How can you scale People Analytics in a Global Organisation?

As I wrote in The Rise of People Analytics, the field is undergoing significant growth in interest and adoption, with organisations that have developed advanced capabilities witnessing positive improvements in both business outcomes and employee experience.

Perhaps it is not a surprise therefore that many people analytics leaders I speak to are significantly scaling their teams as their business impact grows and the scope of work they do continues to broaden and deepen.

One such leader is Guru Sethupathy who took on the role of Head of People Strategy and Analytics at Capital One in March 2017. Since then Guru, who has a background in Economics and Computer Science and previously worked at McKinsey, has tripled the size of the team to 75 people. I caught up recently with Guru to discuss the journey of people analytics at Capital One, and what lies ahead.

1. Guru, it’s been nearly two years since you joined Capital One as Head of People Analytics. What have been some of the key learnings during this time?

It has been an incredible journey these past two years, and as the size and impact of our team has grown, we have learnt some important lessons with regards to our progress. These include:

  • Insights are insufficient; converting insights to action is of utmost importance

  • Invest in data quality; without high quality data, all other aspects of our agenda are meaningless

  • Invest in learning about the business; it enables you to ask better questions, make more targeted recommendations and build your credibility and influence both within HR and the business

  • Associates are generous with their data; think seriously about data privacy and ethics and develop guardrails. We should also reward employees with insights that help them develop their careers as well as improve experience and wellbeing

  • A lot of value can be delivered without using machine learning. If you do decide to use machine learning, do so thoughtfully and place guardrails. If machine learning goes awry, it can quickly destroy value

  • Differentiating correlation from causation is important. Sometimes the former is sufficient, sometimes the latter is necessary; there are a lot of confounding factors in our work

  • Storytelling is important and a real differentiator for driving influence, action and impact

  • No measurement > bad measurement; invest in improving measurement but also be willing to acknowledge that not everything can be measured well enough

  • Be thoughtful and clear about who your customers are; they can vary and having that clarity will lead to better designed products, projects, and impact

2. It’s quite an achievement to have scaled the people analytics team from 25 to 75 in less than two years. How is the team organised? How has this evolved over the course of the journey? What is the vision of the team?

One of my first goals was to build a centre of excellence. I believe there are four reasons for creating a centre of excellence: Synergies, Standardisation, Talent and Data Access (see also FIG 1).  

FIG. 1

FIG. 1

Next, I wanted to be vertically integrated to improve efficiency and quality. For instance, data management was not part of the team initially but by bringing it into the team, I feel the interaction between the managers of data and users of data (e.g. our analysts) has become more efficient. Now we own the process end to end, from HR data management, to products and insights, to strategic consulting.

  • We have a data and products team that owns data management and governance, reporting, enterprise survey, and products.

  • We have strategy and analytics teams aligned with each of our five lines of business.  Each of these teams also own a functional area such as enterprise diversity and inclusion, compensation and benefits, people leadership, etc.

  • We have a team dedicated to providing data, products, insights, and strategic support for recruiting and selection.

  • And finally, we have a team focused on the key strategic talent projects for Capital One.

3. What’s your philosophy to hiring and talent development of the team?

We have a lot of diversity from a skills standpoint on our team. This includes data engineers, data scientists, product developers and visualisation experts, economists, IO-psychologists, and consultants. Our talent philosophy has been to hire talented general athlete problem solvers, who are passionate about human capital. Very few of our hires actually have experience in HR or People Analytics and that is okay because I think we are trying to build something new and different.

Analytics is often used to define a broad spectrum of work – from pulling data and simple calculations to advanced statistical modelling and machine learning. We want our team to be focused on our comparative advantage, which is the advanced analytics side of that spectrum. To solve for the more basic side of analytics we have a two-fold strategy: (i) build easy-to-use, sometimes intelligent, products and tools; (ii) help our HR partners on their upskilling journey for basic data needs

4. I remember when we spoke in 2017 just after you’d assumed the role at Capital One. We discussed the improvements you wanted to make in measurement and identifying actionable insights. Please can you describe how you have achieved this.

We have an agenda to advance how we measure areas like engagement, inclusion, people leadership and quality of hire, to name just a few examples. We blend the best of academic research and empathy interviews with our HR and business partners to create metrics that are sound, useful and aligned to key business priorities.

We have partnered with others to improve the quality of data in a multitude of ways (e.g. increasing response rates on surveys, implementing HR tools and systems to improve the capture of data, better detection and reduction of data errors). 

Finally, we do statistical analyses to identify the drivers of key metrics in order to develop actionable insights.

There is still a final step of converting insights to actual action and we are thinking through a model to improve that “last mile” of impact.

FIG. 2

FIG. 2

5. You’ve clearly made some significant progress with people analytics. What has been the feedback from i) HR and ii) the business?

The feedback from both HR and the business has been awesome. There is a strong thirst for all things data and analytics at Capital One and people analytics is no different. The insights provided from the work of our team has enabled leaders and associates to have wholly different types of conversations around talent. That being said, there are a couple of challenges that also arise in this context.

First, everyone wants to see all people data and we have to place some governance structure around data transparency to protect our Associates’ data. Additionally, it’s easy to drown in a deluge of data. This means we need to provide guidance to help our customers use the data and insights in the most impactful way.

6. I know the importance you place on enabling behavioural change through people analytics. How have you approached this? Where are you on the journey with regards to embedding behavioural change?

Our theory of impact is three-fold. First, we hope to use our insights to shape talent-related strategy for our business and HR partners. Second, we are building real-time descriptive, predictive, and prescriptive products to help business leaders make better talent decisions.

But I don’t want our work to only be for leaders. So third, people analytics also needs to directly benefit our associates. I think we can provide tremendous value here by helping associates find their career path and be the best versions of themselves at Capital One via behavioural nudges. We are early in this journey but have many pilots in place to test and learn about the effectiveness of nudges.

7. As the team has grown, how has your role as Head of People Analytics evolved? What are your key responsibilities?

I spend the vast majority of my time focusing on the following four areas:

a.     Talent /organisation of the team: shaping our talent philosophy, attracting and interviewing talent; coaching and developing team members; thinking about the organisational structure of our team to identify efficiencies and better interaction models

b.     Strategy and prioritisation: Setting the strategic priorities of our team

c.     Trusted advisor: Being a thought partner, adviser, and consultant for certain HR and business leaders on their talent strategy

d.     Learning and sharing: I spend time reading, listening, and investing in my own learning. I also write and speak both internally and externally.

8. Please can you outline the main enablers that have supported the people analytics journey at Capital One.

There are two critical enablers at Capital One that have provided us with a positive tailwind. First, data and analytics is core to the DNA of Capital One. This means that there is a real pent-up demand for data and insights on talent from the business. We didn’t have to create demand or convince people of the value of our work, it already existed. Second, our CHRO was a leader in the business prior to moving into HR. He has both a deep understanding of the business and significant influence on the executive committee. So, through him, we know we are working on the most important talent challenges for the business, which in turn enables us to have more impact.

9. Finally, what’s next on the people analytics agenda for Capital One?

I want to be hyper-focused on impact. I have already discussed our three-pronged theory of impact above (see Q6). But how do we measure and manage against that? How do we partner, collaborate, use technology, build products, etc to drive that impact? How we get our insights into the right hands at the right time, to drive better decisions and behaviours? We are developing a framework and strategy to answer all these critical questions.

In our first two years, we have heavily invested in what I call People Analytics “infrastructure” – data, measurement, building relationships with key stakeholders, basic dashboard and product suite. Over the coming years we will be leveraging and building on that infrastructure to drive impact.


Thanks to Guru for sharing his inspiring story of scaling people analytics at Capital One. You can follow Guru on LinkedIn.



Guru Sethupathy is VP, Head of People Strategy and Analytics at Capital One. Prior to joining Capital One, Guru has spent the last decade researching, teaching, and advising universities, companies, and policy makers on the impacts of globalisation, technology, and AI, on the workforce. He spent time as an assistant professor of economics at Johns Hopkins and as a consultant at McKinsey. You can follow Guru on LinkedIn.  


David Green is a globally respected writer, speaker, conference chair, and executive consultant on people analytics, data-driven HR and the future of work. As an Executive Director at Insight222, he helps global organisations create more cultural and economic value through the wise and ethical use of people data and analytics. Prior to joining Insight222, David was the Global Director of People Analytics Solutions at IBM Watson Talent. As such, David has extensive experience in helping organisations embark upon and accelerate their people analytics journeys. You can follow David on LinkedIn and Twitter and also subscribe to The Digital HR Leader weekly newsletter.