Top 10 Benefits of Data Quality

Data science for business analytics. Infographics on futuristic virtual interface screen.


Data modeling for business analytics. Infographics on futuristic virtual interface screen.
Image: Song_about_summer/Adobe Stock

Data quality is essential for any organization that relies on data to make decisions. Most companies realize that they need to monitor and maintain their data quality, but not all employees understand the benefits of data quality and how high-quality data can extend business use cases and opportunities. In this article, explore 10 of the ways that data quality can have a positive impact on your business.

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What is data quality?

Data quality is a measurement of company data that looks at metrics such as consistency, reliability, completeness and accuracy. The highest levels of data quality are achieved when data is accessible and relevant to what business users are working on.

To use a simple baking analogy, high data quality is achieved with the right ingredients (data cleaning), measured correctly (data preparation) and combined in the right way (data transformation) to create a delicious end-product (actionable insights).

The benefits of data quality are numerous and impactful. Good data allows businesses to make better decisions, improve operational efficiencies, optimize marketing campaigns and boost customer satisfaction. It is essential for businesses that want to develop a competitive edge.

SEE: Hiring Kit: Database Engineer (TechRepublic Premium)

When data is accurate, complete and consistent, organizations can make informed decisions that lead to positive outcomes. Poor data quality, on the other hand, can lead to false conclusions, wasted time and resources, and lost opportunities.

Top 10 benefits of data quality

Raised data confidence

Confidence is critical in an organization but difficult to attain, especially because the people who use data to make decisions are rarely involved in its collection and preparation for consumption.

Take the example of a CEO who has to make tough decisions based on data provided by his technical teams. If the CEO has historically received inaccurate data that has led to poor business decisions, they may have misgivings about future data, hesitate to rely on it and seek to validate it.

When an organization has a robust data quality strategy and processes that everyone trusts, it gives the CEO and other decision-makers the confidence they need to rely on the data for decisions.

Better decision-making

Data quality directly impacts an organization’s bottom line because it affects the accuracy of decisions. When data is complete, accurate and timely, organizations can make sound decisions that lead to positive outcomes.

Poor data quality, on the other hand, can lead to false conclusions and bad decisions that can harm the bottom line.

As an example, if a bank or financial services company makes decisions based on incomplete or inaccurate data, they are at risk of making poor lending decisions, which can lead to defaults and losses. An insurance company that relies on flawed data to price its products can end up overcharging or undercharging customers, which can hurt the company’s reputation and competitive edge in the industry.

Increased scalability

As organizations grow, their data needs change and evolve. Good data quality is essential for ensuring that an organization’s data scales for new business use cases and opportunities.

Poor data quality can impede an organization’s ability to scale effectively and efficiently. For example, an e-commerce company that uses data to personalize the customer experience for each visitor to its website will need a robust and scalable data infrastructure to support this personalized experience at scale. If the company’s data quality is poor, it will be difficult to scale personalized experiences to a large number of visitors without major errors or workforce inefficiencies.

Improved consistency

High data quality is essential for ensuring consistency across an organization’s processes and procedures. In many companies, different people may need to see the same sales numbers but could be looking at totally different data sources for those numbers. Incongruency across systems and reporting can harm decision-making and cross-departmental initiatives. A consistent data quality strategy ensures that data is harmonious throughout the organization.

Readiness to deal with changes in the business environment

Organizations with good data quality are often better prepared to deal with changes in the business environment and can adapt to change more quickly and efficiently. Poor data quality can hamper an organization’s ability to change and adapt to new technologies, processes and operational needs, leading to stagnation and decline.

Lower costs

Higher data quality can also help to reduce costs within an organization. When data is accurate and complete, organizations spend less time and money on things like reprinting product documents or re-running reports after initial errors. Additionally, high-quality data can help organizations avoid regulatory fines or penalties for non-compliance.

Time savings

Inaccurate data can lead to operational inefficiencies and wasted time and resources, especially if certain members of your team spend all of their working hours on quality testing data.

A company that doesn’t maintain a complete dataset of its customers may, for example, send marketing materials to the wrong addresses or call customers by the wrong name. These kinds of mistakes not only annoy customers and damage a company’s reputation but also waste time and resources that could have been used more effectively.

Boosted productivity

The byproduct of lower operational costs and time savings is increased productivity. When an organization runs more efficiently, its employees can be more productive and focus on more strategic tasks rather than tactical data maintenance tasks.

Improved compliance

Organizations that maintain high data quality standards are also more likely to comply with the laws and regulations that govern their industry. This is because accurate and complete data makes it easier for organizations to meet their reporting requirements and avoid penalties for non-compliance.

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

In fact, with most privacy laws today, such as CCPA and GDPR, having the confidence that your data is high quality is an important first step to preparing for and passing compliance audits.

More effective customer satisfaction measures

Finally, good data quality can lead to increased customer satisfaction. When an organization has accurate and complete customer data, it can provide a better and more targeted customer experience. This usually leads to increased sales and customer loyalty.

Optimizing data quality for future business goals

Data quality should be a top priority for any business that wants to stay ahead of the competition. With the correct data, companies can make decisions that will help them thrive in today’s ever-changing marketplace.



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