A New Age of Human Resources
The measure of who we are is what we do with what we have. - Vince LombardiWithout knowing anything about you, your leadership style, or your organization’s performance metrics; I’m confident stating, “You are not getting enough business value from your Human Resources function.” As 70% of CEO’s desire their heads of HR to play a more strategic role than they currently are - the odds are (sadly) on my side! Yet, this is not trivia or a point of insignificant importance. This is an existential risk in our knowledge-driven economy with an increasingly mobile, empowered, and demanding creative workforce.
Be S.M.A.R.T.Leading enterprises are taking innovative steps to turn this threat into an opportunity. First, they are bringing clarity to their organizational goals for HR. Next, they are deploying advanced data methods to build competitive advantage based upon - people, talent, and culture (“a way of being”). The basic requirements for effective HR data analytics, are surprisingly similar to the well-known best practices for goal attainment in any domain. To be S.M.A.R.T. S – Specific M – Measurable A – Attainable R – Relevant T – Time-Bound Technology extends these characteristics to previously hard to quantify areas; enabling today’s revolution in HR data methods.
Break from the PastThough the nature of work has changed significantly throughout the last 100 years, manager evaluations have not kept pace. Formal performance assessments that began to appear in the 1960s tended to focus solely on individual output and goal attainment, and the paradigm has been remarkably hard to shake. Perhaps the biggest problem with only measuring outcomes is that it gives managers little diagnostic insight into their teams - especially when it comes to highly skilled knowledge workers. In order to shore up this shortcoming, over the last two decades of the 20th century more holistic assessment frameworks began to pop up. Managers began to measure processes as well as the outcomes. This gave them many more clues about what was behind employee performance. For instance, measuring and benchmarking activity across the steps of a sales funnel (lead qualification, sales calls, meetings, closes) could help hone in on replicable best practices or on causes underlying underperformance. In some cases, especially when compensation was directly tied to process metrics, issues with gaming of the metrics emerged. In the next phase of HR data analytics, companies have begun to apply statistical methods developed by data scientists to unlock invaluable intelligence hidden within. Following the paths blazed by online retailers seeking to deeply understand customer behavior, companies are finding rich insights into what their employees, and their broader organizations, are truly like. Google is a recognized leader in HR Data Analytics. As a result, Google is exemplary in having a culture that drives innovation and performance. What is less understood is how they achieve this. When Larry Page and Sergey Brin hired Laszlo Bock as head of HR in 2006, he spent the first year doing the same insufficient HR functions similar to many other companies – managing performance reviews, conducting employee surveys, and celebrating employee birthdays. Yet, one year later he made his first major hire in 2007, Prasad Setty, from Capital One to lead a people analytics department. He challenged Setty to apply the data-rich and empirically rigorous approach that Google was known for to HR and Talent Management. The rest is history. Fortunately, many of the custom solutions that Google built can be delivered to you out of the box via the cloud. Actually, more advanced features are available to at a fraction of the cost. According to Josh Bersin, head of the HR arm of Deloitte, in the past 10 months, the percentage of companies using predictive HR analytics doubled, from 4% to 8%iii. Startups are being funded at an increasing pace; $2bn in 2015. A variety of new tools and data sources have entered this domain. Employee feedback and engagement systems, real-time narrative analysis, and off-the-shelf predictive models from almost every talent management vendor are now available. Companies are entering a “golden age” of people analytics— and progress will accelerate.
Enter HR Data – Both Big and SmallAs technology makes data-driven HR decision making a possibility, companies are building people analytics teams, rapidly replacing legacy systems, and combining separate analytics groups within HR into one strategic function. In 2016, 51 percent of companies are now correlating business impact to HR programs, up from 38 percent in 2015. Forty-four percent are now using workforce data to predict business performance, up from 29 percent last year. iv Below illustrates the increasing levels of data sophistication.
Strategically Enhance Business ValueThree organizational enhancements of a data-driven HR are:
- Increasing the breadth and depth of data collection and analysis (i.e. – what is measurable)
- Closing feedback loops; certainly for decisions with delayed and uncertain outcomes
- Increasing collaboration, information share, and transparency to achieve a common language. Which is foundational to building
- organizational trust.
- Performance and incentives
- Recruiting and hiring decisions
- Talent, culture and employee engagement
We will arrive at a new Digitized HR organized around:Data-driven Culture, Feedback and Employee engagement
- Why do employees stay with us? Why do they leave?
- What are the key indicators of my organization’s overall health? [HBR: Competing on Talent Analytics]
- How to institutionalize our core values, principals and standards? Are they being passed on?
- When to staff up, cut-back, switch to a different sourcing channel (on-demand)
- Real-time adaptation of workforce management and needs to changing business environment
- Which Business Units need the most attention? What kind?
- What Activities have the greatest impact on my business?
Data-driven Culture, Feedback and Employee engagementEconomic research on innovation and growth shows that businesses that embrace interdisciplinary linkages create measurably more value. Employees are the new customers of your organization. Below are examples of companies leveraging data to drive insights:
- An electronics manufacturing company built a model that predicts the impact of attrition, wage increases, and profit on each other, to help each factory use site-specific data to set optimal pay rates and better manage thin margins.
- Several companies use embedded sensors in office furniture to better understand employee behavior patterns that in turn lead to optimized office design.
- Harrah’s used metrics to evaluate the effects of its health and wellness programs on employee engagement and the bottom line.
- Bridgewater Associates built a “Pain” button to spot negative patterns, track progress in dealing with conflict and potentially avoid similar experiences in the future.
Data-driven Recruiting and Talent Supply Chain ManagementGrowing the accessible talent pools both internally and externally are priority #1. As the categories of workers increases – full-time, contractor, on-demand, seasonal – so will the complexity of actively managing each component. This is a core area for data analytics. Below are examples of companies leveraging data to drive insights:
- Using performance data, sales data, and employee survey data, retailers determine which employees are most successful and why then develop pre-hire screening surveys that predict which applicants are most likely to succeed and produce higher sales.
- A client of Bersin by Deloitte used analytics to discover that their approach to finding sales and marketing candidates (by going to top-ranked schools, looking at GPAs, and assessing academic activities) had almost no relationship to success on the job (Bersin, 2015).
- Google used analytics to confirm their suspicions that low-performing employees were either misplaced or being poorly managed. They used data and predictive analytics to determine the most appropriate intervention to help them succeed.
Data-driven Organizational Development and Capability BuildingThis is critical to a strategic shift in responsibilities. To succeed here, a broader understanding of the commercial aims of the business are important. Also, aligning talent development activities with the longer-term strategy must take center stage. Below are examples of companies leveraging data to drive insights:
- California energy utility Pacific Gas & Electric Co., or PG&E, created a team with the mission to develop a workforce planning platform that would help the firm get ahead of trends that could disrupt the energy industry.
- Many companies are recommending real-time training curricula personalized to the specific performance reviews and individual goals
- Some HR organizations are figuring out how to analyze unstructured data from career-oriented social networking sites not only for recruiting purposes but to better understand career progressions so they can create more effective learning and development activities.
- A large Midwestern manufacturer identifies which skill sets need to be passed on, and how to transfer that knowledge through their talent pipeline.
Measurement against a standard makes you think through WHY the results were what they were. – Andy Grove
Dive into the Data StreamIn order to succeed on this new frontier, you must be willing to experiment, learn and embrace quantitative and technical capabilities that have historically been foreign. While these changes may be daunting, the new way of seeing your organization will remake your HR managers as true strategic partners to the C-suite.
Sources: http://www.ibm.com/solutions/files/V379497Q73917S31/ceo_how_hr_can_take_on_a_bigger_role_in_driving_growth.pdf https://hbr.org/2010/10/competing-on-talent-analytics http://digitalcommons.ilr.cornell.edu/cgi/viewcontent.cgi?article=1090&context=student http://www.kenan-flagler.unc.edu/~/media/Files/documents/executive-development/unc-white-paper-driving-talent- development-with-data.pdf