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The next frontier of AI? Data quality Estimated reading time: 8 minutes

Blog

The next frontier of AI? Data quality

For marketers across industries, there’s no doubt that AI continues to be the topic du jour. Over the past few years, we’ve learned how to use tools like ChatGPT and Gemini to support us with basic tasks like providing meeting summaries, drafting cover letters—and even recommending holiday gifts.

But what makes an AI tool actually worth using? How do we know that the AI tools we build and implement are secure, high-quality and trustworthy products? Setting marketers up for success in AI is all about data inputs and outputs. Have you ever entered an AI prompt that gave you an inaccurate answer or an image that was completely wrong? Then, when you continue to ask the prompt more questions, clarify your request and add details, like magic, the answers become more predictive. What’s happening behind the scenes is telling data models what they’re doing right and wrong to create a stronger feedback loop.

It all starts with a solid foundation of clean, updated and accurate data. All in all, if you fuel your AI engine with inaccurate, poor or outdated data, the results will also be inaccurate, poor and outdated. In other words? Garbage in, garbage out.

As a refresher, there are two main types of AI we typically encounter: predictive AI and generative AI, and how they work is pretty intuitive. Predictive AI, well, predicts, and generative AI generates, but really what it comes down to is how they interact with data.

In general, when we talk about predictive AI, we’re talking about a methodology (like machine learning) that combs through existing data to forecast future outcomes, while generative AI uses existing data to respond to a user’s prompt and create original content.

Now let’s get back to how data quality impacts AI. At Epsilon, we recommend evaluating data quality based on the following 10 key criteria.

10 criteria to assess data quality

1. Privacy

Privacy is the top consideration when it comes to data quality, and remember: It should never be sacrificed for the sake of performance.

Data providers should be able to share how they comply with current legislation and are preparing for new legislation. Always review privacy policies and opt-out language and how companies are handling consumer reporting, data deletion and the handling of sensitive personal information.

AI benefit: Privacy protects individual rights, builds trust in AI systems (which can aid in fair and accountable decision-making) and promotes using AI technology ethically by minimizing the potential for discrimination and manipulation via personal data.

2. Accuracy

To compare and evaluate data accuracy, use a truth-set file that has your full confidence. While we all know that there is no universal truth set for evaluating data quality at scale, there are ways to use smaller-scale options to get a relative read of data accuracy. This ensures you’re connecting to the right people at the right time with the best-possible messages and offers to maximize your marketing dollars.

AI benefit: Accuracy ensures that the AI models can make accurate predictions and decisions. Erroneous or noisy data can lead to incorrect model training, resulting in poor performance and unreliable outcomes.

3. Coverage

The data coverage conversation should determine how much of the target universe is covered, as well as the completeness of each record. High coverage with little depth of useful information won’t serve a purpose—coverage must go beyond name and address to include multiple channels. This translates to consistently higher identification rates across devices for more effective omnichannel marketing.

Many providers offer data hygiene and identity-completion solutions that clean and fill holes in customer data by appending or reverse-appending contact information (e.g., address, phone number and email). Make sure you understand average match rates and the quality of the referential data file that’s used. Tradeoffs between coverages and accuracy will happen, so the key to success is balancing, picking a priority and achieving high marks in both.

AI benefit: Carefully curated, clean data can mitigate biases. Incomplete or skewed datasets can lead to biased models, and cleaning can involve ensuring that the dataset is representative and balanced.

4. Granularity

Granularity characterizes the level of detail in a data set. Granular data is broken down into the smallest pieces possible to be more defined and detailed. For example, while a person’s entire address could be in a single field, a more granular approach would be to divide the address into multiple fields like street number, street name, city, state and ZIP code.

One of the advantages of granular data is that it can be aggregated and disassembled to meet the needs of different situations.

AI benefit: When data is well-organized and labeled, it’s easier to trace how decisions are made, which is crucial for explaining AI decisions, particularly in sensitive applications.

5. Timeliness

When it comes to data, timeliness tells us how much time has passed between when the data became available and when the actual event(s) occurred. Generally speaking, recent data is the most useful, so it’s important to understand how often data is refreshed.

Consumers’ attributes each have different sensitivity to timeliness, but for data that adds extreme value (such as financial, in-market, propensity to purchase or other economic or activity-influenced attributes), timeliness is even more crucial.

AI benefit: Algorithms can process data free of redundancies, duplicates and irrelevant information faster with less computational cost.

6. Predictive power

Predictive power is the cornerstone of the data-quality evaluation process and is directly associated with data performance. Understanding what data types lead to successful interactions—or what data would most likely generate specific activities—is the focus of this assessment and frequently requires use of more advanced data and analytics. Well-balanced models with data elements reflecting depth, breadth, variety and uniqueness typically drive the best performance.

AI benefit: Clean data plays a critical role in building models that are able to effectively generalize new, unseen data. If the training data contains irrelevant or misleading patterns, the model might fit the noise instead of the underlying trends, affecting its ability to generalize.

7. Consistency

Consistency requires that information or certain attributes will exist or be accurate in each observation. For example, if a data solution for an insurance policyholder requires knowing their mortgage value, age and house’s square footage, this data must be consistently available. Consistency is key for modeling solutions that require variable stability.

AI benefit: Uniformity across datasets leads to consistent AI model performance and predictions by eliminating discrepancies and variabilities that could otherwise hinder the decision-making progress.

8. Transparency

Data transparency is becoming more important, especially for certain industries. The Interactive Advertising Bureau (IAB) Tech Lab has partnered with leading associations and companies to create an industry standard, a Data Transparency Label. Similar to an nutrition label, it tells marketers what’s inside the data segments they buy, providing details on source, collection, segmentation criteria, recency and cleansing. It’s intended to give every marketer, agency, data provider and publisher a transparent view of syndicated audience segments.

AI benefit: Structured and well-organized data allows AI models to become more transparent and interpretable, allowing stakeholders to understand how decisions are made and enhancing model explainability. It is highly recommended that real people should monitor any AI outputs in a supervised environment.

9. Omnichannel activation

Data needs to be available for use across channels. This includes traditional channels (like direct mail and email) and all major digital platforms (including DSPs, DMPs, social networks and connected TV). Using a consistent identity for each consumer across all their online and offline channels drives a consistent experience.

You should understand what ID graph, matching methodology and partners your brand is using for data activation and identity resolution across different channels and devices. Data should hold up across all of them with scale and accuracy. Keep in mind that both your audience definition and activation channels may be different for upper-funnel awareness versus lower-funnel conversion.

AI benefit: Facilitates seamless integration and activation across multiple channels, enabling AI systems to deliver personalized and coherent experiences to users, regardless of the platform or touchpoint.

10. Usefulness

Usefulness assures that data achieves business goals and delivers value. The only way to gauge it is to test the data and see if it works. Ideally, test it how you plan to execute it.

For specific campaigns, assembling a valid test is critical, so be mindful of the number of variables so you can isolate the data’s performance. It’s an opportunity to revisit and optimize the data types and variables for future campaigns and see if something new or different can improve performance. Qualitative feedback from users is also important to ensure all users are maximizing their data usage and the knowledge the data provides.

AI benefit: By removing noise and irrelevant information, clean data improves the relevance and quality of insights generated by AI, making the outputs more actionable and valuable for decision-making and strategic planning.

Learn how to improve your data quality so you can take your AI tools to the next level.

Data