Organizational theory, efficiency studies, network analytics, team dynamics, and other approaches have been applied to the workforce for nearly a century. Peter Drucker and his cohorts studied and wrote extensively about management practices. Many of the macro goals remain relevant, but of course the details change significantly over time. New technologies such as artificial intelligence (AI) and natural language processing (NLP), new external factors (remote work, the Black Lives Matter movement, #MeToo), and new relationships between companies, their employees and their investors have all contributed to a rapid sea change in the area of People Analytics.
Organizational change, including RIFs (Reduction in Force), Reorgs, M&A, and attrition is constant. And the rate of change is dramatically higher than when Dr. Drucker studied management. We have the opportunity to make these changes based on sound management principles, and to augment them with software, data and compute power. In that way we can provide a level and depth of insight that wasn’t possible just a few years ago.
Consider the example of a planned RIF to reduce 5% of your workforce. Typically, management would consider the most recent performance reviews, stack rankings, and general awareness of key projects and clients. They would attempt to minimize any known impacts. These techniques rely on semi-stale data, and certainly include both bias and blind spots. Often debates and turf wars occur as the “peanut butter pain” rule is applied. A best practice would be to model employee networks (both internal and external) for strength and sentiment. Measure and predict the impact of any changes utilizing data and algorithms. And prioritize replacement candidates for the most critical relationships. New AI/NLP models and software are now extremely accurate and cost effective, allowing mid-sized firms to deploy sophisticated analysis once the sole purview of the Fortune100.
The techniques are straightforward, and the desire to get near 100% coverage and accuracy is possible by utilizing all written communications within the enterprise as a baseline organizational model. Corporate email and messaging systems contain vast amounts of useful data. Measuring who talks to whom, how frequently, about what (topic) and in what tonality (sentiment, collaboration) will produce a very accurate heat map and graph of key relationships. Email and messaging systems are essentially “free” to mine and can provide current and trended insights nearly immediately.
It is time to apply technology for good to RIF and Reorg efforts. It is better for employees, better for investors and ultimately better for companies.