Optimization, Trending and Reflective Learning for Better Employee Analytics
Optimization, Trending and Reflective Learning for Better Employee Analytics Key words: Employee surveys, optimization metrics, trending data, reflective learning and diversity and inclusion
Optimization and Trending are Key to High Quality Analytics Initiatives
With escalating interest in analytics and data, leaders are spending large amounts of money and time finding the right data that will lead to better decision making. When it comes to employee survey data or perhaps even metrics focused on customers, two key aspects have received less attention, and in this short article, I review the importance of these two concepts. I do so through an example from the Leadership Pulse data. Most scales and metrics associated with data collection from individuals at work (customers or employees) tend to focus on the importance of maximization. Organizations collect data to maximize scores on satisfaction or engagement on a variety of topics (e.g. pay, leadership, resources at work, etc.). However, should all these metrics be based on a maximization scale? Should employees be maximally satisfied? Do we perhaps need some level of dissatisfaction to drive individuals to make their situation a bit better? At least one area of work that I have studied since the late 1990s, employee energy, is not best studied by focusing on maximization. With over 1 million data points collected from employees around the world, we have been able to analyze the predictive capability of studying average and optimization scores on energy. In all cases, optimization wins. Energy is defined as the ability to do work; this definition comes from physics. And in physics or sports physiology, energy is understood to be an optimization construct. The sports analogy is well understood by anyone who begins an exercise program. When working out, we are told to measure our pulse, and no doctor or coach suggests that we always maximize our body pulse. You find out the zone where you will be at your best, where you burn optimal calories, and you learn how to exercise in the zone. Absolute value of energy gap predicts performance We learned the same concept applies when we study employee energy at work. We ask employees where their energy is today, where they are at their best, and then we calculate the absolute value of the gap between those two numbers. Employees with higher individual performance, higher sales, higher quality scores, who are more prone to stay in their jobs and who have better safety records all have lower gaps between optimal energy (where they are at their best) and working energy (energy today). Our questions focus on an energy scale that ranges from 0 to 10; both 0 and 10 are suboptimal numbers. At a pulse that is too high, it can cause damage. A ten at work means that you have so much input and work that you cannot process it all; you are at risk of burnout and can’t sustain at that level. Thus, people who report a ten need help. Our job as managers, leaders and coaches or as individuals managing our careers is to learn how to keep energy levels at rates that are optimal vs. highest. We find that people in different occupations report differing optimal energy levels. For example, sales people say they are best at a 9, while engineers and programmers usually report they are at their best at a 6 or 7. The difference is that one job requires high levels of interaction and rejection, while the other needs time to concentrate. Trending is also key to helping employees be at their best Our other key learning is that asking about energy once a year does not help a leader evolve into a better leader, not does it help employees learn to optimize their own energy. Trending data with more frequent reporting and learning makes a positive difference. Consider the example below: This graph show data rolled up from the Leadership Pulse from 2004 to 2018. The shaded in area represents the zone where respondents report they are at their best (optimal energy), and the single line below shows where they are at the time of the pulse survey (working energy). Only in 2006 and in 2015 did the leaders come close to being ‘in the zone.’ These data led to a tremendous amount of learning over the years. The biggest insight may be in the importance of reflective learning as part of any metrics strategy. Reflective learning: An opportunity to take your analytics strategy to the next level
In our work on employee energy, we learned that employee energy fluctuates dramatically; thus, we set up our process of data collection to be more frequent than what is used with most other employee data collection strategies. We often collect data as frequently as weekly, and in some cases, we have done daily work. Most people think this is an unreasonable thing to ask of employees; however, because we created from day one a learning process that provides feedback directly to each employee, we are putting employees in the middle of the learning and improvement process. The energy optimization frequently process is an engagement tool for individual employees and leaders.
When employees get their own data, we teach employees and managers to use reflective learning techniques to understand their data and make their own personal changes to see positive results. Employees get their own data, reflect on what they see, focus on understanding cause and effect and make changes. Employees own the data; employees own the change; employee and employers benefit from the results. It’s not a once a year process, and it’s bottoms up vs. tops down learning with small, continual behavioral changes. The Case of Diversity and Inclusion What better use of reflect learning, optimization work and trending than diversity and inclusion. This is an area receiving great attention today. That’s because after years of top-down training initiatives, we are discovering that much of what we thought was being learned is not being acted upon. Diversity and inclusion initiatives, just like employee engagement, should not be once a year initiatives. We can’t force people to change their attitudes and behavior. Learning processes that use reflective learning, reinforced with data, can have significant changes immediately and ongoing. Much of the focus on employee data collection work is done under the auspice of improving the business, however, in too many cases a lot of money is put into data collection efforts done for public relations purposes rather than for internal improvement. The questions asked are usually not relevant, and employees are not using the data to improve their own lives. This is a big miss. Not only are the concepts of optimization and trending important for anyone seeking an effective metrics strategy, but being willing to share ownership to employees, or using employee-centric engagement™ as a strategy will be key to gaining competitive advantage.