Data Quality: Handling denial

Every organization is now fully aware of potential of their data, and how critical is to have the right data to derive useful insights to feed business decision processes. Bottom line any strategic, tactical, and operational decision must be made with accurate data.

Data without enough or of unknown quality is not of no use and will lead to undesired or unexpected results.

Data quality has always been a challenge to all organizations, but it has never been so challenging as it is now.

To successful any data quality program must be focused on leveraging the business strategy, it must be intimately connected with the business objectives and challenges.

This means it can't be handled as a technical problem, it must be addressed as a business problem, and when this happen it becomes intrusive and disruptive, creating the natural resistance to change within the organization.

When we stop addressing data quality as a mere technical issue, easily solved by a set of processes, and start addressing the business processes underlying the data problem it is usual to find some resistance from the business stakeholders, this kind of resistance as some parallels with what psychologists call denial.

denial [dĕ-ni´al]

a defence mechanism in which the existence of unpleasant internal or external realities is denied and kept out of conscious awareness. By keeping the stressors out of consciousness, they are prevented from causing anxiety.

Of course, some level of denial can be healthy and reveal some signs of vitality in an organization. It allows to give a somewhat more critical look at things that are new and do not have clear impacts, or it can help focus on positive objectives setting aside potential threats. However, it can easily turn into a focus of resistance to change.

Psychologists identify some basic types of denial from which parallels can be drawn:

• Denial of fact – Avoiding facts that can be potentially harmful by denying or omitting them.

• Denial of responsibility – Avoiding personal responsibility, usually shifting attention away from themselves, this can be done by blaming others, minimizing problems, or justifying the situation with a given context.

• Denial of impact – Avoiding thinking about or understand the consequences of the problem being handled.

• Denial of cycle – Avoiding considering that a certain chain of events/decision lead to a problem or negative impact.

• Denial of denial – Under the cover of positive thoughts, actions or behaviours which strengthen belief that the problem does not exist or that no negative impacts can be related to it.

When we look back at our past experiences, most of us can identify one or more of these behaviours on several occasions.

What can be done to escape these situations and mitigate its effects?

Stated as they are, all of these fall under the scope of Change Management and the set of tools it uses, however, there are a few things that data quality teams can do to in order not to fall into denial problems during a project.

Most of them, such as strong sponsorship, management commitment, strategic alignment or staff training, are almost common sense, but the one point that I think can determine the success of any initiative and that is frequently overlooked:

To make data quality a business issue, make it part of the business process.