Data scientist roles are still not very common in HR departments. A successful establishment of such a function requires balancing the analytics maturity of the business and HR leaders with the data scientist’s skills. Therefore, it is essential and fascinating to explore how data science and HR needs are knitted.

I was fortunate to interview Elizabeth Esarove, a data scientist in a senior advanced analytics role in the HR department at AT&T. I contemplated many things yet to be learned and developed and found some clues for data scientists’ development between the lines.

A career path from HR roles to data science

Littal: Tell us about your background, current role, and professional journey from an HR professional to a data professional.

Liz: I was hired into an HR role due to my analytics experience in the banking industry. At that time, HR departments were beginning to understand the value of using data to measure and improve HR functions. My role was on the new data analysis team; I was the third person to join the group. That was about 20 years ago. Since then, I’ve had roles within HR that involved data analysis for Talent Acquisition, Talent Management, and Training, where I learned about each area of HR and improved my ability to provide valuable insights.

When self-service reports became important for HR, I moved into a project management role where I served as a liaison between Information Technology and HR to work on projects developing reports for HR teams. This role helped improve my ability to communicate effectively in business terms with HR leaders and technical terms with Information Technology. In addition, the experience helps in my role as a data scientist today as I explain the results of my analysis to executives.

In 2013, I read news articles about companies that used predictive models to reduce employee attrition. I could envision other potential applications for predictive models to address workforce and business issues. I was also ready to take on a new challenge in my career. So, I decided to get a master’s degree in Data Science to develop my skills in predictive analytics. One of my electives was a text analytics course which is an asset in my current role because it’s necessary to analyze comments received in our employee surveys. I started applying my new skills the year before I graduated and have been working on predictive modeling and text analytics projects for the past six years. I enjoy applying my data science skills to HR projects!

The role, skills, and tools of a data scientist in HR

Littal: How does your data scientist role differ from other roles in the team, such as data engineers or data analysts?

Liz: As a data scientist, I focus on statistical analysis, predictive models, or text analysis to provide insights for business decisions. Other roles on our analytics team include data engineers who build and maintain our data architecture and security and monitor data integrity to ensure accuracy in the products created by our data analysts and data scientists. In addition, our data analysts design, build, and manage reports that can be automated; some of them also serve as consultants with HR professionals to help with data storytelling and clarify business needs for our projects.

Littal: Are specific skills or knowledge domains needed to be a data scientist in the HR department as opposed to other departments?

Liz: I believe that understanding ethical and legal standards regarding the use of employee data is incredibly beneficial to a data scientist working in HR. Our People Analytics team works closely with the Legal team to comply with laws regarding the use of employee data. We also have a Privacy team that reviews the use of employee data in reports and predictive models; they make recommendations based on our company standards regarding the protection of and appropriate use of employee data.

Littal: Describe the tools that you use. Are there any R libraries or other open-source tools that are a must? Which is your favorite and why?

Liz: R and Python are open-source tools used by data scientists on our team. I prefer R with RStudio integrated development environment. I have over a dozen R libraries for different projects; my favorite is dplyr – it’s the one I rely on most for data wrangling and probably appears in my R code for every project. For time series forecasting, I use the feasts and fable packages. For text analysis, I use tm and topicmodels packages. The ggplot2 package is excellent for creating beautiful data visualizations.

The contribution of a data scientist to the HR department

Littal: How do business questions related to people are raised? Who initiates them? How do they become a project or a product?

Liz: Our HR Business Partners usually learn about questions from their clients in the Business Units. The HRBP contacts a People Analytics consultant on our team to discuss the project. The consultant then determines which team members should be included in the project and sets up a meeting with the appropriate stakeholders to start defining the project scope and the expectations regarding the project’s final product. The project team continues developing the solution/product and meeting with the client regularly to keep them informed of progress and, if necessary, make changes until the project is complete. If it’s a data science project, the data scientist will be involved in presenting the final product to the business unit leader and HRBP.

Some projects are initiated within our People Analytics team. For example, if we see common themes for requests from multiple business units or persistent requests, we may build a product to satisfy that business needs regularly.

Littal: What are some Ethics considerations in your day-to-day work? Specifically, how do you consider explainability when creating predictive models? Can you share some examples?

Liz: I’ll share a hypothetical example to help explain the ethical considerations. Let’s say a company develops a predictive model determining which employees are likely to leave. If they used a “black box” model that doesn’t allow one to explain the impact of the variables in that model, the result is a model that predicts which employees are likely to leave but doesn’t tell anyone why they are more likely to leave. With this type of model, it would be hard to recommend actions that the company can take to retain valuable employees.

Also, consider how predictive models are used. If misused, a model like this could negatively impact employees and the company. For example, what would happen if a supervisor had access to that information and decided not to assign a team member to a new project because the predictive model indicated that the employee was likely to leave? Perhaps assigning them to a new project will give them a new challenge and eliminate their reason for leaving.

Now consider a situation where a company used an explainable model and was able to understand that employees who have been in the same role with no job growth for years are more likely to leave. Now, the company has some information that can be used to reduce attrition! Perhaps the solution is to help employees learn about available career paths, company training, or other growth opportunities. Again, using explainable models and using them well can be very powerful for HR.

Littal: What are, in your opinion, the fundamentals in data science that should be a part of every HR professional training so that they can be better inner clients of data professionals?

Liz: I think HR professionals should have an introduction to data science with some examples to help them understand what’s possible and how data science can help answer business questions and solve problems. The introduction should include a basic understanding of the different types of predictive models available with HR-related examples of how they can be used. In addition, if the company uses surveys that include employee comments, understanding what’s possible with text analytics can help. If HR professionals have that understanding, they are more likely to recognize a situation where data science can be used, and they can tell their clients, “We can help you with that.” Also, if they understand the process involved in developing data science solutions, it could help set expectations regarding timelines and deliverables early in the conversation with business unit leaders.

Littal: Thank you, Liz, for your insightful perspective. I look forward to finding out how implementing it will lead to more advanced analytics practices within HR departments.

The post Being A Data Scientist in The HR Department appeared first on Littal Shemer Haim.

The People Analytics professional community is evolving. One of its remarkable qualities is the open-source culture that characterized many of its members. While datasets, analytics, and insights are usually restricted from sharing, the experiences, resources, and advice are generously shared.
I had the pleasure of meeting Nicole Lettich, Sr. Specialist in HR Metrics and Analytics at Nasdaq, and discussing her development and success in her role. Our conversation inspired me to list the ABCs of People Analyst’s success, starting with Autodidact habits, Business understanding, and Coding skills.

Career path into People Analytics

LSH: Tell us about your background, professional journey, and current role.

NL: I have a bit of an unusual background because my career hasn’t exactly followed a linear path. I have an English degree but spent much of my early career working in marketing analytics in the financial services industry. I held roles on both the Acquisition and Retention sides of the business. I was responsible for A/B testing campaigns and building marketing mix models, plus I got to participate in Six Sigma projects which taught me a lot about process improvements. After doing that for several years, I eventually had the opportunity to open my own business. I learned so much through that experience. I broadened my analytical skillset to include business and financial analysis techniques and got my first exposure to overseeing Human Resources. That role enabled me to see the impact that my policies had on business outcomes. 

Ultimately, I decided to transition back into being an employee, so I started working as a Staff Accountant and planned on sitting for the Certified Management Accountant certification.  The owner asked me about building out Human Resources since there was never a dedicated person in the role, so I accepted the challenge and gradually stood up the function. I oversaw a small team responsible for HR, overhead and property accounting, and payroll. Since I was operating in whitespace where nothing previously existed, I was able to create analyses using information from all of our different systems. 

Now, I’m the first hire on the Organizational Insights & Analytics team at Nasdaq. When I joined, my manager and I went on a listening tour to understand the needs of our internal clients, and we used that information to help us develop the roadmap. It’s an ideal role for me because I enjoy translating business needs into tangible outcomes. It’s again a place where I have the opportunity to help create something that didn’t previously exist.

LSH: Let’s dive deeper into your role: What business questions are you involved in, and who initiates them?

NL: I work closely with our Organizational Strategy Leaders and CoEs to answer questions for key decision-makers. One of the critical pieces of work produced is quarterly reports used during business reviews. We use these to provide business leaders with organizational health metrics and drive workforce planning discussions. These reports synthesize HR data from different sources to give leaders a holistic view of their recruiting, talent management, and DE&I processes and outcomes. I also partner with members of the different CoEs to understand how we can leverage data from external sources to provide insights to leaders.

Coding for People Analytics

LSH: Describe the tools that you use and your outputs. How do you leverage coding skills in your daily work?

NL: I primarily use several key python libraries for data management, analysis, and visualization. Pandas is a library for working with tabular data. I use it to import and manipulate data from flat files or databases. I work with Statsmodels, Sklearn, and Scipy for statistical analysis. I also use Matplotlib and seaborn to produce the visualizations for my PowerPoint reports. But, I will also use bokeh to produce interactive reports or dashboards depending on the requirements.

LSH: Let’s explore further the advantage of coding. How did coding skills contribute to your success?

NL: Learning to code is highly beneficial because you have the flexibility to create customizable solutions, and you can quickly produce and share insights. I began coding when I took over the tax abatement process at my previous employer. I inherited an entirely manual process. I looked at the process design to figure out where the roadblocks were. I knew the approach had to ultimately be automated so it would scale as we took on larger projects. Plus, we needed a way to report on KPIs and metrics related to staff capacity. I coded a data entry form workflow and automated reporting using VBA for my proof of concept and then continued to refine it using SQL and python. 

In my current role, I most recently applied my coding skills to build a Markov analysis to show the impact of hiring and attrition on the gender composition of the workforce. I became familiar with this type of analysis when studying for the SPHR. However, I learned to build the model using Excel without any interactivity. I now use NumPy, pandas, bokeh, and a few other Python libraries to create this model as an interactive report with slicers that the end-users can use to assess impact.

Learning on the job

LSH: How do you learn on the job? What are the barriers, and how to overcome them?

NL: Fortunately, we have a very robust Learning & Development platform at Nasdaq – I have access to tools that help me learn on the job. One of the things that drew me to the company is that they encourage the 70/20/10 learning principle. 70% of learning is on the job, 20% through mentoring, and 10% by formal training. I’ve been fortunate to work in organizations where people are open to knowledge sharing and are willing to help if you get stuck on a concept or a technique.

LSH: What should be the learning goals for people Analysts? What are the crucial skills for future success?

NL: The most important skill for people analysts is to know how to translate business problems into analytical questions. They must have a deep understanding of the business, its strategies, and objectives to produce an analysis that will have an impact. When it comes to technical skills, they should know how to choose the correct analysis, deal with data issues, and communicate results and complex subjects to individuals of varying technical expertise. I’m, of course, a firm believer in the importance of knowing how to code.

Working with HR Professionals

LSH: What is, in your opinion, the most prominent challenge in the HR sector regarding data analytics?

NL: We’re in an exciting time where people analytics isn’t a nice to have but is a business necessity. However, while it’s satisfying to see that progress, I think that many HR departments don’t have access to financial data, so it can be challenging to quantify the impact of people analytics projects on business outcomes. 

LSH: What would you recommend to HR practitioners who want to become more data-driven professionals?

NL: Foundational data literacy is a critical first step in becoming more data-driven. Understanding the basics will help you become more comfortable interpreting data and becoming more evidence-based. Statisticsbyjim.com is a site that communicates statistical topics in a very approachable way. And the StatQuest YouTube channel features explainer videos on various statistics and data science topics in bite-size pieces that help build up your confidence level. Even if the end game isn’t to become a practitioner, you can have more productive conversations with HR tech vendors and your People Analytics team if you better understand how these techniques work. Lastly, I recommend reading lots of case studies because they’ll help you conceptualize what’s possible.

LSH: Thank you, Nicole! It was an inspiring conversation. I look forward to following your work and finding the next opportunity to explore how Autodidact habits, business understanding, and coding skills lead to People Analytics practices that impact the organization.

The post ABCs of People Analyst’s success appeared first on Littal Shemer Haim.

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!

The post Leveraging workforce data as it was a state security project 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.

The post HR Challenges in A Data-Driven World appeared first on Littal Shemer Haim.

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!

The post People Analytics Leader – Survive Your Onboarding! appeared first on Littal Shemer Haim.

The post People Analytics in SMBs: Small Data, Huge Impact appeared first on Littal Shemer Haim.

The post Can you reinvent career development by using analytics? appeared first on Littal Shemer Haim.