tools“, www.kdnuggets.com

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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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