People Analytics: Here With A Vengeance
I’ve been studying People Analytics for almost 20 years now and this world has really changed. In this article I’d like to give you some insights on the explosive growth, and explain some of the new research we just published.
1. People Analytics has grown up – it is now an established discipline in business.
For years the discipline of HR analytics, training analytics, or people analytics was considered a nichy, backwater part of Human Resources. You may have had an I/O Psychologist analyzing engagement data, or a data analyst looking at the impact of various training programs, or you analyzed job advertising to figure out which ads produced better candidates.
Each of these projects was typically done by a technical individual, often a person doing it in their spare time, and there was a small budget for analysis tools (spreadsheets were always the most popular) or a few specialized tools that helped collect data in that domain of HR.
The projects were often used as a way to “cost-justify HR investments” so we looked at the ROI of various training programs, variations in employee engagement and where managers needed to focus on employees, or perhaps how pay was unevenly distributed, the distribution of performance ratings, or other topics which helped the company improve its various HR programs.
I met with dozens of groups like this over the years and always did fantastic work – but the two issues they typically raised were (A) we don’t have enough budget to expand this work into an enterprise-wide program, and (B) the quality of our data is poor and we don’t have the money, infrastructure, or IT support to build a complete data warehouse (now called a “data lake”).
Today, I’m happy to say, all this has changed. With the increased focus on measuring diversity, gender pay equity, skills gaps, labor utilization, retention rates, real-time feedback, and even organizational network analysis, CEOs and CHROs now understand that people analytics is a vital part of running a high performing company.
Witness the following data from our High-Impact People Analytics study: this year 69% of companies are integrating data to build a People Analytics database . In prior years this was always about 10-15% of the organizations we surveyed – this huge change in investment is a sign that this discipline has grown up.
I recently completed a trip to Asia and spent a few hours with the People Analytics team at a large financial institution. I was exhilarated to find myself surrounded by 15 senior HR and technical professionals, an HR VP and senior Director, and a set of business partners dedicated to helping business unit leaders take advantage of the people-related data in their organizations. This kind of effort is now happening in large companies everywhere.
2. The problems of data quality, integration, and integrity are being addressed.
In all the research we’ve done on this topic (including the study we just published), the problem of data quality, consistent definitions, and data integrity (ie. We don’t have multiple copies of the same data in different forms) have been major obstacles. In the 2017 Deloitte Human Capital Trends we found that 39% of business people believe their company has “very good” or “good” quality data for people-related decision-making and 31% understand what “best-in-class” people analytics looks like. This is an astoundingly high number and I believe the 2018 data will show even more progress.
(Our research also found that level 4 companies are twice as likely to have a data council responsible for data governance – a critical success factor in keeping people analytics data reliable and useful over time.)
In our new High-Impact People Analytics maturity model we found that 90% of the companies at level 4 believe they have accurate people-related data, 95% believe they have strong practices for data privacy and security, and 75% believe they have consistent data definitions. While this is still a small number of companies, you can see that “world-class” is now easy to define.
There are two reasons this has happened. First, the need for high-quality data is urgent, as CEOs and CHROs are being asked to report on pay equity, diversity, and skills gaps by the board. Second, there’s a new generation of integrated cloud HCM systems (approximately 40% of companies now have a cloud-based HCM system) that require a company to implement a more consistent system of record.
I am not saying this effort is easy. According to the latest Sierra-Cedar survey on HR systems, the average company now has more than 7 “systems of record” for people related data. (Payroll, learning, recruiting, performance, engagement, wellbeing, and others.) But what has gotten easier is integrating this data – a wide set of new tools is now available to help integrate data in an easier way than ever before, and most big companies now have Hadoop clusters and data lakes they can set up to bring all this data together.
3. Companies are greatly expanding the type, nature, and level of data to analyze.
We now live in a world where employee-related data is everywhere, and it is expanding day by day. Most companies have lots of data about pay, performance, learning, job candidates, recruitment, talent mobility, and organizational compliance.
But they now also have near real-time data about employee engagement (coming from pulse surveys or continuous performance management tools), employee recognition (from social recognition systems), employee communications and teams (through organizational network analysis and email metadata analysis systems), travel and location (through time and expense, employee badge readers, or phone location data), employee wellbeing (through wellbeing applications and voluntary data shared about exercise and fitness), and even trust and employee sentiment (through “mood analysis” of survey responses and emails).
Our research shows that advanced companies now use 7 different “methods” for capturing data, including looking at internal and external social media, ERP systems, surveys, and analyzing information in business communication tools. Many of the new email systems being offered now enable “organizational network analysis” (ONA) to look at email metadata, so this data is easier and easier to collect.
I know it sounds a little creepy, but several vendors now sell software that reads email and identifies the “mood” or “changes in mood” in team or organizational communication. One of them showed me data that can spot “stress” in the organization and has proven that its algorithms can pinpoint areas of potential fraud or client projects that are going poorly. We now have access to many tools that measure stress in our voice: I would not be surprised to see systems in the workforce in 2018 that listen to meetings and identify areas of stress. (Note that Amazon just announced Alexa for Business – one could easily build a “skill” that listens to meetings and applies off the shelf AI algorithms to analyze the conversations.)
One of our clients told me about a project they did to analyze the performance of their engineering teams. They asked a set of engineers to wear smart badges and join a project to understand “what makes engineers happy and productive at work.” After several months of analysis they found that the “happiest” engineers were the ones who moved around the most – they had more physical activity, more relationships, and spent more time meeting with others. This was important data used to reorganize the facilities, change the way meetings were handled, and improve management practices to encourage engineers to spend more time with their peers. Almost every company now has the ability to do this type of analysis.
In our newest research we highlight how JetBlue uses many sources of data to understand attrition patterns, drivers of engagement, and causes of flight delays and low productivity. They integrate feedback data, crew and customer complaints, HRIS data, training data, and data about employee flight activity into an integrated system that gives the company a total picture of employee satisfaction, engagement, and customer service. Intuit is doing the same.