Why your AI strategy is only as good as your data

Contrary to most expectations, in the world of AI, data quality is arguably the most critical factor for success—more important than sophisticated algorithms and computing power. Imagine teaching a child using a textbook filled with errors. They’ll still “learn,” but the knowledge they gain will be flawed and unreliable, leading to misconceptions and mistakes when they try to apply that knowledge in real-world situations. The same principle applies to AI—feed it inaccurate data, and you end up with faulty outputs that can misguide decisions and strategies.

High-quality data is more than just important—it’s essential for AI to truly excel because it directly impacts how AI models are trained, influencing their ability to learn patterns, make accurate predictions, and adapt to new information. In the realm of personalization, where AI can expertly tailor experiences and recommendations to individual preferences, the stakes are even higher. Clean, structured, and continuously refreshed data is the cornerstone of effective AI-powered personalization, enabling businesses to achieve more accurate and reliable results, ultimately enhancing user experiences and driving business goals.

The consequences of bad data

“Bad data” can take many forms. Inaccurate data can stem from human errors during data entry or from outdated information that hasn’t been updated. Incomplete data lacks the necessary information to provide a full picture, leading to gaps in understanding. Lack of standardization makes it difficult to integrate data from different sources, and bias in data can lead to unfair and misleading conclusions.

But no matter how big or small, data quality issues can be disastrous for training AI models and for personalization efforts. The quality of data fed into AI systems directly affects the quality of the outputs, a principle often referred to as “garbage in, garbage out.” When AI is trained on flawed data, the resulting outputs can be erroneous, leading to poor recommendations, ineffective personalization, and ultimately, dissatisfied users.

Let’s say your business uses a personalization engine to recommend products to customers. If the underlying data is inaccurate or incomplete, the recommendations it generates will likely miss the mark. For example, if the data you have stored about your vegan customer is inaccurate, there is nothing training your recommendation algorithms to never suggest meat products and dairy-based items to this user. This mismatch not only frustrates the customer but also reduces their trust in the platform. Businesses relying on these flawed recommendations might see a drop in customer engagement, retention, and revenue as users turn to competitors who offer more relevant and accurate suggestions — after all, according to Khoros, 65% of customers say they have changed to a different brand as a result of a poor experience.

Improving data quality

To mitigate the risks associated with bad data, improving data quality must become a priority. According to Satish Jayanthi of Coalesce, “There are a lot of aspects to data quality. There is accuracy and completeness. Is it relevant? Is it standardized? … All of these have to be taken into account when you’re providing data quality.” To ensure your data is high quality, you need to maintain your databases, guaranteeing the data is correct, up-to-date, and as complete and comprehensive as possible. In addition, you need to make sure the data is relevant to the task at hand, and standardized to integrate data from different sources. Reducing bias is also an important part of creating fair and reliable models. As Jayanthi says, “We are giving away some control to machines, which is why data quality is so important. … It’s not quite a black box, but it’s still something that we are giving away control to. The AI systems will take this data, and depending on how you train the models, you will get your output.”

Another critical but often overlooked aspect of having high-quality data for AI is the need for diverse and comprehensive datasets. Involving a wide range of sources and contributors can help ensure that the data is more representative and inclusive. This diversity helps to reduce bias and improve the reliability and trustworthiness of the datasets, and in turn, the models. As James Zou of Stanford University puts it, “If we want to build datasets that are more representative, reliable, and trustworthy, we need to have broader participation.”

Optimizing your data pipeline

Improving data quality can involve a variety of strategies, such as implementing robust data collection processes, conducting regular data audits, and using advanced tools for data cleaning and standardization. Ongoing assessments are essential for maintaining data quality over time, ensuring that datasets remain accurate and relevant.

Maintaining data quality can be an expensive and tedious task, but fortunately, there are other options. Qloo has invested over 12 years in building a robust dataset that spans a vast array of lifestyle entities and consumer behaviors. Our sophisticated solution is powered by an extensive repository of more than 575 million cultural touchpoints and trillions of behavioral signals. These extensive datasets power Taste AI, providing unparalleled insights and enabling highly accurate personalization.

Qloo collects data from a wide range of diverse sources, ensuring that the data that trains our models is comprehensive and representative. Our pipeline includes anonymized first-party data, exclusive partnerships, and rigorously selected third-party data, yielding a dataset of trillions of real-time data points. Our data is meticulously standardized, regularly updated, and always quality-checked to maintain its accuracy and relevance. Our commitment to the depth and quality of our data ensures that our AI models deliver better outputs, helping businesses enhance their personalization strategies and achieve superior results.

Ready to see how Qloo’s comprehensive data and AI-driven insights can transform your personalization strategy? Schedule a demo today and discover the power of privacy-centric, high-quality data in driving success for your business.