5 Actions to improve your Data Quality


Written by: Melody Chein, Senior Director Analyst, Gartner

Improving data quality can save costs in the long run 

Data quality is directly linked to the quality of decision making. 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.

Gartner research suggests that poor data quality can costs organizations an average $12.9 million every year. 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.

Using this 12 actions, data and analytics leaders can pragmatically improve their data quality and thereby improve decision making efficiency. 

No. 1: Establish how improved data quality impacts business decisions

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.

No. 2: Establish a DQ benchmarks

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. An enterprise wide DQ standard will help educate all involved parties and make the adoption seamless.

No. 3: Use data profiling early and often

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.

No. 4: Move from a truth-based semantic model to a trust-based semantic model

The source of data is not always internal, where data quality can be controlled and maintained right from the beginning. In some cases, data assets are acquired from external sources where the DQ rules, authorship and levels of governance are often unknown. Hence, a “trust model” works better than a “truth model.”

This means that, rather than thinking about key enterprise data as being absolute, organizations must also consider its origin, jurisdiction and governance — and therefore the degree to which it can be used in decision making. D&A leaders can implement mitigation measures when trust levels are not maintained.

No. 5: Establish DQ responsibilities and operating procedures as part of the data steward role

A data steward is responsible for ensuring the quality and fitness for purpose of the organization’s data assets, including the metadata for those data assets. In more mature organizations, a data steward’s role is also to champion good data management practices, and monitor, control or escalate DQ issues as and when they occur.

D&A leaders need to include this role in their D&A strategy, so that DQ is measured and maintained regularly in a systematic manner. Create a governance scope and stakeholder map that will allow a clear understanding of how DQ issues are managed.


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