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For modern marketers, data forms the foundation of every decision, from targeting the right audience to allocating budget for channels.
But what if the data you are using is inaccurate or incomplete?
Unsurprisingly, inaccurate or incomplete data leads to misleading insights, poor decisions, and, ultimately, wasted resources. In fact, Gartner estimates that poor data quality costs organizations an average of $12.9 million annually.
It’s simple: data quality is an essential foundation for any meaningful insights and decision-making.
In this guide, we explore six key dimensions of data quality, and explain how marketers can build a framework to ensure the data they are working with is accurate, complete, and devoid of any errors that might mislead your insights and analysis.
Data quality refers to the overall health and reliability of your marketing data. This means making sure your data is accurate, complete, consistent, unique, timely, and valid. When your data meets these standards, it becomes a trusted foundation for making informed decisions, running effective campaigns, and uncovering key insights.
On the other hand, poor-quality data can lead to costly mistakes—like sending promotions to outdated contacts or making critical strategic choices based on flawed information. These errors can damage customer trust, inflate budgets, and undermine the performance of your marketing efforts.
Simply put, data quality is the gold standard for ensuring that your data is fit for purpose. It’s what guarantees that your marketing data is reliable enough to act on with confidence.
At Adverity, we measure data accuracy using six specific dimensions based on the official data quality standards detailed in ISO 25012 and DAMA-DMBOK.
Together, these dimensions provide a simple and structured approach to evaluating our customers’ data integrity, ensuring that it can be trusted for decision-making.
With that in mind, here’s an overview of what our six data quality dimensions are, why they matter, and how to get them right.
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Data accuracy refers to how closely data reflects real-world events, ensuring it matches reality without errors or distortions. Data that closely reflects the real-world events it represents is accurate.
Accurate data provides a true representation of metrics such as customer behavior, sales performance, and campaign outcomes. It should be fairly obvious that when data is inaccurate, marketers might target the wrong audience, allocate budgets inefficiently, or draw incorrect conclusions about campaign performance. This can result in lost revenue, decreased customer trust, and reduced marketing ROI. Accuracy ensures that marketers have a reliable foundation for analysis and decision-making.
To maintain data accuracy, marketers should implement regular data validation and cleansing practices. This involves checking for discrepancies, removing errors, and cross-referencing data with trusted sources. Tools that provide automated data cleansing can help maintain a high standard of accuracy by flagging anomalies.
Read more about ensuring accuracy in our blog The 2025 Guide to Data Accuracy
Data completeness measures whether all necessary data is present. Are there any missing data sources? Is all the data from a specific data source included? Are all the different metrics present?
Complete data gives a full picture of marketing performance. Incomplete data can cause marketers to miss crucial insights that would help in better targeting and segmentation, ultimately weakening campaign outcomes and eroding trust in data quality. When missing data goes unnoticed, it can distort results, leading to bad decisions, wasted resources, and ineffective strategies. Imagine trying to simply measure your overall campaign ROI based on incomplete spend data. It’s impossible. That ROI figure will inevitably be incorrect.
Completeness can be ensured through the implementation of validation checks at data entry points. Marketers should also use data integration tools that flag missing fields, enabling quick corrections. Regular audits of the dataset can also highlight gaps that need to be filled.
Explore more in our blog: What is a Completeness Check in Data Validation?
Data consistency means ensuring that all your data is uniformly formatted and synchronized across different systems and platforms. This means all dates, currencies, naming conventions, and measurement units are standardized—so that information is aligned and comparable regardless of its source.
Consistency is crucial for ensuring that marketers can rely on data no matter which system they’re using. When data is inconsistent, it can cause confusion, errors in reporting, and conflicting insights across platforms, making it difficult for marketers to trust their data and draw reliable conclusions.
For example, if you have all your dates from one data source as [month, day, year] and all your dates in another data source as [day, month, year] you cannot meaningfully compare them. They need to have the same date format otherwise you are comparing apples to oranges. The same goes for currency or any other metric that can be measured differently between data sources.
Achieving consistency requires implementing data governance policies that standardize formats, terminologies, and units across all platforms. Using tools that automate this standardization can greatly help in ensuring consistent data across your systems. At Adverity, for example, we use our data dictionary and data mapping tools to achieve this.
Explore more in our blog: What is data consistency and how to manage it?
Data uniqueness simply means that your data should not contain duplicate entries. This might refer to duplicate customers, duplicated sales data, duplicated spend data. For example, let's say someone enters a customer’s name as John Doe and another person enters their name as J. Doe. Now you have two entries for the same person.
Needless to say, duplicated data can cause a whole host of problems. For instance, duplicate customer records can result in multiple communications to the same person, confused orders, and incomplete customer histories all of which damage your relationship with your customers. Duplicate data can also artificially inflate metrics and lead to inaccurate assessments of marketing performance. Additionally, it increases operational costs as redundant data takes up unnecessary storage space.
Using data deduplication tools can help automatically detect and remove duplicate entries. It’s also beneficial to enforce strict data entry rules to minimize the chances of duplicates entering the system in the first place.
For more on how to avoid data duplication, check out our blog: What Is Data Duplication? Examples, Causes, and Best Practice: 2024 Guide.
Timeliness refers to how up-to-date data is. Data that’s current enough to support quick, informed decisions is considered timely. Usually this means fetching data at least once a day.
In marketing, where customer preferences and trends shift quickly, having up-to-date data is essential for timely decision-making. Timely data allows marketers to respond quickly to changes, optimize campaigns in real-time, and improve targeting. On the flip side, using outdated data can result in missed opportunities and wasted spend by targeting customers with irrelevant offers or basing strategies on insights that are no longer relevant.
Implementing automated data pipelines ensures that data is constantly updated and relevant. Marketers should also establish schedules for regular data updates and set policies that determine acceptable timeframes for when data should be refreshed.
Read more about the importance of timeliness in our blog What is Data Timeliness?
Validity refers to whether data conforms to the required format, structure, or rules. Data that adheres to predefined business rules, such as correct date formats or mandatory fields, is considered valid.
Validity ensures that data can be reliably used without additional manipulation or correction. Invalid data can lead to operational inefficiencies and errors that reduce the effectiveness of campaigns. For example, if a dataset contains invalid email addresses, it can result in failed communication efforts, affecting the efficiency of marketing campaigns.
Marketers can maintain data validity by setting up validation rules within their data entry systems. These rules should enforce consistency in formats, required fields, and other predefined standards. Regular data audits can help identify and correct invalid data entries.
Learn more about maintaining validity in our blog What is Data Validity? How to Maintain Accurate and Reliable Data
So, that’s what good quality data should be but, if you’re working with poor quality data, you’ll want to know what steps you can take to fix it. Here’s a step-by-step guide for what you can start doing now.
You can’t fix what you don’t know is broken. So, start by figuring out where the biggest issues are in your data, how much is incomplete, how much is inconsistent, what are the key data sources you are experiencing problems with? Perhaps you have different teams using different databases leading to consistency and duplication issues between departments.
Before diving in, it’s a good idea to get key stakeholders on board. If everyone understands why good data matters and what you’re trying to achieve from the start, you’re much more likely to succeed in the long run. This is also an opportunity to break down any siloes. If different teams are using their own databases, it’s time to get everyone on the same page — with a single source of truth.
Set some data governance rules about how your data should be handled. This means defining formats to ensure consistency, establishing schedules for data fetching, and strict rules for how data is entered. Be sure to make it clear who is responsible for each task too.
Look for tools that can help you validate, clean, and de-duplicate your data before it even hits your system. Better, look for tools that can also automatically notify you if there are any data quality issues. This is a key area that Adverity can help you. If you’d like to learn more, be sure to check out our Data Quality Suite.
Data quality isn’t a one-time fix. It’s something you need to monitor and maintain over time. Obviously this is made much simpler with automated tools and quality monitors but it still requires regular attention and upkeep. And this should also be a team effort. Ensuring data quality is not an individual person’s job but the responsibility of everyone in the business.
Ensuring high-quality data is key to making data driven-marketing decisions. And this is especially crucial in a world where conversational and agentic AI are becoming commonplace tools.
But, as useful as these tools are, AI will have the same problems as you if it’s working with poor-quality data. Ultimately, the most advanced AI in the world can only give you flawed insights and misleading reports if the data it has is flawed and misleading.
So, if you are using, or planning to use, AI to support your marketing efforts, remember; high-quality data isn’t just a "nice to have." It’s the foundation of every successful business.
Nickolay Penchev is VP of Solutions Consulting at Adverity, where he helps global brands solve complex marketing data challenges. With a background in analytics and product strategy, he focuses on turning fragmented data into reliable insights that drive smarter decisions.
Joseph Caston is Director of Solutions Consulting at Adverity. He works closely with enterprise teams to build scalable data workflows, drawing on deep experience in martech integration and analytics operations.
Tom Rennell is the Head of Content & Communications at Adverity where he leads the strategy and execution of all organic, editorial, and external messaging across Adverity’s channels. With almost two decades in content strategy, communications, and storytelling, he’s known for shaping narratives for top-tier organizations like Alibaba and the United Nations.
Lillian Johnson is a Content Manager at Adverity. She spearheads the creation of research reports, long-form editorial, and thought leadership content. As part of the marketing team, she brings a decade's experience developing organic content strategy.