On a recent webinar one of our members on Data Quality Pro asked how they could get their data entry staff to improve their results. They were considering introducing a punitive policy, for every mistake made, the staff would see their bonus reduced.
Whether it’s data entry staff, managers or general knowledge workers, entering data into the information chain is THE most critical activity.
Funny then why it’s often the most poorly rewarded isn’t it?
Call center workers for example are actually incentivized to get through calls as fast as possible, data quality comes second.
I’ve worked with companies where field engineers capture data on paper forms which are typed into the system back at base. No surprises where the inevitable data defects come from.
So how can you improve data quality? Perhaps call center workers should be punished for their customer order data entry finger flubs? Maybe engineers would “learn their lesson” if they were fined for each duct placement data error.
The answer is not punishment. It simply lowers morale and breeds a resentment for the task of entering information. People feel devalued and this is not conducive to learning and maturing skills.
How should companies approach this problem?
Back in 1992, in my first official data quality role, I faced this challenge and thought the answer was technology. I threw every trick in the book at the problem trying to automatically cleanse the errors made by data entry staff.
I found that many issues would not appear as obvious data issues. As time progressed, my code became ever more bloated, hundreds of lines of rules and exotic scripting designed to trap every error, indecipherable to anyone but myself.
Clearly technology can help validate and prevent data entry errors but the key is really empowerment. It’s cheaper, faster and gives longer-lasting results.
How can you empower data entry staff?
The key I found was education and stewardship. By giving each worker a training program with skills that they could add to their CV it really helped to empower them to learn more and take greater responsibility. They no longer had any excuses to fall back on, they had all the tools at their disposal.
Another simple approach was to demonstrate the cause-and-effect of where their data travelled in the organization. They could finally see that the information they created was responsible for delivering entire customer solutions that people absolutely depended on.
Stewardship came from giving specific users responsibility for different types of data. For example: Jean may look after census information, Bill would look after shopper statistics, and Jackie would get the transport network.
By creating this sense of ownership we helped to develop a mentoring culture where each steward trained new recruits in the finer art of entering quality data.
The results were extraordinary. Product lead times tumbled and profits soared. All because we got better at empowering low paid staff.
How could these concepts be deployed in your organization? Why not discuss in the comments below.