Behind the Scenes: How Facebook and Gap Inc. Leverage HR Data

According to the U.S. Bureau of Labor Statistics, the unemployment rate in October 2017 was just 4.1%, representing the lowest score measured in the 10-year reporting period beginning January 2007.

That’s great news for the American worker. But it undoubtedly creates a more complicated recruiting and hiring environment for HR professionals. Thanks to low unemployment rates and other complexities in current hiring practices, the State of HR Recruiting in the United States report by SAP’s WorkConnect found that:

• 97% of survey respondents say the quality of available candidates makes it difficult to fill an open job


• 93% say the availability of candidates in the labor market makes it difficult to fill an open job


• 91% find maintaining a pool of candidates challenging

Big data and predictive analytics can help remedy these and other HR challenges, but only if they’re implemented effectively and embraced fully.

“Today’s HR leaders have access to more data, greater breadth of context, and longer term pictures that put people at the center,” says Zoe Hart, VP of human resources at Upwork. “We also understand that for big data to be most successfully employed, it is imperative that we combine it with the heart of our function.”

 

Facebook Embraces Big Data for Pipeline Management

Ross Sparkman, head of strategic workforce planning at Facebook, suggests you start by understanding what the supply and demand are for your skilled positions. After that, you can develop a big data plan that’s in alignment with your business.

In the context of supply, this means understanding what talent is available in your internal, local, regional, national, and global pipelines. Anticipating demand requires predicting how your company’s need for different types of talent might change in the future.

Once you’ve explored these two factors, opportunities to add predictive analytics to your HR environment will reveal themselves. Starting without a plan like this could result in wasted spend or poorly allocated resources.

Key takeaways:

• Keep your competition top-of-mind while assessing your own supply and demand. Anticipating the needs of your competitors will help you forecast more proactively.


• Regularly ask pertinent questions, such as “Do we need to develop our own channel pipeline to potentially meet the shortage in supply?” and “How does our compensation strategy compare to the market?” Simply improving the quality of the questions you ask can have a major impact on the eventual success of your big data initiative.

Gap Inc. Shifts to Centralized Workplace Metrics and Analytics

For Gap, the challenge with big data wasn’t conceptualizing its talent pipeline or determining how to apply predictive analytics. Instead, the company struggled to navigate the different HR practices of its six decentralized brands—each of which had their own methods and metrics for reporting employment trends like turnover.

Moving to a data-driven decision-making process required the company to not just identify key stakeholders and train relevant business partners, but to build credibility for a centralized self-reporting process across a singular system of record.

Key takeaways:

• Standardize your language around big data. Discomfort with new technology and its related terminology can breed reluctance or resentment among team members. Making the process of adoption as easy as possible through the use of clear language and comprehensive training will help.


• Trust your team’s input. One of the reasons Gap succeeded was that it involved all HR participants when determining how to collect, measure, and calculate data. The result was a set of simplified, standardized processes that enhanced reporting, benchmarking, and tracking.


• Develop an internal definition of success. Getting reluctant stakeholders onboard will be easier if your efforts produce clear wins.

Launching Your Own HR Analytics Initiative

Based on the experience of Facebook and Gap Inc., the benefits of big data are clear. Yet, as the Data: The New Language of HR ebook reveals, few companies are fully capturing its potential.

According to research shared in the ebook, only 10% of 480 large companies studied had performed any significant analysis of employee data. Even worse, only 4% had reached the point where they could perform predictive analytics about their workforce.

Jumping from reactionary mode to fully-fledged predictive analytics implementation doesn’t happen overnight. However, as the ebook lays out, there are concrete steps companies can take at each stage of what it calls the Talent Analytics Maturity Model.

• Level 1 companies — those in “Operational Reporting mode”—should focus their efforts on hiring dedicated resources, improving data quality, creating a data dictionary and developing a 1- to 3-year plan.


• Those in Level 2 — who have achieved “Advanced Reporting”—can begin the process of implementing self-service reporting tools, work on data integration and build strong technical and consulting skills.


• Even those in Levels 3 and 4 who have met the standards for “Strategic Analytics” and “Predictive Analytics” will find suggestions on how to further deepen their use of big data.

For more on the four levels of the Talent Analytics Maturity Model—including how to assess your company’s current position, as well as how to move to the next level—check out the full version of Data: The New Language of HR for free.

Source: B2C