Data Quality

Data Quality: Ensuring Accuracy and Reliability in Your Data

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Data is critical to any business organization’s day-to-day operations and future growth. Most companies collect large amounts of data, but they don’t necessarily know how to manage it for efficient, high-quality use—and your company needs to prioritize the quality of the data collected.

After all, if data is helping to determine your strategy and business decisions, it’s only going to be as successful as your data quality. Maintaining data quality will ensure you have accurate customer data to support your company’s success and growth.

Learn how data quality assurance, management, and tools can contribute to your business’s long-term goals and success.

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What is data quality?

Data quality refers to how well a dataset meets the criteria for factors like accuracy, completeness, consistency, reliability, relevance, timeliness, and validity. In the customer relationship management (CRM) community, data quality is measured and maintained using seven steps, including:

Each of these steps must be applied to ensure high-quality data. When your business collects customer data, it may seem like you’re on the right track because you’re gaining information that can help with overall business operations. However, if you’re not cleaning your data and maintaining a high level of data quality, your organization may be wasting valuable resources and time with poor-quality data that’s leading you astray.

Learn how your organization can make clean data a sustainable priority.

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Data quality vs. data integrity

Both data quality and integrity are crucial components that work in tandem to help achieve goals. Data quality focuses on ensuring that data is accurate, reliable, and relevant. Data integrity goes further into ensuring data is complete, consistent, and secure while remaining relevant to the goals. Data integrity also focuses more on unchanged data over the span of its lifecycle.

By considering data integrity, your business can use good quality data to help stakeholders make informed decisions that apply over the long term. 

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Why is data quality important?

When data quality is defined as “poor” it means the information used by the organization doesn’t meet the standards of the seven high-quality data dimensions. 
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Bad outcomes rooted in poor decision-making

As businesses use data more and more to influence their day-to-day decisions, processes, and strategies, data quality has become a core signifier for future success.

Data processing is only as useful as customer data integrity. If you have poor data quality, your company will likely lose more than just money. Other potential impacts include:

  • Loss of operational efficiency
  • Reduced campaign effectiveness
  • Competitive disadvantage
  • Poor long-term strategy choices
  • Tarnished brand reputation
  • Disrupted customer journey
  • Employee churn
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Lack of awareness of opportunities and threats

Good customer data paints a picture of your company’s overall strengths and weaknesses—from past to present to future. With insufficient data, your organization won’t be able to identify and subsequently improve on weak points or properly understand and build on their strengths.

If your data is not serving you, it’s essential that you implement methods to improve its quality.

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If your data quality issues go unresolved, companies stand to lose efficiency in almost every step of their business processes. From basic operations to targeted campaigns and live customer interactions, your initiatives are much more likely to fail or show reduced success if you’re working with poor data quality.


of companies think they lose existing customers due to poor CRM data quality.

*(source: Validity Research: State of CRM Data Health 2022)

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Benefits of good quality data

When you have good quality data, your business is more prepared to make better decisions and improve productivity and efficiency. Using quality data that meets company standards and expectations helps influence decisions. Businesses benefit across the board from positive impacts like:

  • More informed decision-making
  • Better audience targeting
  • More effective content and marketing campaigns
  • Improved relationships with customers
  • Easier implementation of data
  • Competitive advantage
  • Increased profitability
  • More seamless data migrations

Since your team leaders and members can trust and rely on good quality data, they’re more likely to use it effectively, accelerating your growth and profitability.

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6 core principles of quality data

Several factors can help your business determine if you have good data quality. Streamline your data quality management and pinpoint where to make changes to improve by asking yourself how your data performs in each of the following dimensions.

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1. Accuracy

Accuracy is arguably the most straightforward component of quality data. It determines exactly how precise or error-free your gathered information is, letting you determine the data’s reliability and trustworthiness.

For most companies, data is collected from many sources (depending on the industry, think web-to-lead and web-to-case forms, point-of-sale systems, online reservation sites, etc.) and stored across multiple systems instead of being streamlined into one cohesive platform. Using various systems increases the potential for inaccurate and inconsistent data, such as unreliable data.

A solution to this is to implement a data management system that can verify key components of customer data, like email addresses, and standardize data points used to fuel reports. Both are easy to implement, with automation available after implementation, and will substantially improve data quality and accuracy.

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2. Completeness

When gathering data, your business must consider exactly what information it needs to ensure the completeness of your customers’ data. In other words, does your data include every piece of information your teams need to apply and use the data properly? If not, you’re working with incomplete data, which will negatively impact your data quality.

For example, say you’re building a mailing list, but the customer didn’t include their address. You cannot complete your task with only partial information, so the submission is not helpful. To combat the issue, you could:

  • Make a checklist of every point of data needed
  • Enable required fields for customer-submitted data
  • Use AI technology to sort through data weak points
  • Run exception reports to surface records with missing data points

Implementing methods to ensure complete information is crucial so the datasets are analyzed correctly.

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3. Reliability

Good-quality data builds trust, and reliability will help ensure you can trust your data and build confidence across your organization. For data quality, reliability means that a piece of information doesn’t contradict the same or another piece of information existing in a different source or system.

Imagine you’re tracking a customer’s birthdate in two unconnected databases. In one system, your customer’s birthday is listed as 12/9/1982, while in the other, it says 12/9/83. These contradictory values for the same data point render your data unreliable.

If your data contradicts its own past or another source’s data, it can signify inaccuracy and hurt your business’s ability to trust and use the collected information.

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4. Relevance

Similar to accuracy, relevance can make or break a dataset’s usability to your team. Expansive and thorough data doesn’t mean anything if the actual content isn’t needed or useful to your company.

An easy way to figure out if your data is relevant is to ask yourself and your team members a couple of questions:

  • Why are we collecting this information? 
  • What will it be used for?

If the answers don’t fit your business’s needs and strategies, you might gather irrelevant information, resulting in low-quality data. Data for the sake of data is a waste of time, money, and resources for companies. It creates noise and drowns out the value of your relevant data points. Your data has to be relevant to your current and future goals to ensure it is of good quality. 

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5. Timeliness

Was your data collected recently? Was data entered into the system in the right time frame to not delay workflows dependent on that information to function accurately? If the answer is no, then your business’ data may struggle with timeliness, which indicates how up-to-date your information is.

Once you collect your data, it may seem that you have the information you require to take action and make decisions. However, data needs to be collected in a timely manner to ensure it is not out-of-date.

In many cases, data from a year ago—or even a month ago—won’t accurately reflect the information you need for today. Proper data quality management needs to be aware of how often data needs to be collected to ensure you have the most current data.

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6. Validity

When discussing data quality, validity is how data is measured for accuracy and reliability within a dataset. It ensures all data entries are error-free and consistent.

An easy way to understand this concept is to consider how dates, like birthdays, are collected. Depending on your country, industry, or business standards, dates may be ordered in a specific way (think month/day/year or day/month/year) or collected in precise format (numbers vs. words).

Either way, if the information is entered without following the specific business rules, it will appear invalid.


of companies agree that accurate CRM data improves conversion rates​.

*(source: Validity Research: State of CRM Data Health 2022 )

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Assessing and improving data quality

When improving data quality, it’s follow quality assurance practices. Data quality assurance is the process of identifying inconsistencies and outliers and cleaning the dataset to ensure it’s complete, accurate, and relevant. To help your team efficiently perform quality assurance, a platform like Validity DemandTools® is your best solution.

The tool enables organizations to clean and manage data using secure and automated processes, helping your team stay productive, efficient, and effective.

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The role of data quality management

Data quality management is essential for maintaining quality information and data. It involves cleansing, protecting, monitoring, and analyzing data so decision-makers and stakeholders can make informed decisions for the company’s betterment.

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Ensuring data quality across your organization

If everyone agrees that good quality data benefits companies, then who ensures your business’s data quality is up to par? Each business should have a cross-functional data operations team responsible for high-quality data through management and governance. Your data is only as strong as your efforts to maintain data quality. A good rule of thumb is to include admins in your cross-functional team with a background in sales, service, or finance because they will be able to understand and integrate the functional business needs and expectations of data.

Some data management stakeholders that also play a role in data quality maintenance include:

  • Top management: Top management stakeholders include everyone from C-suite executives to department directors. These are the company plates that influence business strategy overall. Although they may not be directly involved in data collection or analysis, they make the big decisions. If any data pain points need to be addressed, detailed reports should be sent to top management for review.
  • Direct management: Direct management includes the team leaders to whom employees report directly. They are often the middlemen between you and top management. If your data contains inconsistencies or a missing component, they are the point of contact to flag any issues, concerns, or ideas for improvement. If you can pinpoint who can identify problems and who can find solutions, you’ll be able to standardize your data quality better.
  • IT department: Your IT department plays a critical role in collecting, analyzing, and storing quality data. The IT department is responsible for standardizing data management systems and onboarding employees to use proper data techniques to minimize data errors.
  • Governing and industry standards bodies: Depending on your location and industry, your data may be subjected to specific regulations to ensure it meets privacy and compliance requirements. Make sure that your company is following best practices to ensure the quality and security of data.
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Building good data management practices

Good data management is the key to good quality data. It not only dictates how you collect, store, and analyze data, but it also helps to maintain the integrity of the information you use. Improve your data management using best practices, such as:

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Outline your company’s goals.

Create a list of your business’s top priorities for the future. Then, pinpoint what customer information will help you achieve or improve these goals.

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Define and share good data quality components.

Your employees and team members need to understand exactly what data expectations are in place before they can collect and analyze the relevant information. Keep your team updated with data needs so they can flag problem areas and identify improvement points.

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Ensure your data is secure.

Customer data must be kept safe in accordance with industry standards. Use various data quality tools, like Validity BriteVerify® or DemandTools, to keep data secure and reduce the risk of breaches.

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Make data accessible to your employees.

Good quality data won’t help anyone if it can’t be accessed by the necessary teams. Collect, store, and share your data in a central platform so you don’t lose it due to improper management.

In addition to these practices, an effective means of data management is implementing a secure data management platform that can clean and maintain CRM data to help improve your business operations. By opting for a standardized data management platform, your business can make sure that all collected data meets the following criteria:

  • Data standardsEach business must outline quality data components and share its standards to ensure good quality data. Ambiguous data standards will result in low-quality data, as each piece of information is held to different scrutiny or quality control levels.
  • Metadata management standards: Locating and accessing data is at the heart of good data management systems. By creating a metadata repository that uniformly names and defines each data point, your business won’t struggle with lost or misplaced data. This can also be captured in the field-level setup in the CRM space.
  • Data validation rules: By setting specific data validation rules, data that does not meet your business’ preferred format won’t enter the system. Instead, an error will be flagged so you can fix, replace, or remove the mistaken data.
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Start your journey toward better data today.

Have the right tools to perform data quality assurance and management practices to help your business make decisions that benefit the company. 

Explore how Validity DemandTools gets you clean data so you can market, sell, and support more effectively.