Insights & Ideas

Data Quality Management is a Journey Not a Destination

Many organizations approach data quality as a campaign. There are fixed start points and end points that neatly align to some other business or IT initiative.

Unfortunately these initiatives are not delivering long-term value for your organization and any “campaign-thinking” needs to be rooted out and transformed into longer-term, more strategic goals.

Take customer data for example, it’s not uncommon for organizations to pass their data over to bureaus to be cleansed and improved prior to a large marketing campaign.

Whilst outsourcing can clearly be beneficial you have to ask the broader question - why is our data getting into such a state that we need to outsource its improvement?

Why is a data quality campaign so harmful when it provides immediate value? There are several reasons:

  • Waste of Resources: Data quality campaigns that have a fixed period typically require workers to gain new skills or take time out of their schedules. This investment is lost when the project terminates. Ideally, the organization should be creating pockets of expertise that persist instead of delivering ad-hoc education on a sporadic or cyclical basis.
  • Lack of Coordination: Campaigns tend to be self-serving and less focused on the goals of other data related initiatives.
  • Duplicated Effort: Closely related to the waste of resources issue is a situation that often occurs where two different teams are effectively mirroring each other’s data quality activity. For example, you often find multiple data cleansing and matching tools in organizations which are the by-product of a lack of strategic data quality thinking.
  • Management Confusion: A lot of senior managers prefer data quality campaigns because they can see a light at the end of the tunnel: “If we just clean up this data then I won’t need further hits on my budget for next year.” The problem is that campaigns are viewed as a cost center, but in reality, data quality management initiatives are self-funding and generate considerable profits - but they have to continue to enable real value to be delivered.

Data quality campaigns are sadly all too common. They soak up resources, hamper operational efficiency and lead to long-term misconceptions about data and data quality. By actively transforming the campaign approach to data quality and adopting longer-term programs that have no fixed end point, it may take longer to find a sponsor but the net benefits will be far more considerable.

 

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