Instead of breaking your dataset apart, our AI preserves nuance by automatically analyzing every possible data subgroup.
In a complex dataset, you could find a single pattern to sum up the relationship between points. But that pattern won’t be nuanced. Take weight and health as an example. Across the population, greater weight might correlate with lower health. But add in another feature like age, and the pattern no longer holds. (For instance, heavier newborns are generally healthier.)
The more columns a dataset has, the more relationships between features there are to discover. A complex dataset contains a huge number of these nuanced interactions. When you analyze data, you seek these patterns because a human would be unlikely to know them intuitively. This is how you find impactful ideas you haven’t tried yet.
Most data science techniques don’t work well on a large number of columns. With these techniques, each column you add to the analysis increases processing time while making it harder to find meaningful patterns.. That’s why data scientists must start by reducing the number of features being analyzed. This process breaks down complex datasets and severs important connections between features.
Having to simplify the dataset makes it easy to miss patterns. After all, if a pattern isn’t in the cut of data you’ve chosen to look at, you won’t find it—and analyzing each subset on its own would be impossibly time-consuming.
We don’t lead with guesswork. We use unsupervised learning to discover the underlying structure of a dataset. This allows us to find all the patterns in your data.
Our methodology offers discovery in the truest sense: we find insights others can’t. This is crucial. After all, patterns affecting just a sliver of your consumers can represent major emerging trends.