Lorenzo Canlas, Head, Talent Analytics, LinkedIn
People analytics helps businesses make evidence-based talent decisions for all phases of the employee lifecycle. The field is new and everyone is getting into it: approximately 5,000 companies have people analytics departments, with more than 50% having been created in the past five years. In this session, Lorenzo will share LinkedIn’s journey with building their own people analytics function, including the evolution of their infrastructure and technology, organization design, and their leapfrog strategy of focusing on delivering business value while building out a data infrastructure. Attendees will learn the values of people analytics to business problems, how to build out a people analytics team and a maturity model for the team.
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8. Leap-frog strategy
What we had Business
demand
Our solution
Analytics Infrastructure Reporting
Team resource allocation
§ Building the IT
infrastructure is a long
journey…
§ Reporting will consume
100% of capacity and
never be 100% accurate
§ Prioritize quick wins that
solve business problems to
build credibility
10. How many engineering recruiters do we need?
Forecasted hiring needs
# of Hires
Headcount forecasts
# of FTE
2015 2016 2017 2015 2016 2017
11. Are we hiring the right mix of people?
Org. shape has shifted over time
% of Engineering FTE
2013 2014
2013 2014
Senior+
Mid-Level
Entry-Level
Hiring has focused on entry level…
% of new hires
12. Partnered with HRBP and talent acquisition leads to
double mid-level and senior hires
# of new hires
1H 2014 1H 2015
Senior+
Mid-Level
Entry-Level
13. What are the most attractive
regions to hire SW engineers?
14. Supply of software engineers in region
Demandforsoftwareengineers
Findings: Labor InsightsWhat is the supply and demand for SW engineers?
Seattle
Chicago
Boston
Washington D.C.
New York
SF Bay
Houston
Denver
Philadelphia
Atlanta
Dallas
Toronto
LA
Raleigh-Durham
Montreal
Austin
San Diego
Detroit
Minneapolis
Phoenix
High
Low
Low High
15. Findings: Labor InsightsUsed profile data to classify SW engineers into tracks
*18 most common skills among LinkedIn’s current engineering HC: Java, Python, Linux, Distributed Systems, C++, JavaScript, Hadoop,
Scalability, C, Algorithms, Perl, Software Engineering, Git, Unix, Software Development, REST, Agile Methodologies, Ruby
LI Profile features
LI Profile
Features
Candidates from ATS
Machine learning
algorithm
Classification model Classified profiles
TrainPredict
16. Findings: Labor InsightsWhere do we find critical skills?
Engineering track concentration by region
Below average Above average
Systems &
Infra Apps Data Mobile
Eng
Manager
Eng
Services OpsIT
18. What we have learned in the past 18 months
§ Focus on solving business
problems with data
§ Prioritize quick wins to build
credibility
§ Partner to drive change and
business impact
19. Where do we go from here?
§ Diversity & inclusion
§ Workforce strategy
§ Leadership
§ Quality of hire
§ Talent metrics and business
outcomes
20. What can talent analytics do for you?
§ Think of one business question
for your talent analytics team to
solve… scope project for ~1-2
months
§ Make sure the team has capacity
to focus… the “cost of yes” is
asking the team to re-prioritize
existing commitments