Best practices to improve data quality

Data quality improvement is often a difficult, expensive, and time-consuming proposition. Read about these critical practices to keep your data clean and your reporting accurate.

The right data can be used for multiple purposes, including decision-making, business planning and operations. With data as the foundation for most enterprise IT systems, the quality of data becomes vital to the overall success of the IT ecosystem.

Data that is of poor quality can result in inaccurate analytics, operational inefficiencies and other types of issues that won’t let your business reach its maximum potential. In this guide, we offer best practices for improving data quality across your organization’s various data sets and systems.

SEE: Data quality vs data governance: How they impact your business (TechRepublic)

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What is data quality?

Data quality describes the condition of an organization’s data in terms of consistency, accuracy, reliability and completeness. Data that is high quality fits the purpose it was intended for and offers an accurate representation of the real-world construct that it refers to.

Data quality itself is an easy enough concept to understand, but maintaining high data quality standards can be difficult without the right strategies and best practices in place.

Tips to improve data quality in your business

The emphasis on data quality has increased as organizations realize the value of data in driving business decisions and improving the efficiency of operations. 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 lifecycle.

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. The metrics should be measurable and specific so you can objectively evaluate and improve the quality of data. Some examples of metrics you can use for data quality include the number of data test failures or the percentage of data test coverage.

Establish a process to investigate issues related to data

When you encounter issues or errors related to data, you must have an established process to investigate the problem. This will help you understand the issue and take steps to improve data 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.

SEE: Data governance checklist for your organization (TechRepublic Premium)

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 of data in your business.

Enlist data stewards

Data stewards are responsible for the implementation of data policies, rules and procedures as set by your business’s data governance framework. You can enlist data stewards 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 your IT or any other business unit.

Prioritize a data culture in your business

Not only do you need to invest in hiring and training both data stewards and data quality specialists, but you also need to promote a data-driven culture throughout your 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.

Use data quality solutions

Using data quality solutions to support your data quality management strategy is a great way to realize the full potential of your data. Data quality solutions offer benefits in terms of quality, costs, efficiency and scale. You 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.

Data quality solutions

The data quality solutions market is vast, even offering specialized solutions for specific industries and business use cases. But for most companies, a more general data quality solution will meet their needs so long as it integrates with their existing tech stack. For teams that want something straightforward and comprehensive, these two data quality solutions are strong options with a variety of user features:

Ataccama ONE Data Quality Suite

Ataccama offers tools that can turn raw data into actionable data products to support operations, analytics, AI and various other functions of a business. Ataccama ONE includes premium features such as data anomaly detection and automation tools to support a business’s data quality management goals. It also offers access policy documentation and other features that can boost data quality.

Precisely Trillium

Although Precisely has been around since the 1960s, it has only recently acquired and refined its metadata and data governance capabilities. The company offers several types of data quality solutions, including Precisely Trillium, which offers data standardization and cleansing functions to improve and maintain data quality. Implementation can be done on-premises or over the cloud. The open APIs offered by Precisely Trillium allow it to more seamlessly connect to the existing IT ecosystem of your business.