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Data quality, the condition of an organization’s data in terms of consistency, accuracy, reliability, and completeness, is an easy enough concept to understand. The challenge is attaining and maintaining high standards.
Without the right strategies and best practices in place, it can be difficult to maintain and improve data quality. It is critical to get this right because there are multiple benefits, including improved decision-making, business planning, and operations. More importantly, poor quality can result in inaccurate analytics and operational inefficiencies that can prevent a business from reaching its maximum potential.
With data as the foundation for most enterprise IT systems, the quality of data becomes vital to the overall success of the IT ecosystem.
SEE: What Is Data Quality? (TechRepublic)
4 quick tips to improve data quality
Improving data quality is a never-ending process, and that is exactly how it should be treated.
Decide how to measure it
There are many ways the quality can be measured, and there are no set standards for the metrics that should be used. Ideally, an organization should measure data using metrics that are specific and meaningful to their business.
Some examples of metrics include the number of data test failures or the percentage of data test coverage.
Establish a process to investigate issues
When encountering issues or errors related to data, businesses must have an established data quality process to investigate the problem. This will help with understanding the issue and allow those in charge of handling the data to take steps to improve its quality. Every time an issue is resolved, steps should be taken to minimize the likelihood of this difficulty occurring again.
The process can include a data quality checklist to determine if there are any data incoherencies, gaps in the timeline, formatting errors, or missing attribute values. Repeating this process over time will help improve the quality.
Enlist data stewards
Data stewards are responsible for the implementation of data policies, rules, and procedures as set by the organization’s data governance framework. These stewards can be enlisted to work closely with data under their control and make it a priority to improve the quality. Stewards can be individuals from the IT department or any other business unit.
Prioritize a data culture
Not only do organizations need to invest in hiring and training both data stewards and data quality specialists, but they also need to promote a data-driven culture throughout the business. This culture must start from the top. Senior managers and leaders must lead by example, prioritizing data-driven business decisions and investing in data quality tools and roles.
A business should empower the data team by choosing to have a consensus culture over a hierarchical setup. Any boundaries between data specialists and business leaders should be porous, allowing for an easy flow of information and insights.
SEE: Data Governance Checklist (TechRepublic Premium)
Improving data quality with a data governance plan
While the tips above serve as a quick fix to improve an organization’s immediate data quality, to truly improve processes, data managers should devise and implement a data governance plan. This framework would outline the management, use, and protection of data. Typically, it includes policies, procedures, standards, and metrics. As a bare minimum, the plan must address the following areas:
- Quality standards: Establishes standards in terms of accuracy, reliability, consistency, and completeness.
- Roles and responsibilities: Defines the roles and responsibilities, including identifying the users, owners, and stewards.
- Policies and procedures: These are for collection, storage, processing, sharing, and compliance.
- Security and privacy: Implements security measures against unauthorized access and protection for compliance.
- Lifecycle management: From creation and acquisition to archiving and disposal.
- Monitoring and reporting: Sets up mechanisms for continuous monitoring, including regular audits and reporting.
- Training and awareness: Teach the staff about the importance of data governance and their role.
SEE: How to Measure Data Quality (TechRepublic)
Consider using data quality solutions
Using solutions to support data quality management is a great way to realize the full potential of data. These solutions offer benefits in terms of costs, automation, efficiency, and scale. Data managers can also use other types of technology, such as predictive analytics to proactively manage and improve data quality and role-based access controls to keep data healthy and secure.
For teams that want something straightforward and comprehensive, these four solutions offer a variety of user features:
- Ataccama ONE: An AI-powered enterprise platform best for overall data management and governance.
- Collibra Data Governance: An all-in-one solution maintaining data quality and security, featuring a business glossary, stewardship management, and intuitive workflows.
- IBM Data Governance: Integrates with existing systems to protect data confidentiality, integrity, and availability, and is distinguished by its governance processes, standardization, and machine learning algorithms for task automation.
- erwin by Quest: Unique for its automated metadata gathering and strong data policy enforcement capabilities — thus, best for visibility into enterprise data.
This article was originally published in October 2022. An update was made by the current author in January 2024. The latest update was by Antony Peyton in June 2025.
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