Managing With Blinders On

We can all agree the government keeps a lot of records. In fact, just the HR records are enough to fill a lot of warehouses – both literal and electronic. The basic HR record is the Official Personnel Folder. It includes a record of every personnel action for an employee, along designations of beneficiaries, appointment affidavits, job applications, college transcripts, training records, awards, and more. That is just the start of the government’s HR record keeping. The Office of Personnel Management and agencies that run their own hiring process keep millions of job applications. Add to that the job descriptions, injury compensation records, disciplinary actions, performance plans and ratings, grievances, training records in learning management systems, and countless other HR records, and there is a wealth of information that could be used to make HR programs far more effective.

So – what do we do with all of the information in those systems of records? For the most part, nothing. We spend hundreds of millions of dollars on the processes that produce the records, but little to nothing on analyzing and using the data for constructive purposes. Let’s look at a couple of examples.

The government uses a hiring process that collects more information on applicants than the typical private sector employer. Every HR office uses one of a handful of systems to manage those applications, evaluate them, issue referral certificates to hiring managers, and record selections. The applicant data could be a rich source of information that could be used to improve the hiring process, but it typically goes to waste. Agencies receive the applications, rate them, do interviews, make selections, on-board the new employee, then let the data sit in the system, unused, until it is time for it to be purged.

Agencies also maintain performance information on all of their employees. Performance management processes are supposed to identify who is doing well and who needs improvement (how well they do that is another matter). Given that we have a hiring process that is supposed to identify the best candidates for a position and a performance process that rates how well they perform, why are agencies not using the information to see if the hiring processes are actually working? It would not be that difficult to develop analytics that look at key data from the hiring process and compare that to performance once the employee is on the job. When an agency uses a given assessment, does the predicted performance actually occur? Are there questions that are more likely to predict performance? Do applicants from one hiring authority (e.g., merit promotion) compare to those from another (e.g., VEOA)?

Every agency has records that tell them who is leaving, but few agencies analyze the data to predict turnover, let alone comparing that data with information from the hiring process. Do new hires from particular sources turn over faster than normal? Or at a lower rate? Is it a problem? If so, what is the underlying cause and what can be done to fix it? At the Defense Logistics Agency, we found a much higher turnover rate among hispanic new hires in our corporate intern program. A recent report from OPM says the same thing about Veteran hires in some agencies. Analysis of the hiring data, employee surveys such as the Federal Employee Viewpoint Survey, and performance data can help explain what causes the problem and how it might be fixed. That analysis is not happening in many agencies.

The information locked in systems that are viewed primarily as “records” rather than data could answer a lot of questions and help agencies manage better. It could improve the hiring process, identify contributors to morale issues, improve retention, and and generally help agencies manage based on data rather than seat-of-the pants feelings. Our failure to use information that is available to us means we are often managing with blinders on. The information is there, the world around us is visible, but we choose not to look at it. If we truly want to improve all of these processes, we have to start making use of the data that we already have and stop treating the data just as records.