Five myths about People Analytics that inhibit your progress
Photo: Littal Shemer Haim ©
Five myths about People Analytics that inhibit your progress
If you randomly select an HR leader and ask about her progress in the journey into data-driven HR, the chances that she would tell you that she is already on the track, according to a recent survey, are about 70%. However, if you dig deeper in your conversation, you might find out that you and your interlocutor mean completely different things when pronouncing the words “People Analytics”. What your partner sees as progress might not be at all a progress in your eye. No wonder that this might be the case, when the definitions of this field are vague, and we are still struggling to form its practices.
So how can we describe the status of People Analytics, better than a single number in survey results? In one of my public talks about People Analytics this year, I challenged my self to describe the state of our practice in five sentences only. My qualitative effort worked, I guess, at least according to the audience respond. Looking back at my list, I realize now that each point I made implies a myth about People Analytics. HR leaders should be aware of the following five misconceptions, or otherwise, continue to let these false ideas about People Analytics inhibit their advancement.
Myth #1: People Analytics is an established practice within HR management
No. Although we witness higher adoption rates every year, there are barriers, and overcoming takes time. Among companies that presented case studies at a recent conference in Europe, many mentioned a time span of one or two years until gaining return on investment from People Analytics activities. I was fortuned to hear dozens of lectures about People Analytics this year. Those case studies presented in conferences are just a handful. Conferences curators will not pick organizations that have not reached to significant milestones. However, other organization, those who are still struggling, shuffling, freezing or just learning from their mistakes, are not less interesting, nor less important. If you experience difficulties in your People Analytics journey, you are certainly not alone. In a recent study published by Visier, obstacles of organizations include the lack of connection between analytics and business results, basing analytics HR system data only, insufficient “data-driven” skillset among HR people, data quality issues, unstandardized metrics and over-dependence on IT for analytics.
Myth #2: People Analytics is a professional research about HR practices
No. People Analytics is a multidisciplinary profession, that aims to support business decisions related to people on data. HR leaders are not using People Analytics to measure the efficiency of HR practices, but rather to understand the impact of their practices on the business results. HR people manage a variety of processes throughout the employee’s life cycle: planning, recruitment, learning, evaluation, recognition, reward, mobility, promotion, safety, welfare, and more. These processes create aggregated workforce capabilities: engagement, culture, efficiency, leadership, innovation, and so forth. Those capabilities enable the organization to achieve its business goals: productivity, quality, and customer satisfaction, which, in turn, result in business outcomes, e.g., revenue growth and stakeholders return. People Analytics means that HR focus on the use of people data, derived from their processes, to impact the business. In this context, it is worth to mention, again, that the HR dashboards are not People Analytics. Both People Analytics and HR dashboards deal with Performance. However, each practice has a different approach. Dashboards enable to present different HR KPIs, but can’t answer the question: Why? For that purpose, we need People Analytics, which enable to understand the factors that drive those KPIs presented on our dashboards.
Myth #3: Traditional research is outdated in the era of People Analytics
No. People Analytics practices combine new data sources and technologies with the good old practices. Mentioning tradition, let me share the puzzlement I’ve experienced in a People Analytics conference, this year in London. I chose in advance to participate in those sessions that seemed most innovative. However, I discovered that some speakers relied on quite traditional research methods, that actually, I’ve been practiced my self in organizations for years. How does it fit in with new technologies, and with plenty of new sources of people data? The bridge between traditional research methods and innovation are two trends. The first is data integration. We no longer settle for analytics based on HR data sources, but rather combine many types of data about people, both from HR and business units. The second is our new perspective into the future. We refer to people data at different stages in the employee life cycle: candidates, employees, and former workers, and we focus our analytics efforts at forecasting outcomes related to business questions.
Myth #4: People Analytics is great only if you are C-suite
No. People Analytics is about different objectives and questions, of old and new stakeholders: Executives, HR, managers, and the people. Yes, the people! Despite the emerging trends, new technologies and data sources, leaders in organizations still ask the same old and basic question: Who? In the past, this question was quite general: Who are the people with the skills, work habits, knowledge, experience, and personal qualities that drive the organization to meet its goals? Today they still ask “who?”, but more specifically, and with a focus on business metrics: who create the best new products, make the most revenue, find the greatest efficiencies, build great workplaces, adapt to changing business conditions, delight customers, attract others to join the organization? In other words, we put the question “who?” with things that are outside the traditional territory of HR. Traditional research, e.g., employee engagement or training effectiveness, which was already out there, for decades, is now connected directly to the business. However, HR and business leaders are not the only ones who raise questions. Today employees expect to receive personalized service in the organization, just like they do in any other context of their lives. We all live through our smartphones, and there’s no reason why employees should expect it to be different at work, and regarding significant questions about career and wellbeing. The People Analytics function should address these needs.
Myth #5: People Analytics means having a data scientist in the HR department
Not necessarily. Although it would be nice to have such a professional in every HR department, we do witness a shift from a research perspective and data science projects to analytics products. Part of the progress in People Analytics is the implementation of HR-tech solutions that enable real-time analysis instead of research cycles. Organizations implement analytics solutions throughout the entire employee’s life cycle. My HR-tech classification includes: Workforce Planning and Mobility, Sourcing, Selecting and Hiring, Onboarding and Culture Fit, Employee Experience and Sentiment Measures, Employee Wellness, Health, and Safety, Employee Growth, Learning & Development, Goals Tracking, Performance Review, Productivity, Organizational Design, Networks, Teams and Collaboration. But of course, there are so many other ways to capture the HR-tech ecosystem. People Analysts have a lot to offer in the processes of HR-tech implementation. They help using technology to amplify, not overtake, the influential role in humanity in organizations. They can do so, mainly due to their ability to embrace two new responsibilities: procurement and ethics.
I truly believe that awareness of these five misconceptions contributes to faster progress in People Analytics. However, there may be more false ideas about this new practice. Did you tackle more Myths? Please share in a comment.
About the author:
Littal Shemer Haim brings Data Science into HR activities, to guide organizations to base decision-making about people on data. Her vast experience in applied research, keen usage of statistical modeling, constant exposure to new technologies, and genuine interest in people’s lives, all led her to focus nowadays on HR Data Strategy, People Analytics, and Organizational Research.