5 Tips to Improve Data Quality

Optimize your data quality and make informed decisions


May 4, 2023 | Read time: 5 min


Just as there are good and bad calories when it comes to nutrition, there are also good and bad quality data when it comes to software. Good calories provide us with nutrients and energy, while bad calories provide no nutritional value and lead to health problems such as obesity and heart disease.

Likewise, good data provides us with valuable insights and helps us deduce market trends and visualize behaviors. These projections influence the outcomes of market analysis, portfolio optimization, and forecasting. At the same time, bad data can lead to faulty decisions that negatively impact your business. Incorrect data is not only costly but leads to misinformation that negatively impacts our business.

The good news is that you can improve your business performance and customer relationships by maintaining high-quality data. Follow these five tips to optimize your data quality and make informed decisions based on accurate, up-to-date information.

1. Identify Your Customers and Prospects Clearly

The first step towards reaching high-quality data is to accurately identify each customer in your data pool. This involves ensuring that the information is complete, up-to-date, and free of duplicates or fragmentation. It implies cleaning your data, removing duplicates, and updating outdated information. By having a crystal-clear understanding of your data pool, you can personalize your communication and provide a positive customer experience throughout the complete customer journey.

For example, the marketing team can use customer data to personalize marketing campaigns for specific individuals or households, resulting in higher engagement rates and ultimately, higher conversion rates.

2. Continuously Update and Add Relevant Data

Data becomes outdated over time, so it's vital to keep it updated. Track changes regarding addresses, bank details, phone numbers, and email addresses, and ensure that the customer’s order history is accurate. This requires knowing the actual state of your data and detecting any missing or incomplete information. By having a 360-degree understanding of your data pool, you can ensure that your marketing campaigns and customer communication are accurate.

For example, an e-commerce company can use customer data to track order history and provide personalized product recommendations, achieving increased customer satisfaction and customer loyalty.

3. Keep Up with the Ever-Changing World

In this modern era that we live in, the world around us is changing faster than ever. It’s important to keep up-to-date with customers and prospects that have already adopted all digital trends. Your businesses must follow everywhere they are, you must act multi-channel. More than that, new laws and regulations such as the GDPR require additional information, such as a double opt-in. Staying up-to-date with these last-minute changes will ensure that your data is law compliant and effective.

For example, a financial institution can use customer data to comply with regulations and prevent money laundering, resulting in a more secure and trustworthy business.

4. Spot the Black Sheep - Detect and Prevent Fraud

In today's digital and globalized world, it's crucial to identify fraud attempts early and comply with legal requirements in order to prevent initiating business with bad parties. By keeping a high-quality database and comparing it with sanctions lists you can easily recognize fraudulent client. This way you can prevent financial loss and maintain a trustworthy reputation.

For example, an insurance company can use customer data to prevent insurance fraud, resulting in increased profitability and trust among loyal customers.

5. Consistent Data Quality Management

Data quality is not a one-time action, it rather requires continuous effort. It’s like rowing against the current. As soon as you stop, you drift back. By focusing on quality from the start and keeping up-to-date with changes, you will avoid losing hard-worked levels of data quality.

Before you can effectively manage data quality, it’s important to set data quality management responsibilities among your data scientists, data engineers, and business analysts. Identify the data quality management team members who will be responsible for collecting, analyzing, and maintaining unstructured data. For each set of tasks and roles, ensure the scope of their functions is properly established and agreed upon. Provide training to ensure employees have the proper skills to manage the data accordingly.

By following these five tips, you'll be well on your way to achieving and maintaining exceptional data quality. The reward is an extraordinary understanding of one's own customers and prospects, how to serve them, and make informed decisions to deliver personalized experiences throughout the customer journey.

Qualysoft can help you gain clarity about the state of your data –  quickly,  easily, and in compliance with data protection regulations – within the relevant data quality dimensions.

Qualysoft can help you gain clarity about the state of your data

Consistency – Is the existing data consistent?

Completeness – Are all the corresponding data fields filled, how much data is missing?

Uniqueness –  Are the records unique?

Timeliness – Is the existing data up to date?

Validity - Does the data serve its purpose?

Accuracy - Is the existing data correct, or close to reality?


We provide individual business feedback based on the result, with reliable KPIs and target-oriented recommendations for sustainably improving the quality of your data.