Recently at LinkedIn’s annual Talent Connect conference, I had the opportunity to share our experiences building a Talent Analytics team. Our team’s purpose is to help our leaders make evidence-based talent decisions that enable LinkedIn to achieve its vision and mission. Considering LinkedIn’s mission is to connect the world’s professionals to make them more productive and successful, it is important that we do that for our own employees.

We started the Talent Analytics team at LinkedIn 24 months ago, and I joined as the team leader six months later. As anyone who works in HR knows, getting started in talent analytics is not easy. When we began, we transferred a few analysts from talent acquisition operations to form a center of excellence in our Business Operations & Analytics department. Our two analysts were completely overwhelmed with reporting requests. We had no capacity to build automated dashboards or do advanced analytics.

We understand that many companies face similar challenges. LinkedIn data show that there are over 5,000 companies with Talent Analytics employees on LinkedIn. Over 70% of these companies have only 1-2 people.

Leapfrogging the maturity curve

HR data are messy. When we first started, business demand for operational data was more than we could satisfy. Every report took much longer than expected because of all the data cleaning. We concluded that no matter how many resources we added, with our current operating model, we would never move up the maturity curve.

What we arrived at is what we internally called a leapfrog approach. Basically this means we did not wait to complete the 2+ year journey to centralize our data before we focused on business impact. Instead, we took a number of actions:

Manage demand: minimize ad-hoc and operational reporting Build infrastructure: dedicated incremental capacity to automate dashboards Focus on impact: each analyst dedicates 40% of their time to answering analytical questions that address a specific business problem

Our resource allocation looks like:

During our first year, managing demand for operational reporting was the biggest challenge. However, as we provided data-driven insights to the business, the demand from executives for analytical work grew. Our focus on business challenges allowed us to build credibility with business leaders. Our transition from reporting to analytics required a lot of tops down support from our CHRO Pat Wadors, VP of Talent Acquisition Brendan Browne, and the rest of the HR leadership team. Their commitment is what allowed us to prioritize our work and invest in team resources.

The result of our leapfrog strategy is that our maturity development has not been linear. At the same time, we are always working on some combination of manual reporting, automated dashboards, and predictive modeling.

The way we work

We have observed that commonly, companies will separate reporting, dashboards, and analytics into different teams. One unique aspect of our current team is everyone does some combination of all three activities. It does not matter if you are a management consultant, IO psychologist, or data geek, everyone on our team learns to run regression in R, build a Tableau dashboard, and partner with the business.

Although our approach sometimes puts people out of their comfort zones, the advantage is our HR partners do not always differentiate a reporting, dashboard or analytics request. Providing a single point of contact allows us to iteratively frame what is needed to solve a business problem. Our generalist approach requires our team members to be more agile thinkers and grow skills they never imagined they needed. Seeing my team develop and helping them be successful inspires me every day.

Build vs. buy

Every talent analytics team must make a critical build vs. buy decision on where to store HR data. To achieve our vision, we decided to build our own HR data warehouse and work with a variety of partners for data visualization and analytics. This decision was based on our need to merge data from multiple HR and finance systems with internal data that we would never share with a vendor. That said our approach has required a huge time commitment from the analytics team. Even in our second year of existence, data quality is still our biggest challenge and opportunity.

What it takes to make an impact

Our team’s vision is to make talent a competitive differentiator for LinkedIn. We have purposely taken the approach of not focusing purely on predictive analytics. There are many instances where descriptive data are most effective in providing the fact base to make data-driven decisions. Our impact has not been limited by our analytical capability, but our maturity in partnering with HR to implement specific talent interventions that influence business decisions and behaviors.

Tell me about a time when you influenced a decision with talent data?

We get asked this frequently. That will be the subject of our next posts. In the meantime… we would love to hear from our peers. How have you grown your Talent Analytics team? How have you influenced business decisions? What was the business impact?