What is data quality and why is it important to monetization and marketing?

Regardless of industry or company size, data quality and quantity are of critical importance to the success and longevity of any business, especially in today’s digital-first world. And while acquiring data is fairly easy, thanks to the several channels, platforms, and touchpoints with which businesses regularly engage their audiences and customers, data quality still poses a major challenge. 

“Bad data is like a faulty compass, leading organizations down the wrong path and destination,” says LiveIntent’s Gareth Evans, product director, Identity solutions. However, no organization is immune to acquiring inaccurate, outdated, duplicated, or even fraudulent data, which is why understanding data quality and its role in business is so important. Poor data quality can cost your business precious time and resources. 

Take for instance, NASA’s Mars Orbiter debacle. In 1999, NASA lost the $125 million spacecraft due to a data quality issue. Failure to translate English units to metric units caused a navigation error, propelling the spacecraft off its intended orbit. This caused the orbiter to sadly fall and burn into the Martian atmosphere.  

“Without accurate and reliable data, even the best strategies and campaigns fall short. In fact, Gartner recently estimated that bad data costs businesses $13 million each year,” said Evans. However, with the right tools to ensure excellent data quality, you can better activate data to drive engagement and revenue at scale — or keep your spacecraft in orbit.

So, how can you make sure your organization has good data quality? By understanding what data quality is, how to measure it, and how to maintain it. Let’s take a closer look.

What is data quality?

Data quality is a measure of how accurate and reliable your information is, and how suited it is for its intended purpose. An organization can measure data quality according to six data quality dimensions:

• Accuracy: Data should be correct and confirmed with a trusted, verified source. An example of data accuracy would be ensuring that all names in your customer database are spelled correctly.
• Completeness: Data sets should contain all needed and relevant components. For instance, US addresses require a property number, street name, city, state, and zip code.
• Consistency: Data should match across systems and platforms. Data consistency means that your data warehouse, cloud systems, and third-party partners should have the same, up-to-date data.
• Uniqueness: There shouldn’t be any duplicates within data sets. For example, the same person might have created an account with your company using the same name, addresses, phone number, but a different email address. This could be considered a duplicate. 
• Validity: Data should be formatted according to specific rules and standards. International phone numbers, for instance, should contain country codes while domestic phone numbers may only need area codes.
• Timeliness: Data must be current and updated in real-time. For example, if your customers make a purchase, this should be reflected across all systems and platforms so you can deliver the best customer experience possible. 

Tracking these data quality dimensions can help you assess the health of your data, find holes in your data sets, and pinpoint ways to improve your data collection processes.

Why is data quality so important?

Data is the lifeblood of your business and informs every aspect of it — from monetization to marketing — and poor data quality leads to poor business decisions. However, by measuring and maintaining data quality your business can:

• Save time and resources. As noted before, bad data isn’t just frustrating, it can be expensive. By upholding data quality, businesses can ensure that stakeholders across departments can make the best business decisions informed by accurate, quality data.
• Understand customers better. By collecting accurate, real-time data from across platforms, you can build a comprehensive, unified customer view, create accurate customer segments, and deliver more personalized and relevant customer experiences that drive revenue. 
• Improve compliance and data security. Businesses have to increasingly comply with privacy regulations and laws, like the GDPR and CCPA, in order to protect customer information. Maintaining data quality can help you meet these requirements and avoid hefty penalties.
• Better cross-departmental collaboration. When all teams have access to accurate, up-to-date, uniform data sets, they can more easily work together to meet business needs, customer expectations, and meet business goals. 

What’s the difference between data quality and data integrity?

While data quality and data integrity may often be used interchangeably with regard to the health of a data set, they are different terms that mean different things with respect to one’s data set.

Data quality

Data quality refers to the overall characteristics of data that determine its suitability for a specific purpose. Ensuring the data you plan to use is of high quality is useful for any decision making that is based on that data. 

For example, a healthcare organization might use electronic health records to store patient information, medical history, medications, and lab results. The organization would maintain data quality by:

• Implementing validation checks for inputting patient information. For instance, a validation check would ensure that letters are not entered where numbers are needed, like in the case of social security numbers or phone numbers.

• Ensuring all required fields are filled out and that the data is in the correct format. Dates can be formatted in a variety of ways. If an organization does not establish a consistent format, this can create confusion. For instance, 12/10/23 is read as October 12th, 2023 in most parts of the world, but is read as December 10th, 2023 in the US. Furthermore, dates can be formatted as two-digit numbers or a combination of two and four-digit numbers. Establishing a format eliminates ambiguity and confusion across entire organizations. 

Data integrity

Data integrity refers to the preservation of the consistency and accuracy of data over its lifetime. This might include ensuring that data is not accidentally modified, deleted, or lost. By implementing certain practices and policies, like data governance, organizations can ensure that data is only accessed and modified by authorized personnel, maintaining the trustworthiness of their data. 

Let’s take the example of the healthcare organization once again. If data is accessed and altered either accidentally or incorrectly, the implications could be dire. For instance, incorrect patient information could cause a healthcare provider to accidentally prescribe incorrect medication or tests, which could lead to serious health risks for the patient. This could also result in tremendous legal issues for the organization. 

Who should oversee data quality at your organization?

Anyone who touches data within an organization can help improve data usage and collection. Still, there are a few key roles that are often responsible for managing and overseeing data quality. They include:

• Data managers: They analyze data sets and infrastructure to measure and optimize data quality. They also work with other managers throughout the organization to create policies for improving and maintaining data quality.
• Data stewards: Data stewards might be individuals or groups at an organization that help enforce policies and ensure teams are meeting data quality benchmarks and standards.
• IT or Data management teams: They are responsible for technical aspects of data management such as storage, backup, and security. They also ensure that data quality standards are met and that data is properly integrated with other systems.
• Data analysts or business analysts: These individuals are responsible for analyzing data and identifying patterns, trends, and insights. They also validate data ensuring quality. 
• Chief data officers: These executives are responsible for managing and maintaining a company’s data assets. They help make sure those assets are used to inform strategic decisions and meet key business goals.

How can you make sure your partners provide quality data?

Your data doesn’t exist in a vacuum. Most likely, you’re working with third-party platforms and partners to help you make the most of that data and connect it to the larger ecosystem. So just like you have to maintain and uphold data quality within your own organization, it’s important to make sure your data partners deliver the same.

With an identity graph, businesses can connect customer or audience data from disparate sources to resolve identities. With identity resolution in place, businesses can create unified customer views, supported by billions of real-time signals and organic connections. This helps businesses improve data quality by enriching datasets with the latest, most accurate information available. 

How can you maintain data quality within your organization?

Maintaining data quality is an ongoing process. As your business continues to acquire data daily it’s important to keep assessing, analyzing, and updating that data to make sure it meets your business needs and standards. 

You can use these steps to guide you:

• Set your standards and goals for data quality
• Assess the current quality of your data using the six dimensions mentioned
• Establish strategies for improving data quality across those dimensions
• Carry out strategies under the advisement of data managers and stewards
• Partner with third-party data partners to authenticate and enrich your data sets
• Measure results against your goals
• Optimize strategies and continue to assess data quality on a regular basis

When it comes down to it, investing in data quality is about more than just cleaning up numbers and spreadsheets. Done right, it’ll give you the foundation for more accurate customer profiles that inform stronger marketing campaigns and monetization strategies— so you can unlock new revenue-driving opportunities and meet your bottom line.