Guide to Data Quality

Data quality measures how accurate and complete your data is, so you can confidently use it to inform your business decisions. Your organization likely collects data from a variety of sources in order to better understand your customers, the market, and your company’s performance. However, the data is only good if it’s accurate and reliable. That’s why high-quality data at every level of collection, transformation, storage, and analysis is so important.

This guide provides a high-level overview of what data quality is and how data management tools can help you create accurate and complete reports.

Data quality measures if your data is accurate, complete, consistent, and reliable enough to support effective decision-making and business outcomes. High-quality data is trustworthy and actionable, and your teams can feel confident as they use it for analytics or to inform strategic planning. On the opposite side of the spectrum, poor data is inaccurate or incomplete, and using it can lead to inefficiencies, missed opportunities, and incorrect conclusions.

Key dimensions of data quality include how accurately data reflects reality, if it’s complete, and if it matches other datasets.

For example, a retail company using inaccurate sales data might overestimate demand and overstock — which could be costly. If that retail company was using complete and accurate data, they might be able to pinpoint a developing trend for a certain product, and the company could gain a competitive edge. Having high-quality data and safeguards to keep it consistent can improve business operations and decision-making.

Data quality is often used interchangeably with a few other terms, but there are a few key differences between data quality, data integrity, and data profiling.

• Data quality: This is the overarching measure of data’s suitability for use. It includes attributes such as accuracy, completeness, consistency, and timeliness to make data effective decision-making.
• Data integrity: This has to do with the long-term accuracy, completeness, and consistency of data throughout its lifecycle. Data integrity policies make sure data remains intact and unaltered during transfers, storage, or processing. Integrity makes it possible for you to trust historical information.
• Data profiling: This is the process of analyzing and assessing the structure, content, and relationships within a dataset. Profiling identifies anomalies, patterns, and completeness issues in order to improve data quality and integrity.

While there are distinctions between these three terms, they are connected. Data quality is the umbrella concept that benefits from both data integrity and data profiling. Data integrity protects the trustworthiness of data over time, while data profiling can highlight areas where you might need improvement. Together, they form a foundation for effective data management practices.

Data quality is the backbone of effective business operations since accurate data fuels your decision-making and helps you provide improved customer experiences. High-quality data helps your organization operate efficiently and creates a strong foundation for analytics and using AI systems. In contrast, if you’re using low-quality data, you could experience costly mistakes, poor decisions, and diminished trust among stakeholders. While poor data quality can hurt your reputation, using high-quality data to fuel your analyses can improve transparency and accountability, which can help your customers, partners, and stakeholders trust your company.

From a compliance and governance perspective, data quality is non-negotiable. The cost of poor data quality on your organization can be immense. Inaccurate data can misguide marketing campaigns, skew your financial projections, or lead to incorrect AI and analytics outputs. This not only wastes your company’s money and resources but also erodes confidence in your capabilities. Inconsistent, incomplete, or inaccurate data can result in non-compliance with regulations like GDPR, HIPAA, or CCPA — leading to hefty fines and legal challenges.

These are seven elements of data quality.

Accuracy refers to how closely data reflects the real-world entities or events it represents. Accurate data is reliable, which makes it useful to base decisions on. For example, having accurate numbers for how many times customers have clicked on a page can help you determine if the layout of a page is effective.

Completeness measures whether all required data is available and filled in. Missing fields, such as a customer’s contact details or a product’s SKU, can disrupt operations and analytics. Complete data provides a full and usable picture, while incomplete data could lead to skewed analysis results.

Consistency has to do with uniformity across data sets and systems with no conflicting entries. For instance, if a customer’s address appears differently in two databases, you might end up duplicating the customer profile or using the incorrect address, leading to potential inefficiencies or product errors. Consistent data, on the other hand, improves data integration and lets you confidently analyze a dataset.

Validity is about if data conforms to predefined formats, standards, or rules. For example, a date field should only contain properly formatted dates, and an email field should only include valid email addresses. Valid data minimizes errors and improves usability.

Timeliness has to do with how up-to-date data is and its availability when needed. Real-time data activation is important for financial reports or current inventory levels where old information could yield completely different results. Outdated data can lead to poor decisions and missed opportunities.

Uniqueness is about if each data record is distinct without duplicates. For example, having duplicate customer profiles in a CRM system can lead to inefficiencies and inaccurate reporting. Maintaining uniqueness between entries and eliminating duplicates simplifies management and improves data trustworthiness.

Integrity is about the relationships between data elements so they remain accurate and intact. For example, if a database that links a customer’s email with their account number has outdated emails, that would be an integrity issue — as well as a timeliness problem. High integrity means your data is consistent across systems, while low integrity could indicate a problem with your data.

High data quality has significant advantages because of its direct impact on data-driven decision-making, customer trust, and operational efficiency. These are some of the main benefits of having high quality data:

• Improved strategic planning: When you’re using accurate data, you can make informed decisions and improve your data strategy. For instance, accurate sales data improves forecasting, so you can accurately determine inventory and demand, which then reduces costs and ensures you have the right products for your customers.
• More personalized customer interactions: Accurate data can help you better understand your customers, which lets you personalize your interactions and build trust. For example, having a single, accurate customer profile across all touchpoints lets you tailor marketing and support. These positive interactions can then strengthen customer loyalty.
• Increased compliance: High data quality supports compliance with regulations, reducing the risk of penalties and reputational damage.
• Strong foundation for AI use: AI automation can help you speed up processes and reduce the risk of human error. Having high quality data lets you take advantage of AI more because you’re able to feed it reliable data.
• Better ability to scale: In general, high-quality data can improve outcomes such as ensuring your infrastructure can handle the quantity of data you have.

Maintaining high data quality comes with its share of challenges, starting with data silos. When information is stored in disconnected systems across departments, it becomes difficult to analyze effectively. This fragmentation often leads to duplicate or conflicting records, which reduces the accuracy of your data.

Another significant hurdle is the lack of standardization. Inconsistent data formats, missing fields, and varying input methods create inefficiencies and inaccuracies. For example, mismatched customer records can result in errors in billing, shipping, or communication.

To help maintain your data’s quality, you’ll want to invest in data quality management tools. These tools help you identify and resolve inaccuracies, inconsistencies, and gaps in datasets — helping your data remain reliable and actionable. By automating tasks such as data profiling, cleansing, and monitoring, these tools reduce manual errors and save valuable time.

Key features to look for in a data quality management tool include:

• Data profiling: provides insights into the structure, completeness, and quality of your data. For example, the tool could point out where there are empty entries in a dataset.
• Cleansing capabilities: address errors such as duplicates or invalid entries. For example, the tool could automatically combine duplicates and highlight entries that are formatted incorrectly.
• Real-time monitoring: ensures ongoing accuracy and compliance. This helps you keep track of your data and maintain its integrity.

Advanced tools often include rule-based validation, automated workflows, and reporting dashboards to simplify data governance and track improvements over time.

Data 360 is designed to help you centralize all your data from a variety of sources and maintain data quality. And because Data 360 connects natively with the Salesforce platform, you can use the same objects and structures you’re already accustomed to.

Learn more about Data 360 and how it promotes the quality of your data.