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 data quality 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 of ensuring data quality, including improved decision-making, business planning and operations. More importantly, poor data quality can result in inaccurate analytics, operational inefficiencies and other types of issues 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. Therefore, it’s paramount that organizations follow best practices for improving data quality across their various datasets and systems.
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4 quick tips to improve data quality
Improving data quality is a never-ending process, and that is exactly how it should be treated. Here are a few tips to improve data quality at all stages of the data management life cycle.
Decide how to measure data quality
There are many ways data quality can be measured, and there are no set standards for the metrics that should be used to measure data quality. Ideally, an organization should measure data using metrics that are meaningful to their business. To objectively evaluate and improve the quality of data, the metrics should be measurable and specific.
SEE: Data quality assessments can be used to measure data quality.
Some examples of data quality metrics include the number of data test failures or the percentage of data test coverage.
Establish a process to investigate issues related to data
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 will allow those in charge of handling the data to take steps to improve its quality. Identifying the problem would be one of the first steps in the process. Every time a problem is resolved, steps should be taken to minimize the likelihood of this problem 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 data 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. Data stewards can be enlisted to work closely with data under their control and make it a priority to improve the quality of data. Data stewards can be individuals from the IT or any other business unit.
SEE: Data stewardship and data governance often go hand-in-hand.
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 of the organization 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.
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 data quality processes, data managers should devise and implement a data governance plan. This framework would outline the management, use and protection of data in the organization. Typically, it includes policies, procedures, standards and metrics that improve data quality and ensure it remains so over time. As a bare minimum, the data governance plan must address the following areas:
- Quality standards: Establishes clear standards in terms of accuracy, reliability, consistency and completeness.
- Roles and responsibilities: Defines the roles and responsibilities for data, including identifying the users, owners and stewards.
- Policies and procedures: Develops policies and procedures for collection, storage, processing, sharing and compliance.
- Security and privacy: Implements security measures against unauthorized access and protection for compliance.
- Lifecycle management: Manages the data life cycle, from creation and acquisition to archiving and disposal.
- Monitoring and reporting: Sets up mechanisms for continuous quality monitoring, including regular audits and reporting.
- Training and awareness: Trains and raises awareness among the staff about the importance of data governance and their role.
Consider using data quality solutions
Using data quality solutions to support data quality management is a great way to realize the full potential of data. Data quality solutions offer benefits in terms of quality, 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 five data quality solutions are strong options with a variety of user features:
- Ataccama ONE: An AI-powered enterprise platform best for overall data management and governance.
- Precisely Data360 Govern: Best for data analytics and excels in automating governance tasks facilitating discovery, reporting and auditing
- 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.
