What defines process excellence? There are probably (at least) two parts of the equation. Efficiency is the most obvious, as people reengineer processes to make them faster and cheaper. The second part is equally important though, and achieving high quality must also be achieved or the efficiency is meaningless.
When we talk about high quality processes from an HR point of view, we are often talking about data quality. High data quality from a process perspective involves a couple of things. First, it’s about the total number of users and the control the organization has on the users. I’ve been in organizations where the number of people who can create new hire records, edit positions in position management, and even add jobs numbers in the hundreds (high hundreds too). When you have this many people in the system editing the core tables and making revisions to employee records, the loss of controls is significant and the resultant impact on data quality reverberates all the way through to end state analytics (which are now off by a multiple of the total users). The number of total users you have at the beginning of the process will be directly proportional to the error rate for the employee data, but also directly proportional to the disaster you have in the ore tables. Have you ever seen organizations with more job codes than they have employees? Believe it or not, I have, and it’s not uncommon. Whether it’s simply finding ways to limit the population or thinking about implementing shared services, you must find ways to limit the total user population.
The second is about pushing quality audits to the end user. If you wait until the records are saved, it’s too late. Once rows have been added to the job table, or the employee records are committed, you need change rights, not simply update right to correct the problems. Post entry audits are also critical, but they should only be used to spot systematic issues with the process or audit macro level data. Process users at the entry point should be given tools to audit their own entries, or be given audit tools that can run queries prior to commit.
The further forward you can push data quality to the beginning of the process, and the more you can control the initial steps, the much higher your end state quality will be. Saving yourself auditing on the back end spares you from the clean-up tasks and ultimately allows you to do the cools stuff around creating meaningful analytics (which now make sense because your quality is better).
One response to “Process Excellence – Push it forward”
Great post as usual! Having proper data is key to have proper analysis further down the process. Standard GIGO principle.
I beg 1) to differ on your first point and 2) to overemphasize your second point.
Limiting the number of users who can enter data will indeed increase the likelihood the data is entered “properly”.
That is in the same way as “deep” screening all passengers in an airport will increase security.
So, data will be entered properly but with side-effects:
– the process becomes slower: a smaller number of users have to do all the work to enter the data. They become natural bottlenecks.
– the data will be less acurate: it is a known fact that the information has to be entered where it is produced. If you decouple those 2 activities, you will have people who are not responsible for the information entering the data. Who knows better the skills that are needed for a particular job than the manager of this job?
Which leads to your second point that I fully embrace. ALL users should be provided tools that perform an audit of the data right away. We cannot expect all users to have the appropriate level of knowledge on how to properly enter the data, so the tools should support them.