Brands, publishers, MarTech vendors, and beyond recently gathered in NYC for Advertising Week and swapped ideas on the future of marketing and advertising. The overarching message from many brands was one we’ve heard before: First-party data is like gold, especially for personalization. But it takes more than “owning” the data to make it valuable. Scale and accuracy are critical. And the rapid adoption of artificial intelligence can give brands the scale they need to reach customers across channels and devices.   

However, the best technology in the world can’t deliver better experiences and outcomes with inaccurate, messy, and disorganized first-party data. Simply put, AI is only as good as the data it’s fed, and for many enterprises, that data isn’t up to par.

As Forrester analysts aptly point out, “Regardless of which technical path your business pursues, the primary limiting factor you’ll face today is your own data quality. The old adage ‘garbage in, garbage out’ is even more true for genAI.” Without a well-organized and accurate data foundation, AI initiatives will struggle to perform at scale, leading to sloppy customer interactions and wasted resources.

Data Is the Foundation of AI 

When first-party data is clean, comprehensive, and correctly labeled, it enables AI models to learn patterns, make predictions, and generate reliable insights. This leads to better marketing thanks to more relevant customer experiences. And because data is so foundational to AI-powered decisions, companies that use fresh, deterministic data can safeguard against biased, unethical outcomes caused by stale or inaccurate data, undermining trust with customers and partners.

When a brand’s AI model has been trained on inaccurate customer data, it can lead to targeted ads that are irrelevant or even offensive, resulting in alienating customers instead of engaging them. An engagement as simple as offering a mortgage to someone who just lost their job can damage brand equity. Business outcomes can also be impacted immediately – what if a brand’s AI-generated creative contains an offer that isn’t profitable to the business based on an expired promotion or an outdated customer attribute? That kind of mistake can be costly. Prioritizing data quality is critical to avoiding these pitfalls. 

Create Checks and Balances with AI-Human Collaboration

AI offers a powerful way to centralize data and activate it cohesively across channels, including cross-departmental data. Organizations should implement automated processes to refresh data in real time and verify its accuracy to maintain high-quality inputs. Once first-party data is organized, accurate, and complete, it’s important to implement a plan for continuous monitoring and governance of data quality. Effective data hygiene involves regular audits, monitoring for discrepancies, and the ongoing refinement of data collection processes, which is where humans play a critical role. By having human employees work with AI, brands can ensure data remains accurate, secure, and compliant with evolving regulations.

This ability to maintain data security and compliance is not just about adhering to regulations but also about building trust with consumers. Brands that fail to protect their customers’ data will struggle to build loyalty, no matter how advanced their AI capabilities may be.

Reaping the Benefits

High-quality first-party data unlocks the true potential of AI, transforming how brands interact with customers and optimizing their marketing efforts. Clean, well-managed data allows AI models to accurately predict customer needs, leading to more personalized, relevant, and empathetic marketing campaigns. Marketers can anticipate what customers want before they even ask, craft experiences that are seamless and consistent, and add value across the customer journey.

Ultimately, investing in data quality pays off. It enables businesses to scale their AI initiatives effectively, reduces the risk of missteps, helps foster a deeper connection with customers, and gives organizations a clear view of where they should focus their resources. By laying a solid data foundation, companies can harness the power of AI to deliver innovative, customer-centric solutions that drive long-term success.