Assuring data quality is like nurturing a delicate fruit, it takes a lot of time, patience and hard work. Data quality, like a fruit tree, requires nourishment at the source. This is why data quality concerns in a data warehouse cannot be resolved easily. The majority of data scientists’ effort is spent cleaning up data sets that have been ignored at this critical stage. This is not only a waste of time, but it also causes another issue.

Ways to Improve Data Quality

Improving data quality necessitates a well-balanced approach that includes people, procedures, technology, and a significant amount of top-level management participation.

  • Establish how improved data quality impacts business decisions

Determine a clear connection between business processes, key performance indicators (KPIs), and data assets. It would be best to list the current data quality concerns that the company is dealing with and how they affect revenue and other business KPIs. Following establishing a clear link between data as an asset and the criteria for improvement, data and analytics executives may begin developing a focused data quality improvement program that specifies the scope, a list of stakeholders, and a high-level investment plan.

  • Define what is a “good enough” standard of data

To increase data quality, it is necessary to identify the “best fit” for the company. The burden for defining what “excellent” rests with the company. To understand their expectations, data and analytics (D&A) leaders must attend regular meetings with corporate stakeholders. Different lines of business that use the same data, such as customer master data, may have varied standards and, as a result, different expectations for the data quality improvement program.

  • Establish a DQ standard across the organization

D&A executives must set data quality standards that can be applied to various business divisions inside the enterprise. Because multiple stakeholders in a company are likely to have varying business sensitivity, culture, and maturity levels, the method and speed with which DQ enablement needs are satisfied may differ.

“This will enable stakeholders throughout the company to understand and execute their business processes in line with the specified and agreed-upon DQ standard,” Chien explains. An enterprise-wide DQ standard will assist educate all parties involved and make implementation easier.

  • Use data profiling early and often.

The practice of evaluating data from an existing source and summarizing information about the data is known as data quality profiling. It aids in identifying remedial measures to be performed and providing valuable insights that can be communicated to the business to stimulate creativity on improvement strategies. Data profiling can assist in determining which data quality concerns must be addressed at the source and which can be addressed later.

However, it is not a one-time event. Data profiling should be performed as frequently as feasible, depending on resource availability, data problems, and so on. Profiling, for example, may indicate that certain important client contact information is missing. This missing information might have led directly to a significant frequency of consumer complaints, making excellent customer service impossible. In this setting, DQ improvement has now become a high-priority effort.

  • Design and implement DQ dashboards for monitoring critical data assets, such as master data

A DQ dashboard offers all stakeholders a full view of data quality, including historical data, to discover trends and patterns that might aid in the design of future process enhancements. It may be used to compare the performance of data that is crucial to critical business operations across time. Based on reliable quality data, this helps the firm to make the proper business decisions to accomplish the intended business objectives.

DQ dashboards also show the effect of improvement actions like implementing new data practices into operational business operations. They may be tailored to match the individual demands, demonstrating how much faith you can have in your data.

  • Move from a truth-based semantic model to a trust-based semantic model.

Data sources are not usually internal, where data quality can be monitored and maintained from the start. In other circumstances, data assets are obtained from third-party sources where the DQ rules, authorship, and levels of control are unknown. As a result, a “trust model” outperforms a “truth model.”

This implies that, rather than seeing essential enterprise data as absolute, companies must assess its origin, jurisdiction, and governance – and hence the extent to which it may be used in decision making. When trust levels are not maintained, D&A executives might take mitigating measures.

  • Include DQ as an agenda item at D&A governance board meetings

D&A executives must relate DQ activities to business outcomes to assess DQ improvement investments against business objectives. “It is critical to articulate the impact of DQ improvement to the board in the language they understand best – business and revenue impact,” adds Chien. The board must have clear visibility of the DQ improvement progress and difficulties, and this information must be provided regularly.

  • Establish DQ responsibilities and operating procedures as part of the data steward role

A data steward is in charge of assuring the quality and fitness of the organization’s data assets, including their metadata. In more developed businesses, a data steward’s duty includes:

  • Advocating for excellent data management practices and monitoring.
  • Controlling.
  • Escalating DQ concerns as they arise.

D&A executives must include this function in their D&A plan to ensure that DQ is monitored and maintained regularly and systematically. Create a governance scope and stakeholder map to help everyone understand how DQ concerns are handled.

  • Create a DQ particular interest group across BUs and IT-led by the chief data officer team or an analogous entity.

A dedicated group with participation from BUs, IT, and the CDO’s office that collaborates for DQ development may be a wonderful time and resource investment. This type of teamwork allows for improved risk management inside organizations. It also expands chances for cost-cutting and growth by promoting shared and consistent best practices.

  •  Establish a DQ review as a “stage-gate” for release control.

Review and update progress to ensure that fixes and checks are made on schedule. As the organization’s maturity in dealing with DQ efforts develops, identify and disseminate the most effective techniques.

  •  Communicate the benefits of improved DQ to business divisions regularly.

D&A executives must assess the effect of the improvement programme and disseminate the outcomes regularly. A 10% increase in customer DQ, for example, may be connected to a 5% increase in customer responsiveness because consumers can be serviced better and faster by customer care executives owing to the availability of high-quality, trustworthy data.

“It is critical not just to have the board’s attention on DQ improvement, but also for it to be a long-term practice.” “The advantages must be presented to the board regularly,” explains Chien.

  •  Use external/industry peer groups such as user groups from suppliers, service providers, and other well-established forums.

D&A executives may link the company to DQ peer groups and promote organizational maturity in this area. This will allow them to discuss alternate viewpoints on best practices and insights into how others address comparable difficulties.

Inadequate data, whether it’s the incorrect data for the task or simply unavailable or erroneous, can jeopardize your entire data set and hinder you from making educated business decisions. Data quality is the foundation for all data-driven results, and the quality of your data determines the grade of the insights you may obtain.

Develop a successful and widely embraced data culture with a contemporary data analytics platform that can support infinite users and concurrency, continuously optimize, and help create insights and drive results to become future-ready genuinely.

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