HRTech Interview with Dr. Andrea Derler, Principal of Research and Value at Visier

Dr. Derler, can you tell us a bit about your professional journey? How did you become the principal of research and value at Visier?

The way people and organisations affect, inspire and frustrate each other has always fascinated me. Early on in my career, I studied Philosophy in Europe, and on the side, I worked in various sales and customer service roles. During all these years, I observed the effectiveness and failures of good and bad leadership and management, and the effect on employees and customers. I took up a postgraduate degree in economics with a focus on leadership and organisations. Upon graduation, I joined the management consulting world as an HR and talent management research analyst where I got the chance to study HR and talent management practices of thousands of companies from a business, talent, and social psychology-based perspective.

Speaking to and learning from hundreds of HR and talent management professionals over the years made it very clear to me that there is one critical component for understanding the optimal way to utilize human capability and engagement for the organization: access to people data, and the ability to use it for the betterment of organisations and its workforce. So, when I joined Visier, a company that is unquestionably the market leader in people analytics, to research best practices in people analytics, my background in quantitative and qualitative research, storytelling and continuous learning about new practices and technologies came in handy. I strongly believe that no organisation can do meaningful and effective HR without working closely with their people analytics function; today’s business world is too complex to be based on mere assumptions and past experiences, so all HR programs and practices should start and end with data.

In your research, what have you found to be the biggest challenges that people managers face when it comes to handling people-data?

Most managers are used to making decisions based on financial and business-related data, but the inclusion of people data in their decision-making processes is still a muscle that needs to be built. In my interviews with leaders about the many challenges they face when trying to integrate people data into everyday workflows, a few issues came up more than others: first, the translation of people data seen on a screen into the actual business context is not always a straightforward exercise. For example, what does a “turnover rate” mean for their business, what are the reasons for a high or low turnover rate, how does their rate compare to other leaders or their competitors, and what should they be doing to improve it? To make these people data points useful and informative, managers need to see the benefit of using facts (=data points) versus gut feelings for their decision making. This precludes trusting the data, overcoming existing opinions and experiences, but also being able to learn to tolerate a residual level of ambiguity that comes with using real people data.

How do you think organizations can better support their managers in accessing and interpreting this data?

It is useful to specify the many functions, stakeholders and technology solutions available to organisations that can support managers’ access to, and the interpretation of people data. First, the people analytics and IT functions in organisations can help by providing the foundation—by enabling a high degree of data quality, mitigating the risk of data accuracy issues, and helping handle compliance, ethical and regulatory questions. These teams sit at the helm of people data, and hence often help reduce the complexity of statistical models with the goal of making people data digestible and useful. While these data-related issues are often seen as a hurdle, the pressure on organisations to figure them out is rising quickly as regulatory bodies and investors require more and more transparency around people data (for example pay transparency).

Secondly, if deployed, constantly evolving technologies like Visier then allow efficient access to people-data related business insights. The key words for managers at this point are secure, reliable and timely. Managers are neither willing nor able to wait for weeks to get answers regarding workforce availability or projected productivity patterns – they need answers for the next meeting. GenAI-powered technologies make it possible for managers to access quick insights that speak their (natural) language assistant style, here and now.

Thirdly, the interpretation of such insights can then be provided by so-called Human Resource Business Partners, who work in translator roles between HR and the business and who can help contextualize people related findings and insights with and for the managers.

Lastly, and in a best-case scenario, (example Standard Bank) companies can allow managers and executives to access a large variety of people data relating to their businesses. Imagine managers that can look up where their teams stand in terms of headcount, talent movement, PTO utilization, engagement scores, or team member’s average time spent on learning activities – it’s possible and it’s being done.

What are some of the most effective strategies or tools that can make people-data easier for managers to understand?

Based on my observations, to help managers embrace and use people data to inform their decisions requires a focus on impact, visual simplicity and speed of service. Lately, I spent some time reading through dozens of transcripts of conversations with HR and People Analytics professionals about what they are looking for in a solution, and the following three principles seem to guide conversations, inform analytics methods and technology purchase decisions.

Let me explain: the focus on the impact of a people metric on the business is most important and should both be part of any analytics strategy and factored into the choice of tools. To use a simple example: for a manager to appreciate the relevance of their business unit’s ‘turnover rate’, the discussion needs to focus on the direct and indirect costs related to employee turnover on productivity, employee engagement and the financial outcomes. Second, effective visualisations – by that I mean accurate, clean, and simple design and colors – help communicate bespoke turnover rate, for example, better than a complex chart that may confuse the reader more than it clarifies (few things will frustrate a busy manager more than struggling to know what the chart even says before they try to interpret it). Third, and I am repeating myself here, but the data point needs to be available promptly, instead of having to be analyzed by a team – this is where great technology comes in. Easy and swift access to the people data point needed and when it’s needed appears to be a key feature for managers to be motivated and interested in using it.

Can you share any examples or stories where simplifying people-data led to better business outcomes?

Countless examples of our Visier customers tell this story for us. Organisations often come to Visier with an inefficient, spreadsheet oriented approach to workforce planning which is cumbersome, lengthy, exclusive to a few analysts in a company, and therefore really difficult to use for decision making and planning processes. Simplifying people data for managerial and leadership use is key:

Inari now enables managers to make data-informed decisions about employee pay, Gore Mutual streamlined people data processes so that their leaders could help increase employee retention by 25%, and many others see the value of Visier’s GenerativeAI assistant Vee in making people data quickly and reliably answer people related questions on the fly. The trend towards opening up people analytics and data about the workforce to the wider leadership population for their businesses is unstoppable.

How can giving managers better insights into people-data help improve the overall effectiveness of an organization?

Many managers fly blind when it comes to people management, and what’s worse, they overestimate their own management abilities because they don’t have enough data. In fact, they often pride themselves with making “gut decisions” for example, when it comes to hiring new staff, when it comes to estimating how engaged their teams are, or when making compensation decisions. We know from countless studies including our own that these interactions between managers and employees – if mishandled – can drive talent attrition, lower productivity or employee engagement. For example, an underpaid high performer on a team is more likely to leave if the manager overpays that new and less tenured hire which can substantially hit the bottom line of the business, only because they didn’t know the market compa-ratios for each role (drawn from compensation data). Or, it has been shown that there is a close link between engagement and productivity, so managers are advised to take the results of engagement surveys seriously and act where possible in order to retain their most valuable employees. And so on.

In short, scientific and industry studies have shown over and over again that gut feeling alone does not always make a good advisor because of the inherent human biases that accompany our decision-making processes (we can’t help it). Don’t get me wrong: people data can support managers to improve their decision-making abilities, they don’t replace them. Any manager still needs to have the freedom to make the ultimate choice about which candidate they go for, how much to pay them, or how to react to feedback from their employees’ engagement survey. But they can lean on what they can know, which is people data.

What role does technology play in making the relationship between managers and data more seamless?

The role of people analytics technology is crucial for establishing a robust data infrastructure that enables a seamless experience for the manager, the end user. People-related data needs to be more accessible and actionable especially as productivity and performance are top concerns, and as regulatory bodies and investors demand greater transparency in areas like pay equity. Organizations face increasing pressure to refine their data management practice and emerging technologies, such as Visier, are delivering: they help revolutionize how businesses access and utilize people-related insights. These platforms offer managers secure, reliable, and rapid access to critical workforce information because in today’s fast-paced business environment, waiting weeks for data analysis is no longer feasible. Managers require immediate answers for upcoming meetings and decision-making processes. The integration of generative AI in these technologies enables natural language interactions, allowing managers to obtain instant insights tailored to their specific needs.

What personal strategies do you use to stay on top of data interpretation in your role?

In my role at Visier, I get to work with a team of amazing data scientists and together we study talent trends in the Visier Community Data of over 25 million live employee records. This work keeps me on my toes when it comes to interpreting analyses, for example, when we study questions such as: Is Turnover Contagious? How much more do Boomerang Employees earn when they return? or Does Manager tenure matter when it comes to team performance? As our studies become more and more complex, interpretations, contextualizing become trickier, especially in relation to the applicability of the findings for people analytics and managers. It’s never easy but that would be boring anyway. ☺

Two things work for me: for one, I have to remind myself with every new project to maintain a growth mindset: being OK to get things wrong and ask the “silly questions” without pride; test narratives and interpretations with others, and get their feedback; iterate, and improve. Rinse repeat. Second, I use and learn with GenAI tools such as Perplexity, that are immensely helpful when it comes to simplifying data points, the math behind them, how to talk about them, and with whom. Then I write my own story, but the GenAI assistant is really helpful in providing detailed, and vastly simplified interpretations that help me understand the data and talk about it with confidence.

What advice would you give to managers who are having a tough time with data interpretation and decision-making?

Managers who are given dashboards or charts to interpret from a people analytics team should not be shy to insist on simple, clear visualization for a start. They should not feel bogged down with complicated, unreadable, and hard to understand charts but ask for those that are telling an “obvious” story – their job is not to make sense of the insights but to use it for decision making for your business.

Get a second or third pair of eyes on every analysis or data point, to discuss what you are seeing and what the data point could mean for the team and the business. For example, if the organisation has a Human Resource Business Partner, discuss the data points with them, to get the interpretation right and don’t be too proud to ask the tough questions.

Lastly, what are your thoughts on the future of people-data in the workplace? How do you see it evolving in the next few years?

I see two fundamental ways in which the use of people data will become more prevalent than it is today. First, people data which currently still primarily lives in the HR and operational side of many businesses, will become more ingrained in everyday business decision making. Enabled by Generative AI, people data platforms are already more and more accessible to business leaders beyond HR because fast developments in AI innovations are making it easier, and quicker to access, interpret and use people data. See our above conversation about the impact of people data on the business.

For HR itself: I think the use of people data is going to be a much bigger driver of HR practices, across the HR function areas. It will replace traditional practices where measurement currently is an afterthought at best: it will steer HR practices rather than accompany them. For example, hiring data about how long it takes to hire for certain roles, what the quality of hires by source has been or what the most effective hiring sources are can be integrated into whole workforce planning efforts and therefore drive proactively when, how and where to hire talent. Or take Learning and Development: instead of just measuring the impact of a training class on user satisfaction, people data can and will be used to measure outcomes on productivity and engagement with more accuracy. The data exists, and the more organisations are working on integrating various HRIS platforms that hold data on learning, engagement, performance and business outcomes, the easier it will become to instigate development efforts based on outcomes looking forward.

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