13 Ways to Improve Data Accuracy

improve data accuracy

Do you need to improve data accuracy to ensure you make the right decisions? Here are 13 ways to improve data accuracy for better insights. Regardless of your industry, operations or location, it’s critical to pay close attention to both the quality and accuracy of your business data since insights developed from that data guide business decisions. And, we all know the adage about garbage in/ garbage out. Poor data leads to poor decisions that can sink your business fast.

According to a recent study, poor data quality costs businesses nearly $13 million annually. Moreover, it worsens the complexities of data systems leading to wrong decisions in the long run. Fortunately, there are ways to improve data accuracy to ensure you make sound business decisions. Here are thirteen ways to help with that problem.

improve data accuracy
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Improve data accuracy

Data come from a variety of sources such as internal sales and inventory data, external data related to marketing such as social media and website analytics, and customer data coming through surveys or self-reports. Not all that data is accurate. For instance, surveys are notorious for producing bad data, especially when constructed by folks who don’t understand the nuances of survey design. Case in point is the debacle at Coke that led them to jettison their successful formula in favor of a new one based on survey data, which failed so fabulously the case is taught in every marketing research class as what not to do.

Since insights often originate by combining data from several sources or transforming the data as part of the analysis, further data quality issues can emerge if you’re not very careful. Even a hiccup in your data collection systems, analytic tools, or computers can result in poor data quality. Thus, no company is immune from data quality issues and must scrupulously work to improve data accuracy.

Here’s how:

1. Appreciate the significance of quality data

Data correctness, relevance, and readability are all important factors in establishing the quality of your company data. This information should also be accessible, complete, and timely. However, according to data quality research, American businesses now feel that 32% of their data is inaccurate, representing an increase from 25% in 2014. That means the situation is getting worse in most cases. Your data experts must solve the problem and reach a more satisfactory data input accuracy rate. Data values, for example, must reflect “the right figure and indicate formats that are not confusing. This step will help keep you from making avoidable errors that could cost your business.

2. Centralize your database

Centralizing your database can be an absolute game-changer regarding data quality because there’s only one place that contains updated information. By housing your data in a single location, you can ensure that it is consistent, accurate, and more usable. Whether you bring together data from various sources or work to reduce the risk of errors and duplication, a centralized database is often the key. Additionally, it provides an unparalleled view of your data, enabling you to establish governance policies that maintain the quality of your information.

3. Encourage data literacy and educate your employees

It is crucial to prioritize employee training and education. Investing in comprehensive training programs can empower your employees with the knowledge and skills needed to maintain and improve data accuracy. You can survey employees to obtain valuable insights into their understanding of data quality concepts, their proficiency in data entry and validation techniques, and their awareness of the impact of accurate data on business outcomes. This survey can serve as a baseline assessment to identify areas of improvement and tailor training initiatives accordingly. By equipping your employees with the necessary knowledge and continuously educating them on data quality best practices, you can foster a culture of data accuracy and ensure that every team member contributes to maintaining high-quality data throughout the company.

4. Keep your data sources in sync

Data synchronization is comparable to having a single source of fact for the database. Changes are automatically updated when an organization’s data sources are in sync across two or more devices. It helps to maintain system consistency, reduces mistakes, and makes it easier to maintain clear and accurate data that drives informed decision-making and business success. As a result, if no flawless synchronization mechanism is in place, data transfers may be delayed due to erroneous data.

5. Assign permissions for data changes

You can significantly impact the data quality by assigning the appropriate authorizations to meddle with data. By putting a lock on data modifications, you can curb the risk of erroneous entries and secure an unbroken flow of accurate details. That becomes especially paramount in collaborative setups, with several team members working on the same data set. By customizing access levels based on roles and associated duties, you can empower each member with precisely their scope of action and responsibility proportional to their workload. Moreover, introducing a mechanism for change requests and approvals can bolster data quality by adding a layer of vigilance and authentication.

6. Foster cross-department collaboration

Break down silos and promote collaboration to enable various business units to collectively contribute to maintaining accurate and consistent data. When departments work together, they can share knowledge, insights, and best practices for data management. This collaboration allows for a holistic view of data processes and encourages a collective responsibility toward data accuracy. Through cross-department collaboration, teams can identify data dependencies, establish data governance frameworks, streamline data integration, and ensure effective communication and coordination regarding data-related activities. You can leverage the expertise and perspectives of various departments to achieve higher data quality standards, improve data consistency, and drive better decision-making across the entire organization.

7. Implement robust data security measures

Implementing strong data security measures is vital for improving data quality. Firstly, organizations should establish strict access controls, user authentication protocols, and multi-factor authentication to prevent unauthorized access to sensitive data. Robust encryption should protect data in transit and at rest, allowing only authorized individuals to access the information. Regular encrypted data backups stored in secure offsite locations reduce the risk of data loss or corruption. Intrusion detection systems and resilient firewalls help detect and prevent cyber threats, reducing the chances of data breaches. Regular security audits and penetration testing proactively identify vulnerabilities. These comprehensive measures enhance data quality, maintain customer trust, and ensure regulatory compliance.

8. Use data profiling early and often

Data quality monitoring is the process of assessing existing data and assessing information about it. It aids in identifying remedial measures to be implemented and also provides important insights that can be provided to the business to stimulate creativity in improvement strategies. Data profiling can assist in determining which data quality concerns must be addressed at the source and which can be addressed later. Additionally, data quality monitoring helps companies gain valuable insights and achieve their data management goals, such as improving data accuracy, completeness, and consistency while boosting company efficiency and reducing operational costs.

9. Identify inaccuracies in your internal and external data sources

When delving into the causes of data inaccuracies within your business, it is common to uncover several internal and external sources of errors. Among the frequently encountered sources are data movement, data decay, and incorrect values. Inaccuracies occur in data movement when data is transferred between databases or systems, leading to improper alterations due to disconnections among multiple databases. Over time, inaccuracies accumulate as changes occur without being accurately reflected in the corresponding data records. That includes outdated information like marital status, telephone numbers, and addresses. Incorrect values also refer to situations where incorrect values are initially entered and persist without being corrected. Although these inaccuracies may arise from simple typographical errors, they still result in data inconsistencies, making it vital to identify them as soon as possible.

10. Undertake regular data audits

Regular audits play a vital role in improving data quality within an organization. It is critical to perform periodic assessments and audits to verify that your data is excellent and maintained. Data audits assist corporate executives in addressing various issues and making educated decisions, including consumer data integrity and security. Also, an audit might assist in revealing silos and access concerns that would otherwise go undetected. Make data audit a routine part of your data management to maintain data integrity over time to provide valuable insight and drive business growth.

11. Collect and store all data outcomes

Collecting and preserving all data results is an important step toward increasing data quality. Businesses may detect trends and patterns, evaluate performance, and make data-driven choices by collecting all data results. According to an Experian poll, 92% of firms feel that quality data drives business success, highlighting the need to maintain data integrity and completeness. However, according to the same report, 19% of firms have a defined data quality plan. Companies may acquire a comprehensive understanding of their data by collecting and retaining all data outputs, which can lead to more accurate and trustworthy insights. You can use resources like field service management software to collect real-time data, help with decision-making, optimize processes, and save expenses.

12. Act with speed

In the lightning-paced world of digital data, there’s simply no time to delay when it comes to enhancing your data quality. With more data at your fingertips than ever, it’s crucial to be proactive in pursuing accuracy and dependability. The conflict between speed and accuracy is among the most pressing concerns that large and small businesses regularly confront. Companies that address the problem more swiftly and successfully will put their company ahead of their competition. Fortunately, outsourcing your data entry requirements to trustworthy business partners is one of the most timely and cost-effective ways to improve your data accuracy.

13. Establish a data quality standard

One of the most effective ways to improve your business’s data accuracy is to establish high-quality data standards applicable throughout the organization. Because various stakeholders in a company are likely to have varying levels of business sensitivity, culture, and maturity, the method and speed with which data quality enablement needs are satisfied may differ. Establishing a data quality standard ensures that various enterprise stakeholders understand and execute defined and agreed-upon data quality standards. This step will also assist in educating all parties involved and make implementation easier.


Accuracy is critical in today’s data-driven environment for making educated decisions and generating company success. Improve data accuracy and job quality by applying the above-listed strategies. This way, you’ll improve the quality of your business’s data and reach your goals with certainty.

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