They said it can’t be done….
They said it won’t be relevant….
They said you can’t reduce a workforce comprised of individuals with their own unique perspectives, work habits, management styles, impact into a few financial ratios explaining the impact of people on profitability…
Welcome to the 21st century …the time of big data and data analytics. It is a time characterized by experts espousing the benefits of data analytics – even those focused on human beings. Some of these thought leaders include:
- Deloitte: “The use of workforce data to analyze, predict, and help improve performance has exploded over the last few years.”
- McKinsey: “Advanced analytics can dramatically improve the way organizations identify, attract, develop, and retain talent.”
- USC Marshall Business School: “Human Resource leaders are urged to apply big data and predictive analytics to talent, leadership, and organizational capabilities.”
The question is how can organizations create HR analytics and how can they use them effectively in decision-making? The first thing to understand is that there are essentially 4 levels of analytics according to Gartner Group—each with its own level of complexity and value
Each level of analytics (in progressive sophistication) can provide useful information to decision-makers by providing collective (aggregate) information about the behavior of the workforce. As an example, let’s take descriptive analytics as applied to Human Resources. Descriptive analytics will let you know certain fundamental information about your workforce:
- Average age and distribution of age by generation;
- Diversity of the workforce and distribution of diversity by generation or by division/function;
- Education and experience of your workforce by division/function.
These are some basics so you understand your “audience” – and can better contextualize decisions about HR programs. For example, we know that Millennials value time off and intangibles as a reward. If your organization has more Millennials than any other generation, then you probably won’t spent time designing a new bonus plan that delivers additional pay…. this won’t be valued by the majority of your employees and it is a waste of time and resources as far as engaging and motivating them.
How would you know this if you didn’t know the composition of your employees? I recently worked with a company where the new CEO believed that the majority of the employees (200+) were Millennials based on his “listening tour” of all locations. He asked that I create programs that would be attractive to the millennial generation. Once an age analysis was done, he learned that 75% of the employees were older Gen X’ers and Baby Boomers. This was an “eye-opener” for him! A simple analysis of date of birth added a lot to his understanding of his workforce. We designed programs to would be meaningful to that employee population.
Age distribution is a simple example of descriptive analytics – easy to do and very informative!
Please join me for the next post in this series which will deal with Diagnostic Analytics as we continue along the path to derive a relationship between people data and bottom-line returns. Now that we know what happened, can we use data to ascertain why it happened?
Selected reference Links:
Deloitte: Future of Work https://www2.deloitte.com/insights/us/en/focus/human-capital-trends/2018/people-data-analytics-risks-opportunities.html?id=us:ps:3gl:confidence:eng:cons:102228:nonem:na:Hlombp0t:1123237770:303816297793:b:Future_of_Work:People_Data_BMM:nb
McKinsey: People Analytics https://www.mckinsey.com/solutions/orgsolutions/overview/people-analytics
Solange Charas is a senior-level human resources expert with 30+ years of experience as a consultant, practice leader, top corporate executive, and board director across all industry sectors. She was the Chief Human Resources officer at Havas Worldwide, Benfield and Praetorian Financial Services Group and held senior-level positions at Ernst & Young and Arthur Andersen. She serves of the boards of 2 public companies, a non-profit organization and a higher-education institution. She is the Founder and CEO of HCMoneyball – a SaaS company founded to provide support for enhanced decision making about spend on people in any organization.
Solange earned a PhD in Management from Case Western Reserve University’s Weatherhead School of Management, an MBA in Accounting and Finance from Cornell University’s Johnson Graduate School of Management, and a BA in International Economics from the University of California, Berkeley. She has authored numerous articles, including “The Art and Science of Valuing People” in HR Director, “6 Ways to Coach Your Company’s Teams to Be Champions” in Entrepreneur Magazine and “Why Men Have More Help Getting to the C-Suite” in Harvard Business Review..