In this article I want to reach out to members and readers to learn how they are engaging the organisation to get involved with data quality.

What techniques are you adopting to "pull" employees in your direction or are you relying on a "push" approach instead?

Two Approaches to Data Quality Engagement

With any improvement initiative there are essentially two approaches to getting the workforce and management engaged.

One of the most common approaches is the "push" approach. We introduce new data quality procedures and stipulate that they must be adhered to as a part of our employment contract. If we are obtaining data from a 3rd party perhaps we can introduce some punitive measures. If the data does not come up to our required service level then a penalty will be liable or the contract terminated.

The opposite is obviously the "pull" method. This works on a reward strategy instead. Employees may gain something, either tangible or intangible, as a result of adopting new data quality processes.

Case Study A: The Push Approach

In a former organisation I was responsible for the technical management of a large data warehouse. Within the small development team we implemented some simple measures and made it a stipulation of employment that the measures be adopted. Training sessions were held and everyone was educated on what the process would be moving forward. Regular meetings were held to ensure that people were aware of their obligations.

A three month audit revealed that the standards had been ignored on many occasions. Several members of the team, whilst openly agreeing with the standards, had failed to understand the importance of the new processes and were degrading the data quality as a result. New contract staff were also found to be less than enthusiastic with the standards and even though they were compensated, began to deviate from the standards set down.

Cast Study B: The Pull Approach

In the very first organisation I worked our projects suffered from considerable data quality issues. It was simply taking far too long to create our information products. With a limited team of data analysts our only available resource were part-time data entry workers.

We came clean, explained the situation and invited the data entry team to suggest ideas and become a part of the solution. Numerous ideas began to flow and I promised a series of intensive training sessions for the data entry staff to give them a data quality education. The benefit for them would be marketable skills that they could use to advance a career in the organisation or indeed in the wider marketplace. The results were incredible and still shape my data quality approach to this day. Each individual worked tirelessly to educate themselves and improve the tough situation we had got our team into. Within six months the entire team were proficient in advanced data analysis and several went on to take senior data management and software development roles. Their efforts transformed the performance of the department and helped the business grow substantially.

The reason I quote these case studies is that it is all too common for management to fall back to the "push" approach. In my experience, this often has limited impact. As my "pull" case study shows, even with staff who are the lowest paid, part-time and relatively unskilled, incredible data quality gains can be made. Another important observation is that, in my experience, the pull approach tends to create longer-lasting gains.

How are you pulling employees in the direction of your data quality vision?

Are you forcing knowledge workers to adopt new standards and processes or are you demonstrating the value to them as individuals using a pull approach?

I welcome your views on how you are engaging and motivating the workforce (and management) to get behind your data quality initiatives

Please share your experiences in the comment section below.