Published
12 Oct 2017

How is data transforming HR at Facebook, LinkedIn & Salesforce?

12 Oct 2017

HR is on the verge of a data-driven transformation - but where do you start? Insights from HR leaders at Facebook, LinkedIn and Salesforce. 

This article is provided to Changeboard by our Future Talent 2018 partners, Heidrick & Struggles. You can see Heidricks' Managing Partner Colin Price in conversation with Aviva's CEO, Mark Wilson, at Changeboard's Future Talent Conference 2018.


We recently hosted a panel discussion where three data-driven HR leaders - Tito Magobet, senior director of talent acquisition for global R&D at LinkedIn; Dr. Ernest Ng, senior director of employee success at Salesforce; and Ross Sparkman, head of strategic workforce planning at Facebook - offered their thoughts on how to build impressive teams and affect organisation-wide change.


Facebook: using data to influence policy change

At Facebook, the leadership didn’t need to be convinced that we needed to take a different approach to analysing our people. But no matter where you are, a crucial first step is to partner with other leaders, including finance, to identify the organisation’s key drivers of growth. 

You can then relate those drivers through regression analysis to people growth. You can create models that basically show that if we have X number of projects on the horizon, that equates to needing Y number of people. This approach takes some subjectivity out of the budgeting process - for example, when someone says: “I want 10 people for this team.” Now the discussion becomes: “OK, help me understand why you want 10 people, because the data suggests that you need 8 people. What’s the business case for those other 2 people?”

Recently, Facebook has been in the news because we implemented a four-month parental leave policy for both male and female employees around the world. That decision was driven by data; my team performed a cost-benefit analysis to understand the opportunity cost of retaining certain individuals, as well as the cultural well-being and workforce engagement that such a policy would create. We worked directly with finance and sales operations, and we found that the ROI would substantiate making the up-front investment.

You can also run a cost analysis on other variables, such as whether it’s better to pay more for experienced hires or less for inexperienced hires. At Facebook, I got the conversation started by developing a model that showed we could have saved money by building our workforce through an investment in more experienced hires.

 

LinkedIn: reducing time to identify talent 

At LinkedIn, we’re exploring methods for scaling talent identification. One approach involves distilling 80% of our core engineering hiring down to 10 talent profiles. This has allowed our sourcing team to use LinkedIn data to drill down from a base number of prospective candidates to an actionable pool of high-quality talent that maps to our organisational needs. Candidate research that used to take us months can now be accomplished in a fraction of that time.

We also wanted to get a better sense of the makeup of LinkedIn employees - their schools, degrees, titles, tenure - so we could identify gaps. We built a model that can instantly analyse LinkedIn data from our employees’ public profiles and return insights about our workforce - things like years of experience, tenure, previous work experiences, and academic information.

When I joined LinkedIn, we didn’t have the benefit of an analytics function. But the company was growing, doubling its head count about every year, and we knew we had to think about our talent in a different way. We launched a “tiger team” approach - bringing together a diverse group of specialists, including a recruiting leader, an HR analytics expert, an HR business partner, and an engineering client - to combine our functions and efforts to solve business problems.

 

Salesforce: understanding performance using predictive analytics

My team at Salesforce works on predictive analytics using machine learning, among other things. Building the data set necessary to reach valid conclusions in predictive modeling projects takes a long time because they depend on lagging indicators such as sales performance.

When you get started, people will ask you to provide answers - and you may have to say: “Well, it’s going to take a year because you didn't set up data collection the right way.” I think it’s important for all companies to start looking at the internal data they have, even if it’s not immediately related to the ideas they want to explore. The data may be tangential, but it can help them start to understand their workforce in a richer way, rather than just what’s in their HR information or applicant tracking system.

At Salesforce, our analyst team gained credibility by helping our HR team better understand sales performance and attrition. We started with an advantage because our salespeople use our own Salesforce platform, so we merged those data nuggets with people data into our core platforms to demonstrate to our leaders that the data we were capturing in our sales platform can actually start to drive people strategy. That was a pivotal moment for us—when we gave our sales leaders the metrics to transition from managing deals and accounts to managing people.

 

An article detailing the full panel discussion originally appeared in the Heidrick & Struggles Knowledge Centre.

 

See Heidrick & Struggles at the Changeboard Future Talent Conference.

About the authors 

Rebecca Foreman Janic is a partner in Heidrick & Struggles’ Menlo Park office and a member of the Global Technology & Services Practice. 

Brad Warga is a principal in the San Francisco office and a member of the Global Technology & Services Practice.