HRTech Interview with Helen Friedman, Partner and CEO at Merit Analytics Group LLC.

Helen, you’ve had a distinguished career in HR consulting and analytics—what inspired you to co-found Merit Analytics Group LLC?
After over 30 years working at Mercer, Aon Hewitt, and WTW (three of the largest HR consultancies), I realized that there is an insatiable need at the larger firms to “feed” their core business services. I’ve specialized in people analytics consulting and technology for a long time (maybe longer than I’d like to admit), and this service wasn’t—and truthfully still isn’t—core to these types of consulting businesses. That said, there are a lot of advancements being made, and to be the best that I could be in helping clients in this area, I jointly founded Merit Analytics Group, so that our sole focus was people analytics.

How does the Merit Analytics Group’s mission differ from traditional consulting firms?
Our mission differs in that we don’t try to be all things to all clients. We aren’t a big firm. We are a niche provider specializing in what we do best—people analytics consulting. Whether it is setting up a new function or a new business intelligence tool to capitalize on available HR data to deliver talent insights, we have the depth of experience to add value.

Reflecting on your 30+ years of work experience, how has the role of talent analytics evolved in helping organizations make critical workforce and business decisions?
There have been numerous points of evolution over this timeframe. First, the work is much more mainstream than in my early days in the field. Years ago, when I gave speeches, there might be a handful of PhDs in the audience. Over the years, that changed to packed conference halls with standing room only. This evolution didn’t happen because I became a better speaker (although I like to think that I evolved too) but because so many more organizations were realizing the value of having people data and insights to inform their decision-making processes. That evolution required a change in views on HR technology and the integration of that technology into business intelligence platforms, predictive analytics and the like. We went from a largely transactional view of HR data to a data-mining mindset to better attract, engage, develop and retain talent. It is no surprise that this evolution coincided with an evolution in the technology landscape, which has facilitated both access to and reliability of data—the latest iteration of which has been in leveraging AI. Especially for people analytics, where so many factors about people are interrelated and complicate our understanding of what matters, AI is giving us an even greater—and certainly more efficient—way to analyze massive amounts of HR data.

What are the most impactful ways that predictive analytics are changing how companies approach talent investments and optimization today?
Predictive analytics allows us to identify not only the root causes of people-related issues but also the relative magnitude of the impact of potential changes and investments.
By understanding this magnitude, organizations can determine which changes maximize their return-on-investment. It is a fundamental game-changer for HR, enabling risk/return analysis in a much more powerful and quantifiable way.

How can HR leaders balance the increasing reliance on data with the need to maintain a human-centered approach to talent management?
Better leveraging HR data isn’t an either/or decision. It is an “and” reality … without which we are missing critical insights to make the best decisions possible for organizational talent. Data allows us to blend what people say and how they actually behave in order to dissect what matters most. Without it, organizations often end up spreading actions like “peanut butter”, which either isn’t enough for some groups or gives more than needed to others. By definition, this approach is sub-optimal. The more HR and business leaders can use data to target actions, the better and more efficient those talent management actions are for organizations and their employees. In so doing, the insights from people analytics help leaders be better talent managers.

What factors are essential for successfully tailoring analytics solutions to unique organizational needs?
This is a great question. From my perspective, it all starts with the business strategy. This strategy ultimately impacts what and how we look at the data and what issues we try to address. Imagine, for example, two retail organizations: one is a value play and the other is a high-quality, high-touch model. To the former organization, turnover may be a cost of doing business (likely, at least in part, due to paying below market median compensation) but to the latter organization, where product and customer knowledge typically matter more, turnover may be a critical factor to look at and address. Starting with this business strategy focus, we also are able to prioritize people data differently. It isn’t so much about the volume and sources of data as it is the data elements that could inform what is driving the business challenge and what can we do to address it. I’ve seen too many organizations lean into collecting massive amounts of data that can have diminishing returns on investment. By focusing first and often on the business strategy and data that are more readily available, we can tailor our solutions and remove some of the barriers to making progress, honing in on data elements and sources that yield the greatest return on investment.

What role do dashboards and reporting tools play in enabling leaders to make informed decisions?
Dashboards and reports are essential educational tools. They help leaders understand patterns and trends in the data. These insights actually prepare leaders for predictive analytics by building this foundational understanding. I often say that dashboards and reports allow leaders to ask better questions. For example, I see that our turnover went up at the same time that profits went up, but that doesn’t make sense. With fewer people, sales and profits should have gone down. Why did this happen? Well, typically, when we lose people, we stop paying them, but there can be a lag effect before we see the impact on customer behavior, so it appears “profitable” to have higher turnover at a particular moment in time. That said, longitudinal analyses can uncover not only the true impact of turnover but also the root causes of that turnover to bring about meaningful intervention, where needed. While a dashboard or report can’t point to this solution, it can both identify what issues an organization might want to tackle and prepare leaders for the complexity of finding the right solution.

Looking ahead, what major shifts or innovations do you foresee shaping the future of human capital analytics over the next decade?
That shift has already begun, and it is AI. AI allows us to work more effectively with much broader data sets than ever before. Think about both internal and external data; think about the unstructured data that can be added to our historical reliance on largely structured data. AI still has a long way to go to be leveraged without significant human guidance—at least in the people analytics domain today. That said, a decade is a long time in the HR technology world, and I can imagine more advanced AI models being embedded in a seamless way to inform almost all HR processes at some point in the future.

What one piece of advice would you give to HR leaders who are just beginning their journey into data-driven decision-making through people analytics?
Start with strategy. Too often when we think about data, we start with data. While that seems appropriate, it can lead us to invest heavily in technology and services that do not optimize our return on investment. While some of this work may be required for various reasons (like compliance or data security), organizations are more likely to realize success in making more data-driven people decisions by starting with the use cases that matter to the business. What use cases can be addressed by a single data source or more limited data history? Start small in terms of data but big in terms of business value. This focus typically yields greater buy-in for people analytics, when organizations are first expanding into this arena. In truth, it is a great litmus test at any point in their journey into people analytics.