Data quality requires integrated approach

Johannesburg, 26 Jun 2009

Data quality is a business problem, not just an IT issue, and it requires a business approach to solve it, says Bobby Thoolsi, founder of Didactic Services.

Speaking at a data integration and data quality executive breakfast organised by SAS this week, Thoolsi lamented the fact that most organisations believe data quality problems are caused by data producers only.

“They think these problems can be edited out by implementing business rules or implementing sophisticated new systems,” Thoolsi said. In reality, everyone in the organisation needs to be involved in the solution.

Thoolsi argued that data quality needs to be seen as a core strategic asset, and that it's more than just a matter of data cleansing or data assessment. To tackle the issue properly, “organisations need to adopt and nourish a culture where data quality is one of the organisation's top priorities,” he stressed.

According to Thoolsi, data quality is a multidimensional concept. Faulty systems, management and staff failures, and unhelpful processes all contribute to data quality problems, he said. For this reason, strategy, culture, people, processes and systems must all be aligned to produce high-quality data.

Any organisation needs a combination of strategy, processes, systems and culture in order to succeed, he added. The focus on processes that most organisations adopt, he claimed, is misplaced as long as organisational culture is misaligned.

Quality management threads

Quality management is crucial for data quality, according to Thoolsi, but this requires several related management “threads”: a customer-driven strategy for quality improvements, a commitment to quality by leadership, the involvement of all staff, and continuous process improvements.

He added that in a business context, two common practical applications are needed to ensure that data quality is achieved. These are quality assurance, defect prevention before the event, and quality control, defect detection after the event - often referred to as verification and validation.

However, these activities have to take place within a quality management system, a set of policies, processes and procedures in the core business areas of an organisation, according to Thoolsi. “This is the only way to ensure that the various activities necessary to design, develop and implement a product or service are effective and efficient and achieve the desired outcomes.”

According to Thoolsi, the challenge is to get the balance among different factors right. To achieve quality data management, organisations need an approach that harmonises hardware, software, policies, audits, cleansing, merging, acquiring, processing, monitoring and training, among others.

The complexity of the task comes from the fact that data management is an integral part of all business processes, said Thoolsi. This is also why the damaging effects of inaccurate and poor-quality data can be so devastating to an organisation. But by the same token, Thoolsi pointed out, high-quality data can be the key to organisational success.