Industrial-Organizational (I-O) psychologists have long placed emphasis on measurement, data, and the scientific method. These are key to adding evidence-based decision-making power to such things as selecting the right job applicants, understanding and influencing workplace climate, and diagnosing organizational development challenges.
Colleagues Tanya Delany, Sara Weiner, and Robert Gibby are among the coauthors of this review piece, arguing that our training, along with that of economists, business analysts, and mathematicians can help push the boundaries in the use of data for understanding, prediction, and influence. Much of our experience has foundations in flat-file or relational databases. Imagine rows representing people and columns representing attributes or measurements, and you'll get the idea. While a lot can be done with this type of data, it is estimated that only about 20% of the worlds data is so neatly structured. That leaves 80% of the available data in unstructured or semi-structured form (e.g., video, pictures, freeform text, sensor data).
As organizations struggle to find insights in the deluge of data that is available these days, expectations are high that understanding and recommendations will be forthcoming quickly, even in real time. Online shopping, where a list of recommended related items is available instantly, has provided just one example. Even online search provides data driven results that point us to where most others have spent time researching any given topic. This is how Google started down the path of information technology stardom, by providing smarter search results than others. In talent management, we have evolved from annual snapshots of employees to monthly, daily, or even real-time refresh updates. Historical trends across these snapshots can help provide models for predicting what future refresh updates might look like. Even in this one narrow area of the world, the data can become overwhelming.
The contrasts start to become clear when we look at controlled experiments with clean data sets versus analyzing data while still in motion and deciding what to capture and how to link it to existing data and models. It's like the difference between predicting tomorrow's temperature in your city and understanding patterns of our global climate. "The distinct value of I-O psychology in this new world of big data is in providing a behavioral science and theoretical overlay for the data considered, analyses used, insights drawn, and creation of ongoing processes and systems leveraging big data to inform decisions on talent in the workplace."
In short, math is only part of the equation.
Colihan, J., Brophy, V., & Steel, J. (2016). HR Analytics (R)eVolution Panel: Challenges and Opportunities for I-O Practitioners. Panel discussion conducted at the conference of Minnesota Professionals for Psychology Applied to Work; Minneapolis, MN.
Ducey, A. J., Guenole, N., Weiner, S. P., Herleman, H. A., Gibby, R. E., & Delany, T. (2015). I-Os in the Vanguard of Big Data Analytics and Privacy. Industrial and Organizational Psychology, vol 8:4, pp. 555-563.