The vast amount of data now available offers businesses with the potential to gain more insights than ever before. However, one key challenge is to ask the right questions in order to gain meaningful insights. As Head of Assessment at RHR International (‘RHR’), Dan Russell addresses this challenge head on to help his clients build the strongest possible leadership pipeline for future success.

In today’s conversation, Dan sat down with Joel Lister-Barker, Host of Talking Data, to discuss his background in consulting, how working with data has changed over the years, and learn about the metrics used by many businesses to evaluate leadership candidates.

Dan’s Journey

Joel: Let’s kick off with a quick introduction to yourself and what led you to your current role as Head of Assessment at RHR.

My background is in industrial-organizational psychology (a.k.a. occupational psychology for those in the UK). I started out at a research consulting firm in Washington DC, which was very data oriented and involved performing lots of cluster analyses on the working population in the United States. This was the beginning of working with data and really set me up for success in the rest of my consulting career. In my current role at RHR, I focus on leadership development and assessment for the CEO, C-suite, and pipeline into those leadership roles.

Joel: I understand that throughout your career you've integrated data analytics with talent management. How have you seen the role of data evolve in the field of talent management and organizational effectiveness?

It’s literally been over 30 years, so it’s a long time to look back at! The first thing that comes to mind is that there is significantly more data available today. Early in my career it was hard to collect data. We would have to use things like surveys, while now everything is being measured. For example, we are speaking on Microsoft Teams, which will automatically track that we have had a conversation, for how long, and record everything that we have talked about. The other thing is that clients are so much more data savvy today with the use of data scientists and analysts. The introduction of these internal roles has meant that the level of data literacy has increased quite a lot within many organizations. So, we have a situation where it has flipped from having lots of questions, but not having enough data 30 years ago - To now having lots of data, but not quite knowing the right questions to ask.

Joel: With your experience in people analytics, what are some of the most significant challenges you’ve encountered when working with large datasets, and how did you overcome them?

With huge amounts of data now available, the first main challenge is cleaning it all up. Collecting data from many different sources usually means there is a lot of data wrangling involved to present it in a standardized way. This was a challenge early in my career and still remains a challenge today. The difference is that the data sets are much larger today, so we now need more tools to clean up data efficiently. Another challenge that is coming up more often is using meaningful data only. With so much data available, we can confuse access to data with the thoughtful question of “Is this a good measure for the question that I am asking of the data?” Thinking about what kind of data you need to answer your question early on is critical. Further to this, it is up to consultants and those of us in the professional services industry to be really specific around what the question is that we are answering. Every question should be well understood and defined during the scoping process to really add value to the client at the end of the day.

Joel: It sounds like you’ve worked on many projects across different industries and geographies. Can you share some key metrics or data points that you find most valuable when assessing a client's leadership pipeline?

The metrics that I usually work with are focused on sustainability and viability at the top levels of leadership. We’re often undertaking leadership assessments of mid-level leaders in an organization to assess their scalability, which is their ability to take on jobs of greater scope, scale, and complexity. This is to help the organization with their succession planning, talent management, and leadership development efforts. It involves assessing the type of leaders that already exist in the organization, such as whether they are strategic thinkers or operators. We then also look at external leadership options for the organization. There’s this phrase that we use called “multi-trait, multi-method”, which involves looking at different leadership traits and assessing them using multiple methods. For example, with internal candidates we often run psychometric testing and in-depth interviews to understand their leadership impact and potential within the organization. For external candidates, we will run these psychometric testing and interviews, but also compare their resume against publicly available information to benchmark their experience to what we would expect for the role. It’s this combination of data points that allows us to effectively assess the leadership pipeline for an organization.

Joel: When you have clients who are looking for insights on their current workforce, but only have limited amounts of people data available, what alternative data sources or methodologies do you typically use to provide those insights?

There is lots of data out there. The first thing that I would say is to ask yourself whether the data doesn’t really exist or whether you can source it relatively cheaply. The second part to my answer is to think qualitatively - Do some interviews, talk to some folks, or run a focus group. This is also where the power of Artificial Intelligence really comes in with its ability to analyze qualitative data much more efficiently than ever before.

Joel: Is there anything else that you would like to share about using data effectively in the world of consulting?

Take a step back and really think about the business question. Start with what challenges your business is facing, then think about the questions to ask that will address these challenges, and finally leverage the data to overcome them.

Joel: If you had a magic wand that could do anything with data, what is the one thing that you would make it do?

Auto clean – The ability to quickly turn messy and disparate data into structured and connected data. Another thing would be increased data literacy, so that people ask the right questions early on!