Big Data & Analytics in Recruiting & Learning

by Jun 8, 2016Learning, Talent0 comments

Big data and analytics are just beginning to be tapped for recruiting and learning, but their future growth will depend on HR practitioners understanding of what big data and analytics can do for them, their willingness to use the data to make decisions and change behavior, and agreement around privacy and ethical use of this information.

The overarching technical enabling factors are artificial intelligence (AI) and semantic search, which can now make sense of written and qualitative data for the first time. The promise of AI has been around for decades, but is only now being used effectively for qualitative data analysis. 

Big Data

Big Data in Recruiting

1. Over the next 5 years, using tools like IBM’s Watson, researchers will examine Facebook posts, Tweets and other social media postings of people who have applied for a particular position and been hired. Then it will use this data to weed out candidates whose profiles do not match the patterns of successful hires. Recruiters will be presented with lists of candidates ranked by the probability that they will be good hires.

2. Matching candidates to specific jobs or tasks will be easier because we will have much deeper knowledge about what skills and competencies are actually needed to achieve a goal, rather than the subjective inputs we have today. Google recently performed an analysis of the data it had accumulated on successful hires, their skills and competencies, and their performance. The data was so different from their earlier assumptions, that they radically changed their recruiting practices.

3. We will see widespread adoption of screening and assessment highly augmented by AI-driven algorithms over the next 5 years. HireVue has recently launched such a capability tied to its video interviewing software. By analyzing the candidate’s language as well as numerous other characteristics, this tool can provide insight into the candidate’s personality that is unavailable otherwise.


Big Data in Learning

1. Learning materials will be assembled on the fly based on learning style. By carefully watching how people respond to different media, for example, computer algorithms will adjust what media works best for you. Visual learners will see more charts and videos than a person who prefers to read. People who learn by doing might be presented with simulations or activities to complete. Learning progress will be measured continuously and the material adjusted by difficulty and depth based on your progress.

2. By looking at your social profiles, Facebook posts, any material you have written, and other personal data, a computer will know what you have already learned, what you reference, what books you have read, the level of language and the actual languages you use and much more. It will then be able to provide you with very personalized learning on demand. It may even be able to predict what you will need to learn based on your activities.

3. Much of your learning will be presented via mobile learning apps where small amounts of information are made available. Performance will be measured and difficulty will increase along with your learning speed.


Privacy and Ethics

None of these tools will work successfully without dealing first with the issues of privacy and ethics. Every action and result above is only possible with access to personal data. Developers are already struggling with the relationship between what is possible and what is legal. We do not have clear answers to the questions about data ownership – do you own your own email, Facebook posts, LinkedIn data? Or does the organization you work for? Or Facebook or LinkedIn. Today both of these firms sell access to your data, albeit aggregated and anonymous, to third parties for marketing and analysis. Do you have the right to control how your data is used and by whom? Who can access your data and to what extent? Can it be used for making decisions about employment, criminal activity, tax evasion, or divorce?

I predict some major challenges to the technology over these issues, but they will be hurdles, not roadblocks. We will learn our way through these and evolve to find useful ways to leverage the power we now have.

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