D&A leaders must take pragmatic and targeted actions to improve their enterprise data quality if they want to accelerate their organizations’ digital transformation.
Every year, poor data quality costs organizations an average $12.9 million. Apart from the immediate impact on revenue, over the long term, poor quality data increases the complexity of data ecosystems and leads to poor decision making.
The emphasis on data quality (DQ) in enterprise systems has increased as organizations increasingly use data analytics to help drive business decisions. Gartner predicts that by 2022, 70% of organizations will rigorously track data quality levels via metrics, improving it by 60% to significantly reduce operational risks and costs.
“Data quality is directly linked to the quality of decision making,” says Melody Chien, Senior Director Analyst, Gartner. “Good quality data provides better leads, better understanding of customers and better customer relationships. Data quality is a competitive advantage that D&A leaders need to improve upon continuously.”
Identify a clear linkage between business processes, key performance indicators (KPIs) and data assets. Make a list of the existing data quality issues the organization is facing and how they are impacting revenue and other business KPIs. After establishing a clear connection between data as an asset and the improvement requirements, data and analytics leaders can begin building a targeted data quality improvement program that clearly defines the scope, the list of stakeholders and a high-level investment plan.
To improve data quality, first it is important to understand what is “best fit” for the organization. This responsibility of describing what can be defined as “good” lies with the business. Data and analytics (D&A) leaders need to have periodic discussions with business stakeholders to capture their expectations. Different lines of business using the same data, for example, customer master data, may have different standards and therefore different expectations for the data quality improvement program.
D&A leaders need to establish data quality standards that can be followed across all business units in the organization. It is likely that different stakeholders in an enterprise will have different levels of business sensitivity, culture and maturity, so the manner and speed with which requirements of DQ enablements are met may differ.
“This will enable stakeholders across the enterprise to understand and execute their business operations in accordance with the defined and agreed-to DQ standard,” says Chien. An enterprise wide DQ standard will help educate all involved parties and make the adoption seamless.
Data quality profiling is the process of examining data from an existing source and summarizing information about the data. It helps identify corrective actions to be taken and provides valuable insights that can be presented to the business to drive ideation on improvement plans. Data profiling can be helpful in identifying which data quality issues must be fixed at the source, and which can be fixed later.
It is, however, not a one-time activity. Data profiling should be done as frequently as possible, depending on availability of resources, data errors, etc. For example, profiling could reveal that some critical customer contact information is missing. This missing information may have directly contributed to a high volume of customer complaints and would make good customer service difficult. DQ improvement in this context now becomes a high-priority activity.