Building a Talent Analytics Department, as LinkedIn has, is an important way to identify and retain high-performers.
For businesses the world over, attracting and retaining a talented cohort of workers is an ongoing, high stakes and high cost endeavour. Employees not only represent a brand; they are also an intrinsic component of a business’s profitability. Therefore, accurately identifying top – and bottom – performers is of the utmost importance.
To that end, many businesses are building Talent Analytics apparatuses to measure performance and isolate top-performing employees. The Talent Analytics team at LinkedIn is a great case study – although it has only been in operation for 2.5 years, it has already gained the respect of business leaders and their services are in high demand. The processes by which LinkedIn built their team and managed their data should serve as a model for other businesses across industries.
Talent Analytics at LinkedIn
Lorenzo Canlas, head of Talent Analytics at LinkedIn, described the process of building his team at LinkedIn’s annual Talent Connect conference. HR built their Talent Analytics team around a core group of analysts transferred from talent acquisitions operations, thereby creating “a center of excellence” in LinkedIn’s Business Operations & Analytics Department.
The workload, at the outset, was overwhelming to say the least. Reporting requests inundated the nascent team, which only consisted of two people. Canlas notes that such a scenario is not unusual – of the 5,000 companies on LinkedIn that employ Talent Analytics personnel, 70% of these teams only consist of two people bearing the brunt of the work.
Initially, messy HR data delayed reports, and the team could not adequately meet businesses’ demand for operational data. Nevertheless, the team worked through its kinks and performed under the pressure, and ultimately, Canlas notes, their ability to pr6ovide data-driven results to business leaders bolstered the team’s credibility.
The Leapfrog Approach
LinkedIn’s success arguably depends on what Canlas calls the “Leapfrog” approach, which consists of three core tenets: the management of demand, the building of an infrastructure to automate dashboards, and an increased focus on impact.
It’s also important to note that members of the team did not occupy stratified roles per se. Instead, LinkedIn took a generalist approach, giving team members the opportunity to gain experience in all areas, from the dashboards to reporting and analytics. This encouraged analysts to think on their feet, develop a diverse skillset, and ultimately attain a more holistic view of the company’s operations.
HR Data Storage – Where and How to Store It
There’s also the question of HR Data storage. Businesses thinking of implementing a Talent Analytics team need to make the crucial decision between building and buying a storage system. LinkedIn opted to build, a decision influenced by the company’s need to merge data from multiple HR and finance systems. This decision may vary based on the specific needs of a company; regardless, much hinges upon it, as data quality is the crux of a successful talent analytics operation.
In an ever-competitive market, talent is key. The ability to connect hiring activities to business outcomes is a crucial step in cutting down the often huge cost incurred by poor hiring. As it stands, all businesses would do well to emulate LinkedIn’s model and adopt a quantifiable system for tracking talent data and its impact on the bottom line.
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