A Profound Improvement in How We Find What Others Miss
The core of Swarmalytics is an approach to data analysis that applies powerful methods of insight, refined over 30 years working with the world's largest institutions, to find what others miss.
Most analytics tools give you answers. Swarmalytics finds the right questions, then works until it locates signal where others see noise.
The latest evolution of that approach is a profound one.
What Changed
What was already a battle-tested system, running 160,000-plus observation datasets across AmEx, UBS, Bank of America, and AARP, has crossed into true swarm intelligence: a fundamentally different way of finding patterns at scale, running 20x faster than before.
Where the original system optimized in sequence, the new architecture runs many agents in parallel, sharing information and converging on solutions through collective behavior. The models run faster. They also find things that sequential processing misses.
Gartner puts true swarm intelligence at 6-plus years ahead of mainstream adoption. We are running it now.
Why It Works
I commissioned one of the first commercialized genetic algorithms at Epsilon in the early 1990s, before most analytics teams knew what one was. The intervening 30 years were spent solving the problems that early implementations stumbled on: overfitting, variable pressure, observation ratios. The result is a system that generalizes rather than memorizes, drawing on 16,000-plus variables per record across 140 million US homes.
The swarm layer was added by Dan, our lead engineer, as a true architectural upgrade. Not a faster version of the same thing. A qualitatively different approach that compounds the pattern recognition we have spent three decades building.
What This Means for Our Clients
Every problem we take on gets the same foundational rigor. The data infrastructure, the variable engineering, the model architecture: all of it is built to be extended, refined, and improved as your data and your questions evolve.
When you bring us a new problem six months into an engagement, we are not rebuilding from scratch. We are building on a foundation designed to carry the weight.
That is the difference between a data project and a data asset.