A particular department has the responsibility of both onboarding new customers and cancellation of customer policy upon the customer's request. Their KPIs are based on the number of new customers they have onboarded and no KPI is attached to those of cancellation. This is reasonable, every organization wants to grow. In a way, no one should be measured by the number of customers they cancel from the business. Because of this, the department has historically prioritized onboarding activities over the cancellation.
The effect of not cancelling customers as and when required is however costing the business more than necessary. One, some customers noticed this and still use the services since they are not stopped. On the other hand as well, at every point in time, the report of active customers within the organization is inherently wrong. Cascading down all of the organization, everyone continues to make wrong estimates, budgets and expectations.
"Show me the incentive and I'll show you the outcome."
- Charlie Munger
Charlie Munger once famously said, “show me the incentive and I'll show you the outcome”. Many organizations are plagued with data quality issues because they have set the wrong incentives for their people. At the end of the day, humans (you and I and the leadership) are responsible for every data quality issue that our organization is facing. We have either failed to enter the correct details when getting data from a source or even failed to enter the information at all. And why will we do that? Lack of incentive alignment sure does rank high among the possible reasons for that. When incentives aren’t set to achieve and ensure data quality, it can almost be guaranteed that the organization will not have quality data no matter how much effort and resources they throw at it. Humans respond to incentives and our data operating models must be designed to reward behaviors and priorities that guarantee data quality.
Often, incentives are not aligned because the subject of data quality is an afterthought for leadership. Imagine the scenario painted above, if data quality was sitting at the center of every decision by the organization’s leadership, such a scenario should never exist at all. For organizations to achieve high data quality, incentives must be set right in the following area:
1. Key Performance Indicators (KPIs)
It might be obvious from the scenario used so far that whenever KPIs are being set, it should be done with the consciousness of directing actions and inactions of the employee to strive for data quality. Yes, no company wants to lose customers and as such will not reward employees for facilitating the cancellation of customer policy/products. But if viewed from that lens alone it is one-sided and costly. Does non-cancellation also cost the business money, does it open room to wrong reporting and does wrong reporting invite regulatory sanctions? Think about this when setting KPIs within your organization.
2. Recognition
Individuals and teams (data owners) in any organization should be recognized for fostering the culture of data quality and for producing quality data from their processes. And it should be done in a way that will motivate other individuals/teams to want to do the same.
3. Work quality
Often, when work quality is judged, it is judged on “done and working now”. But if organizations start to judge work based on the usual but also the quality of data that comes out of the work, then people will start to act accordingly. Every new process in an organization creates new data, the quality of the process shouldn’t be judged excellent if the data generated from the process is not designed to be of good quality.
Conclusion
As your organization strives to ensure data is of high quality and does put all options on the table to consider, considering how incentives align is also critical. Often, it is the lack of aligned incentive that is responsible for bad data quality and fixing that will potentially increase the quality of your organization’s data.
