Getting Started with Workforce Analytics
In part one of Enterprise Apps Today’s two-part interview with Dr. Carl C. Hoffmann, CEO of Human Capital Management and Performance and one of three authors of “Calculating Success: How the New Workplace Analytics Will Revitalize Your Organization” (Harvard Business Press, 2012), Hoffmann shared some common misconceptions about workforce analytics and spoke about challenges associated with starting a workforce analytics initiative.
In part two, he discusses using workforce analytics to promote knowledge sharing and also share his six key steps for introducing workforce analytics to an organization.
You can read an excerpt from Calculating Success on sister site IT Business Edge.
EAT: Your book includes examples of how workforce analytics can be used to improve recruitment and retention but also how it can be used to boost innovation by promoting collaboration and knowledge sharing. Can you share some insights as to how that can work?
Hoffmann: In many respects the book is all about innovation. One of the exciting things that we have noticed is that some teams seem to be much more creative and effective in working together to accomplish their goals. We have found that measuring the network of communications, the content and amount of information shared, who produces it and who receives it, and how it is used to accomplish the work determines the success of a project. This is true regardless of whether the project is a research and development project, a systems implementation project or a sales effort.
When you can identify teams that are accomplishing stellar results, the analytical question becomes how are they achieving this and can it be replicated in other places?
There are four important components to capturing this innovation:
Understanding the talent: The people that are part of the team clearly have a great deal to do with the team’s success. They must have the knowledge to share, be open to receiving and sharing knowledge and work to synthesize those inputs with others. Workforce analytics can be used to identify those knowledgeable, open, creative and self-confident people.
Understanding the organization: Analytics can also define the organizational conditions that encourage knowledge sharing. How is the team organized? Have they developed team roles or tools that are different from teams with “standard” performance? Too often organizational budgets, boundaries, and expectations or physical constraints prevent knowledgeable, open and creative people from finding each other, let alone working effectively together.
Understanding the management of creative teams: Analytics can be used to understand how leaders create and support an atmosphere that encourages and rewards sharing of knowledge and insights, while simultaneously focusing on the deliverables, deadlines and work that must be done.
Understanding the context of the innovation: Finally, analytics can establish whether what appears to be an innovative process was the lucky result of a unique set of local circumstances that can’t be reproduced. Once we control for serendipitous effects, we can determine whether the documented differences are generalizable to other teams, business units, geographies, and financial and market conditions.
EAT: What are important first steps for companies interested in workforce analytics?
Hoffmann: Your best tactic is to get a foothold by using analytics to solve a meaningful business problem — a solution that provides clear and substantial benefits. The problem should be attacked using the six-step method we describe in the book, which is briefly summarized here:
- Take time to fully understand the problem confronting the company.
- Build an analytical model that describes the potential causes of the problem and associated potential solutions, as well as the constraints on the company’s actions.
- Use that model as a guide to collect the meaningful data that is available. This often means capturing and integrating data from a variety of human resource, operational, sales and finance systems. The data need not be perfect to get the process started, just good enough to be dependable in pointing the way.
- Test the causal relationships and potential solutions using appropriate statistical and mathematical techniques.
- Present those findings to stakeholders and those who will be responsible for implementing any solution. This may be the most important step as their feedback will be invaluable. It will test the practicality of the solution and often contribute new and important facts that need to be taken into consideration.
- Develop a roadmap for implementing the solution with specific steps, clear timelines, estimated costs and benefits to be monitored, risks and their mitigation, all clearly spelled out.
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