Gautam Mehra – CEO & Co-Founder – consumr.ai powered by ProfitWheel.
Garbage in, garbage out has been a standing principle since the beginning of the compute era. Yet, many enterprises, eager to embrace artificial intelligence, focus too much on the algorithms instead of gathering the right data. The reality is that not all data is created equal. And understanding this is critical to building AI systems that can deliver real business value.
Sources Of Data Powering AI Systems
Let’s start by examining the oldest trick in the marketing research arsenal: claimed survey data. For decades, surveys have been a cornerstone of understanding consumer behavior. But here’s the catch—human nature. Respondents often say what they think they should do rather than what they actually do. In fact, studies suggest that a non-negligible amount of survey responses are influenced by social desirability bias.
So, while surveys might paint a rosy picture of aspirational habits, they’re a poor predictor of real behavior. And when AI models rely on these inputs, they inherit this disconnect.
Then there’s owned data, which comes directly from your operations—loyalty programs, CRM systems and transaction records. This type of data is precise and trustworthy, but it’s also narrow. Think of it as looking at your customers through a pinhole: You see them clearly, but only in a small context. A retailer, for instance, might know a customer’s in-store purchase history but be blind to their online behavior or preferences when shopping with competitors. This lack of completeness can limit the scope and accuracy of your AI-driven insights.
Enter synthetic data—one of the newest darlings of this year. It’s generated to mimic real-world patterns, promising to fill in gaps left by traditional data sources. While this sounds great, think about it for a second, you are getting an AI that hallucinates to hallucinate several thousand times to create a “variety” of data. It’s like creating a random distribution that will meet at the mean and one says that the data is statically valid. It can be valid but totally untrue at the same time.
This brings us to the gold standard: deterministic observed data. This is the data of real actions—what people actually do, not what they claim to do or what we approximate they might do. Whether it’s purchase patterns, digital interactions or footfall data, deterministic observed data provides a concrete and unbiased view of reality.
Why The Business Stakes Are High
As an enterprise, your choice of data sources cannot be about convenience, it needs to be on accuracy. Models trained on incomplete/unreliable data won’t just underperform—they’ll mislead you into the wrong decisions. On the other hand, investing in deterministic observed data ensures you’re building a strong foundation.
While you might think it will require more effort, the long-term benefits of accurate insights and better decision-making are well worth it.
How To Build Your Foundation: A Practical Playbook
Getting deterministic observed data right isn’t just about collecting more data—it’s about building a robust and ethical data strategy.
Here’s how to start:
1. Focus on Privacy: Using cohorts privacy isn’t an afterthought, it is how you must think. Instead of trying to zero in on individuals, focus on aggregated cohort-level data. By analyzing trends at a group level, you can uncover actionable insights while maintaining customer anonymity. This approach safeguards privacy without sacrificing accuracy and actionability.
2. Partner with Experts: Building a deterministic data infrastructure isn’t a solo endeavor. The integration, cleaning and validation required can overwhelm internal teams. Trusted vendors bring specialized expertise, proven frameworks and scalable tools to ensure your data is both reliable and actionable. They help navigate complexity, so your team can focus on using the data rather than wrangling it.
3. Gut Check: Do a proof of concept where you can do a gut check. Choose a use case that you have internal expertise to evaluate, compare outcomes to existing methods and then define success criteria to guide the broader implementation.
The Bigger Picture
The shift to deterministic observed data isn’t just a technical upgrade—it’s a cultural and strategic transformation. In the age of AI, your data strategy is your competitive advantage. Sophisticated algorithms might dazzle, but without high-quality data, they’re like F1 cars in sand dunes.
By prioritizing deterministic observed data, you’re not just investing in better AI—you’re setting your organization up for long-term success. This is the foundation for better insights, smarter decisions and the ability to adapt to an increasingly complex business environment. It’s not just about getting ahead; it’s about staying there.
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