By Mike Bollinger
Analytics are not about having the best tools, they are not about volumes of paper. Rather they are about useful and timely information delivered just as the business needs it.
Every day organizations struggle to answer essential questions about their workforce. How much money are we losing by not having the right talent in place and how is that impacting current projects? What skills will we need in the next 5 years that we don't have today? How will business be impacted by impending retirements and are we prepared? Fragmented systems and bolt-on analytics are only some of the barriers that HR faces today. The consequences include missed opportunities, lost productivity, attrition, and uncontrolled operational costs.
A recent study by the Economist Intelligence Unit found that the top 2 most impactful types of analytics which are the most helpful to the C-Level suite in making data driven decisions was predictive and trend based answers.
Predictive Analysis Imagine if you could look ahead and be prepared for upcoming workforce trends. Most organizations do not have the analytic capability to do predictive human capital analysis, yet the worker information needed to make educated forecasts already exists today. Aging populations, shifting demographics, rising and falling economies, and multi-generational issues can have a significant impact on workforce decisions ? for employees, managers and HR professionals. Not being able to accurately predict how all the moving parts fit together, and where you really have potential problems, can make or break an organization.
As good HR practitioners, we can make a difference by bringing our knowledge of our employee populations and business to bear. We don't have to be the ?analytics tool? people. Rather we need to ask good questions. Start with the classic scientific method.
How do you go about developing the questions that you want analytics to answer? What bears scrutiny and what doesn't?
If we recognize that personal and cultural beliefs influence both our perceptions and our interpretations of what is going on in our business, we aim through the use of standard procedures and criteria to minimize those influences when developing a theory. As a great physicist, Richard P. Feynman once said, "Smart people (like smart lawyers) can come up with very good explanations for mistaken points of view." In summary, the scientific method attempts to minimize the influence of bias or prejudice in the experimenter when testing a hypothesis or a theory.
We need not be nearly as diligent in our case, but the approach is still useful. We start with a question, and I strongly suggest that it be a simple yet impactful question. Analytics are most successful when applied to an immediate and pressing business problem whose solution is critical to competitive success , one that aligns to the business initiatives.
Let's start with an example, we have a business which is in the middle of an acquisition. There are varied approaches within the new and existing cultures regarding best practice when it comes to assessing and hiring people for a multitude of critical engineering job roles which we will need to realize the benefits that our CEO has committed to the board. Some of the team believe that assessments are the most useful, some of the team believe that group interviews are the best approach but what about other influences such as source, or onboarding, or manager technique? We also need to examine success criteria, what defines a good outcome? Results? Performance Rating? Retention?
Using a simple chart method, often promoted by the great Dr. Jac Fitz-Enz we can use the following table to outline our data to develop our theory.
If we take some sample data, we can lay this out and develop a hypothesis. In this case, using the success metrics to the right (results and retention) ? I suspect that using referrals in conjunction with onboarding may be the best approach. I now have an idea of what I want to examine in the next meeting with the analytics team.
The idea is to create further texture and insight. Workforce analytics is not just about:
As HR practitioners, we are often fooled into believing that great technology will solve our issues. We try and buy technology and immediately start looking for a way to use it. When it comes to analytics we need to be attuned to the interventions that will promote company success. After identifying a problem or more importantly, an IDEA. Use your team to adopt the processes and tools necessary to implement the right solution, tools do not make the result. That responsibility lies with us.