Talent Data Is Only As Good As How You Use It

Way back in 2005, in his now classic book, The Singularity is Near, futurist Ray Kurzweil described an eminent and optimistic future in which human life would merge with machines. This “singularity” would usher in a new age of unpredictable growth and change. 

Talent acquisition is now on the verge of its own singularity. The tools and processes we have used to recruit, interview, and retain talent for a hundred years will experience a seismic shift brought on by the twin tsunamis of big data and artificial intelligence. As organizations continue to improve their data capture capabilities, AI will gradually become better at identifying complex relationships among data points. For example, instead of wondering or guessing the type of background that leads to the most success as a call center agent, statistics will conclusively reveal the ideal profile.  

The Power of Big Data

Full data capture and analysis provides TA teams with a number of powerful capabilities: 

  • Hiring managers and recruiters will receive highly predictive and unbiased indicators of candidate fit without having to wade through endless resumes, application forms, background checks, interview results, and assessments. As new candidate information flows in, the overall indicators can be automatically updated to take data into account.
  • Candidates can receive real-time, relevant feedback on their fit for various positions. There is also no need for them to reapply to new jobs within your company because your algorithms will save their records and match them with new positions as soon as they’re available.
  • Your talent mobility system will automatically identify qualified internal candidates for relevant development opportunities and open positions that match their skill sets and experience. 
  • Your TA team can view simple scores showing the overall effectiveness, efficiency, and ROI of your hiring process — meaning there will never be any doubt about how well each hiring tool is working or whether it is worth the cost. 
  • The executive leadership team can also check your real-time talent scoreboard to see exactly how well your tools are working to elevate the performance of your company.

Big Data Is Only As Good As Access to It

While there are plenty of powerful benefits of big data and AI in TA, there is still a significant barrier preventing us from leveraging their true potential: access to the data in the first place. For all of the talk of big data and its vast capabilities, much of that information is not actually accessible. Here’s why:

  • A lot of data is stored by organizations “just in case” it needs to be accessed, not so that it can be analyzed. For example, if a rejected applicant files a discrimination claim, a company might need to provide the notes from the applicant’s interview to investigators. 
  • Personal privacy concerns lead to greater security and restrictions on how candidate and employee data can be accessed and analyzed. Of course, ensuring individual data is kept private is crucial – and becoming ever more important with GDPR, CCPA, and other global privacy guidelines. But by anonymizing the data, organizations can still protect candidates and employees while generating powerful, useful insights. 
  • Large organizations often have decentralized structures, meaning different regions or departments determine how to collect and store data, and they often do not coordinate to standardize and centralize data storage companywide. So, a business’ call center in Singapore might be run completely independently from its call center in Paris. As a result, the metrics that are collected may not overlap to a great extent due to a lack of standardization in how each location collects and stores data. 
  • Many types of data were not very analyzable in the past. For example, resumes — which are a type of unstructured data – could only be interpreted by a human reviewer until very recently. Now advances in deep learning and natural language processing are allowing machines to begin to make sense of resumes in powerful ways, but only if that data can be accessed. 

Steps to Reap Big Data and AI Rewards

Though there are many barriers preventing organizations from amassing the large and standardized data sets that data scientists crave, the first step to unlocking the tremendous power of advanced analytics in TA is gaining access to large sets of data. To realize the transformative benefits of big data and AI, organizations must get their houses in order with the following steps:

  • Conduct a review of all stored hiring-process data to understand what you have, where it is located, and what restrictions on accessing it en masse are involved.
  • If there are obvious missing types of data, see which processes can be implemented or actions taken to begin collecting them.
  • Conduct a similar audit for data on the post-hire side of the hiring process. Review performance metrics, ratings, and survey results across the organization. And work toward standardizing those sets of data to ensure they are accessible for analysis. Post-hire data serves as the criterion or target for pre-hire data, and without it, candidate tools cannot be calibrated to your environment.
  • Ultimately, organizations should establish data-governance policies and standards that encourage broad, standardized, and secure collection of data, while also allowing for aggregate and anonymized analytical exploration. 

Once this is complete, you will be in position to begin connecting different sets of data and analyzing them. This will show you how well your hiring process is working, which parts are redundant and ineffective, and ultimately how you can hire the best candidates with a speed that was previously inconceivable.

Eric Sydell is the EVP of innovation at Modern Hire, where he oversees all research and product innovation initiatives, including the data science-focused Labs team. He is passionate about applying machine learning, and deep learning in particular, to candidate data to match candidates with career opportunities more effectively. Eric is highly motivated to show the power of these new AI-based technologies and guide them to ensure they benefit individuals as well as organizations. Eric previously served as EVP, Innovation at Shaker International, of which he was also a founder. He was also a consultant at CEB/SHL.

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