Data Science For HR: Barriers To overcome In 2023

3 fundamentals for success in the journey to data-driven HR

People Analytics as a discipline has existed for over a decade, or a few decades, if you consider the traditional practices in organizational research. Moreover, the HR-tech industry has brought some brilliant solutions in the last couple of years. And yet, people analytics is still not an established practice in many HR groups, either by embracing tech solutions or impacting the business with actionable insights derived from analytics projects.

So, what holds back the maturity of people analytics, and what should HR leaders do to overcome this in 2023? How can they go the extra mile beyond reading dashboards and reports? Here are three fundamentals for anyone on the journey to data-driven HR.

#1 Have Clear Expectations When Working with Data Professionals

First, acknowledge the differences between data analysts and data scientists. Though some role descriptions may overlap, a data analyst generally spends more time on routine analysis, providing reports regularly, and typically using BI tools or Excel. However, a data scientist may design how to integrate data from different sources, then manipulate, analyze, and sometimes productize it, leveraging advanced analytics and typically using programming languages like R.

The practice of data science is multidisciplinary. It encompasses three general skills – the business domain of expertise, statistical modeling, and programming. Therefore, a crucial part of your challenge in People Analytics is the effort to establish communication between professionals with different skills.

I believe you have heard a lot about the People Analytics journey that enables HR professionals to become more strategic because they speak the language of the business and impact using the right questions and insights derived from people's data. But they can support decision-making only when they communicate those questions to the right data professionals or tech providers.

#2 Understand Your Essential Role in a Successful Project

If there is one message I hope you would take, this would be it: Ensure that the data scientist understands the business needs in workforce-related analysis. In addition, it would help if you articulated the right business questions so the research findings yield the best data storytelling you can leverage to impact.

Beyond that, let me shed some light on all data science projects' processes while taking the data scientist's perspective. First, you always start with a business question, sometimes titled research objectives. Then, based on a specific concern, goal, or challenge for the business, you create hypotheses about how human attitudes, behavior, or performance impact that key concern.

Only when you define what you need to measure to test your hypotheses can you source the data from any department that holds it. But then, you must ensure that there are no missing values in people's information, typos that corrupt categorical variables, wrong labeling, duplicate records, neglected records that were not updated, or any other issues with messy data.

Then you reach the phase of Exploratory Data Analysis (briefly, EDA), which sometimes proceeds with selecting variables for prediction models and then modeling. These steps beyond EDA are called feature engineering and practical machine learning. Eventually, you communicate the results, focusing on actionable insights from the findings, and sometimes implement models into products.

As an HR professional, you have a crucial role in this process. The data scientists can't maintain the data for you. Also, remember that while you may lack experience in data science, your data scientists may lack an understanding of people's processes. Your responsibility is to ensure no gap between the analysis made for you and the business questions and actionable insights.

#3 Be Proactive When Dealing with Findings and Results

Occasionally, findings are boring. There are cases in People Analytics where statistically insignificant results are the desired outcome. As with equal pay, sometimes organizational groups shouldn't be significantly different. However, these insignificant results may only be the beginning of the exploration. You can always try to enrich your analysis and reveal additional insights.

For example, you can explore the interactions of variables. Multivariate statistics can raise new perspectives. You don't have to go back to your notebooks of statistics fundamentals. Instead, ask a data scientist about interactions. So, if a comparison between groups does not reveal striking differences, adding a single variable to the analysis may uncover some hidden patterns.

From a data scientist's point of view, your proactivity is invaluable. When you ask "why?", suggest hypotheses, and challenge explanations, you leverage your domain expertise and complete the data scientist skills. Moreover, when you stick to the business questions and research objectives, you may be surprised that the sponsors of your analysis project embrace the so-called boring story and may even be thrilled to have it. Finally, insights confirming a known fact or domain knowledge may validate your data integrity and maintenance procedures.

To conclude, having clear expectations when working with data professionals, understanding your essential role in a successful project, and being proactive when dealing with findings and results are three fundamentals for your success, either in making or buying the solutions of People Analytics.

A version of this article was originally published on Littalics.com

Author Bio

Littal Shemer Haim helps organizations improve business performance by making informed decisions about people. She offers executives guidance in the journey towards a data-driven HR and conducts People Analytics projects. Littal sources innovation in the HR-tech industry and advises start-ups in this ecosystem. She writes and lectures about People, Data, Ethics, and the Future of Work. In addition, she leads mentoring and learning programs for various industries, in which she shares her knowledge and more than twenty years of experience in People Analytics, HR Data Strategy, and Organizational Research. Littal's blog, also known as Littalics, is one of the first and most influential blogs in People Analytics. Connect Littal Shemer Haim