We are just beginning to tap big data and use analytics to measure and improve recruiting and learning. But their future growth will depend on HR practitioners understanding what big data and analytics can do for them, their willingness to use the data to make decisions and change behavior, and mutual agreements around the ownership of data, privacy and ethical use of this information.
The overarching technical enabling factors that make it possible to tap this data are artificial intelligence (AI) and semantic search, both of which can now make sense of written and qualitative data for the first time. The promise of AI has been around for decades, but we are only now using it for qualitative data analysis.
Big Data & Analytics in Recruiting
- Over the next five 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.
- Matching candidates to jobs or tasks will be easier because we will have much deeper knowledge about what skills and competencies are 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.
- We will see widespread adoption of screening and assessment highly augmented by AI-driven algorithms over the next five years. HireVue has recently launched such a capability tied to its video interviewing software. By analyzing the candidate’s language, facial expressions, as well as numerous other characteristics, this tool can provide insight into the candidate’s personality and provide a probability of success at work that is unavailable otherwise.
The algorithms that are emerging may replace recruiters and will most certainly augment them to the point that a single recruiter can handle more volume in less time. Recruiting functions will get smaller, and some firms may decide to use outside resources that can access internal data sources for information and cultural insight.
Big Data & Analytics in Learning
- 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 them. Visual learners will see more charts and videos than a person who prefers to read. Those who lean more toward action 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 the learners progress.
- By looking at social profiles, Facebook posts, any material someone has written, and other personal data, a computer will know what they have already learned, what they reference, what books they have read, the level of language skill and the actual languages someone uses 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 or should learn based on your activities.
- 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.
The tools and capabilities of deep learning software may be able to replace most of what learning professionals do today. There will be less need for instructional designers, course developers, or lecturers. Learners will use a variety of tools to assemble a personal learning curriculum and will measure their progress with many computer- generated tests and activities. All of this can be augmented by crowdsourcing and tapping their networks for coaching or access to best practice.
Privacy and Ethics
None of these tools will work successfully without first dealing 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 definitive answers to the questions about data ownership – do you own your email, Facebook posts, LinkedIn data? Or does the organization you work for or the social network you use? Today social networks sell access to your data, albeit aggregated and anonymous, to third parties for marketing and analysis. They also use that data to serve information they think you will be interested in or to offer a product or service. 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.