Effectively Mining Workforce Data | DCR Workforce Blog

Effectively Mining Workforce Data

If you feel intimidated by the barrage of emails, blogs, ads and posts encouraging you to “harness big data to generate true workforce intelligence”, then bypass all of the buzzwords and break this down into common sense. This really boils down to three straightforward questions:

  1. What do you need to know about your workforce (permanent employees and temps) to set business strategy and future plans?
  2. Does data mining provide you with effective information which helps you to improve your processes and resolve any issues and mitigate any risks?
  3. Where is that data captured today, and how do you get it in a form that is meaningful?

All of us have access to lots of data every day – in fact, we often get more than we need or want!  Do you find that you just collect the reports, print them or save them into a special folder (thanks to our green initiative which does not allow the printing of any data, wasteful or not!) for reference – and probably never look at them again after the initial glance through? It may be because the existing reports don’t provide you with the insights you need.

Begin with a laser focus.  Start by having each of the key stakeholders identify the questions they really want answered. You can always expand later. Once you gain a clear understanding of everyone’s critical needs you can identify the information that serves as lead or lag indicators or progress in each of these areas and map indicator to a quantifiable metric.  You can also determine if that information is already captured in existing systems, and – if so – where.  Make sure that the metrics speak to the business issue they are expected to address.

Historically, companies using large populations of contractors sourced through staffing agencies would focus on whether suppliers were quickly delivering the right talent at the best price.  Associated metrics include time-to-fill, number of submitted resumes against a requirement, adherence to a rate card, and similar metrics. More recently, companies have turned their attention to project work performed by small companies (typically less than 5 employees).  The objective shifts to attainment of project deliverables on time and on budget, and importance is placed on metrics such as adherence to schedules and budget burn rates.

Companies have also expanded their scope of interest from operational efficiency to also include risk mitigation and total cost of ownership or ROI.  Again, this focus has expanded the areas of focus, data to be collected, and metrics used.  Companies examine compliance with screening, onboarding and offboarding requirements and reductions in cycle times.  As companies increasingly see the use of temporary workers as a strategic imperative, they also are seeking the information needed to proactively anticipate workforce levels, determine the optimal mix of temp and perm workers, establish the most effective approaches to managing the workforce ecosystem, and evaluate the most effective talent sources.  These questions can only be answered by going beyond the data generated in-house from your operations to overlaying your information with industry data.  Some ways in which workforce data can be used in conjunction with information available in the public domain:

  • Examine your anticipated attrition rate, factoring in the average age of your workers and anticipated retirement rates. Consider the time required to bring a new worker up to proficiency.  Then compare your forecasts for workers by skill set and location to information about students receiving college degrees with ‘in demand’ credentials.  Use this data to make realistic plans to establish talent pipelines.
  • Compare diversity or veteran hiring plans by type of job against the availability of qualified candidates in the targeted groups.
  • Track productivity against factors such as contractor vs. perm employee, flexible vs. traditional schedules, or work location.
  • Relate attrition information to tenure, age or educational qualifications to gain deeper insight into the trends in workforce mobility and the actual loss of access to skills and experience.

Consider where data is captured and stored, who can access it, and how.  If you are using a vendor management system (VMS) for your contingent workforce, are you able to bring together data from the VMS and back-office applications to get an enterprise-wide view of your workforce?  Can you access the needed information immediately, without having to wait for the IT or Finance organization to respond to your request?  Can you get the information in the form that you want, viewed online or as a printed report?  Can you manipulate the presentation of the information, and then send it on to others for viewing or action?

Successful data mining is achieved through clear focus on objectives, effective processes for capturing, accessing, reviewing and acting on needed information, and robust technology that presents the data when and how needed.  We do not intend to over-simplify the effort required to mine and use workforce information.  In fact, depending on your specific requirements, this may be a significant enterprise initiative.  However, to ensure that you do not get lost along the way and ultimately disappointed in the results, we encourage you to follow these steps, rejecting the urge to expand the initial focus of your data mining program.  Let us know about your successes and set-backs when attempting to establish expand your workforce intelligence system.

The content on this blog is for informational purposes only and cannot be construed as specific legal advice or as a substitute for competent legal advice. They reflect the opinions of DCR Workforce and may not reflect the opinions of any individual attorney. Do contact an attorney for advice specific to your issue or problem.
Lalita is a people/project manager with extensive experience in operations, HCM and training and development across industries like banking, education, business consulting, BPO and information technology. She believes in a dynamic approach to life and learning as change is the only constant.