Best practices to improve data quality

Data quality, the condition of an organization's data in terms of consistency, accuracy, reliability, and integrity, is a fairly easy concept to understand. The challenge is to achieve and maintain high data quality standards.

Without the right strategies and best practices, it can be difficult to maintain and improve data quality. Getting it right is critical because ensuring data quality comes with multiple benefits, including better decision making, business planning, and operations. More importantly, poor data quality can lead to inaccurate analytics, operational inefficiencies, and other types of problems that can prevent a business from reaching its full potential.

Since data is the foundation of most enterprise IT systems, data quality becomes vital to the overall success of the IT ecosystem. Therefore, it is critical that organizations follow best practices to improve data quality across their various data sets 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 some tips to improve data quality at all stages of the data management lifecycle.

Decide how to measure data quality

There are many ways to measure data quality 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 its business. To objectively evaluate and improve data quality, metrics must be measurable and specific.

SEE: Data quality assessments It can be used to measure data quality.

Some examples of data quality metrics include the number of data test failures or the data test coverage percentage.

Establish a process to investigate data-related issues.

When they encounter data-related issues or errors, companies should have a data quality process in place to investigate the issue. This will help understand the problem and allow those responsible for managing the data to take measures to improve its quality. Identifying the problem would be one of the first steps in the process. Every time a problem is resolved, steps must be taken to minimize the likelihood of it occurring again.

The process may include a data quality checklist to determine if there are data inconsistencies, gaps in the timeline, formatting errors, or missing attribute values. Repeating this process over time will help improve data quality.

Recruit data managers

Data stewards are responsible for the implementation of data policies, rules, and procedures as established by the organization's data governance framework. Data stewards can be hired to work closely with the data under their control and make improving data quality a priority. Data stewards can be people from IT or any other business unit.

SEE: Data management and data governance many times they go hand in hand.

Prioritize a data culture

Organizations not only need to invest in hiring and training both data stewards and data quality specialists, but they must also promote a data-driven culture throughout the enterprise. This culture must start from the top. Senior managers and leaders in the organization must lead by example, prioritizing data-driven business decisions and investing in data quality tools and roles.

A company must empower the data team by choosing to have a consensus culture rather than a hierarchical setup. Any boundary between data specialists and business leaders must be porous, allowing for easy flow of information and insights.

Improve 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 stewards must design and implement a data governance plan. This framework would describe the management, use and protection of data in the organization. It typically includes policies, procedures, standards, and metrics that improve data quality and ensure it remains that way over time. At a minimum, the data governance plan should address the following areas:

• Quality standards: It sets clear standards in terms of accuracy, reliability, consistency and integrity.
• Functions and responsibilities: Defines data roles and responsibilities, including identifying users, owners, and administrators.
• Policies and procedures: Develops policies and procedures for collection, storage, processing, sharing, and compliance.
• Security and privacy: Implement security measures against unauthorized access and compliance protection.
• Life cycle management: Manage the data lifecycle, from creation and acquisition to archiving and deletion.
• Monitoring and reporting: Establishes mechanisms for continuous quality monitoring, including periodic audits and reports.
• Training and awareness: Train and sensitize 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 harness the full potential of data. Data quality solutions offer benefits in terms of quality, costs, automation, efficiency and scale. Data stewards 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 simple and comprehensive, these five data quality solutions are solid options with a variety of user-friendly features:

1. Ataccama UNO: The best AI-powered enterprise platform for overall data management and governance.
2. Precisely Government Data360: Best for data analysis and excels at automating governance tasks, facilitating discovery, reporting, and auditing.
3. Collibra Data Governance: An all-in-one solution that maintains data quality and security, including a business glossary, administration management, and intuitive workflows.
4. IBM Data Governance: It integrates with existing systems to protect the confidentiality, integrity and availability of data and is distinguished by its governance processes, standardization and machine learning algorithms for task automation.
5. erwin by search: Unique for its automated metadata collection and robust data policy enforcement capabilities; therefore, it is better for business data visibility.

Top 5 GRC Solutions