Organizational success goes beyond mere metrics; it requires a thorough understanding of the factors that drive a productive workforce. This approach is central to the organizational consulting work performed by Daniel Freedman, who is a Senior Manager at PwC in London, UK.

In today’s conversation, Daniel sat down with Joel Lister-Barker, Host of Talking Data, to discuss topics including his journey in consulting, the challenges that he has overcome when working with data, and some of the tools that he has used to turn organizational data into insights.

Daniel's Journey

Joel: Can we begin with a brief introduction to yourself and your professional background?

I joined PwC as a graduate on the management consulting scheme and quickly ended up in the People and Organization team. Within this team there is a lot of focus on the operating model, but for me it mostly involved organizational design and people. During my 11 years at PwC, I have been involved in a wide range of projects, including some related to technology, transformation, and M&A. Most of my experience to date has been in the government and retail industries.

Joel: Many consulting firms are looking to leverage data more and more. During your time at PwC, how did the approach to using data evolve, from when you started, to what it is today?

I don’t think it has changed as much as it could have done. A lot of our projects are longer term when compared to other types of consulting, which has meant that we have taken a more manual approach to working with data. Over time some things have improved, such as having better access to benchmarks when we are building out a HR function for a client. We previously had an internal paywall to access benchmarking data, which meant getting access was quite difficult even though it was just another part of the PwC. I still think there is some way to go in my team.

Joel: It’s a well-known fact that there is a huge amount of data out there. What are some of the challenges that you’ve faced when working with data?

I’ll split my answer into two categories; client data and other data. With client data, quality is a huge challenge, including a lack of consistent formatting, missing data, and disparate systems that require additional mapping. In some cases, clients can’t easily tell us who works in the company, which means that our work takes a long time to complete. For example, when there are various sources of incomplete data available, we usually start with payroll data to build the employee baseline on the assumption that you wouldn’t be paying them if they weren’t an employee. When it comes to other data, there is so much information out there that it really becomes about what can you trust. This involves filtering through many different sources to exclude data that is assumption based, incomplete, or hasn’t been properly verified. There is also an issue with fake data, which is a broader issue beyond consulting. It’s difficult to know who and what you can trust.

Joel: On projects where there isn’t much client data available. What other data sources or tools have you leveraged to provide insights for your clients?

It can hard to create value early on a project while you are waiting for client data. In these situations, we would typically go down the non-data route such as using interviews, focus groups and employee surveys. They help us to get a feel for the organization and gain rough estimates for key metrics, such as asking a functional leader in an interview what they think is the size, scope, and cost of their team. This qualitative approach also helps us to quickly identify where the pain points are in an organization and when the client data does finally arrive, enrich our analysis. We would also look to get an outside view through benchmarks or any other prior project information that we have access to.

Joel: Having a structured approach to data analysis is an important skill for any consultant to have. What are some of the key metrics that you often use to assess your client’s workforce?

A key focus area is always around cost, which includes both the base salary and additional people costs to reach the fully loaded cost. There can be a lot of variability between additional people costs, with pension contributions being one of the key drivers. It isn’t always about cost and effectiveness though, so on some projects we might also look at demographic data, such as age, tenure, and gender. These can be important factors when thinking about the appetite for change within an organization. Ultimately, it’s mixture of an art and a science when working with both data and people. There is the rational side of using metrics and data to make decisions. But then there is the political and emotional side of our projects because we’re dealing with people. We try to balance this to get the best result for our client.

Joel: If you could go back in time, what advice would you give to your younger self starting out in your career?

Be really open-minded and embrace everything. For me, some of the projects where I haven’t been that excited at the beginning ended up being some of the best projects that I have worked on. Also find people where there is mutual trust, then work with them as much as possible.