High-quality raw data​

AI doesn’t differentiate between good and bad input data. This renders the stakes exceptionally high when you consider that the AI models themselves are trained using between one and (potentially) infinite data sets. The more accurate the data an organization works with, the more accurate the model prediction will be. The opposite is also true: The more “bad” data that an organization feeds into models, the more those models will generate errors in prediction – errors that can increase exponentially in number as more bad data is absorbed. ​

As a leading industry provider of relevant data sources, NIQ provides its clients with professionally run and monitored panels that offer both quality and coverage. The result? Our clients work from a data repository, or data lake, that is more accurate than any other. For example, many brands claim a 50–60% level of data accuracy. NIQ, by comparison, delivers 90% accuracy. Anything less accurate simply isn’t sufficient for superior outcomes, and any organization leveraging inferior data sets is at a competitive risk. 

Common key data sets​

Procuring clean, raw data is just the start. Data sets are only beneficial if they can use a common language to “talk” to each other. How exactly does data communicate? Through common “key” data sets, like EANs or UPCs. Think of these common data sets as the glue that binds tens, hundreds, thousands, or even millions of previously disparate pieces of data together. As soon as data sets can talk, they can start to deliver insights and modeling.

Once a common key data set in in place, the real magic can happen through a process called cross-coding, which enables organizations to build new models that surface new insights that pave the way for unparalleled leaps in analysis – all, notably, with minimal human intervention and at a pace that was previously unfathomable.​

Enter NIQ. Not only do we offer the highest quality, cleanest data sets in the industry, our data also serves as the aforementioned glue between previously unconnected data sets. By marrying data from NIQ to internal data from manufacturers and retailers, our clients get the full view of what’s happening now alongside a predictive model to anticipate what’s going to happen in the future.

These insights propel both retailers and manufacturers beyond the illusion that data only ever reveals a singular truth about a given scenario, enabling them to successfully slice the data in myriad ways based on their specific use cases for the data sets. Instead of walking away with one dry prediction for what to do, retailers and manufacturers gain nuanced, flexible insights that reveal multiple possible realities. ​

Imagine what your organization could do if it were able to marry 100+ data sets together – in near real time – to drive analysis and forecasting.​

Quality control supported by human intelligence​

The standard data science project includes the stages of scoping, collecting data, training the model, and deploying the model in production. Nascent AI partners stop there, relying on the data and the modeling to be accurate. This is problematic for two reasons. First, it ignores the biggest risk currently associated with generative AI: data hallucinations. This phenomenon is the result of AI models’ occasional tendency to identify otherwise imperceptible patterns in data and to incorporate those patterns into their outputs, which can lead to inaccurate conclusions. Second, it can lead to AI algorithms that fail to account for subtle, but critical, nuances between data sets. Any time data is brought together from different sources, there is the risk of delivering misleading outputs – outputs that organizations nevertheless stake their future planning on.​

These are not risks we recommend taking, which is why NIQ takes a different approach – one that applies human intelligence to the quality control protocols for modeling and analysis. With expert humans in the loop at strategic junctures, it’s possible to mitigate those risks and greatly decrease any of their potential negative impacts. ​

What does this look like in practice? NIQ brings in Machine Learning Operations (MLOps) specialists who develop their own methods and algorithms to audit the quality of the AI model’s output, essentially finding ways to separate the “good” predictions from the “bad” ones. They also ensure the model is working optimally amid changing demands by monitoring algorithms’ metrics, improving performance, and establishing best practices – ultimately resulting in more systematic, repeatable, and well-maintained ML and AI projects.

For example, as an organization adds different data from new points of sale or customer interfaces, or as the business environment grows, MLOps specialists evaluate the results and take a proactive approach to the information as it is reported on. When those using the analytics query the results or notice any misbehavior, MLOps specialists identify the source of the problem and remedy it. This becomes more important as organizations ingest larger volumes of data to fuel business intelligence insights. And this is why NIQ has moved from being a traditional market research company to being a trusted provider of prescriptive data analytics, powered by innovative technology.