Scott and Brian co-founded Eyeris in 1999 to solve the biggest, most complex enterprise analytics problems. Along the way, Eyeris pioneered many of the technical practices that later became vogue in what would eventually be called Big Data - including massively parallel computing, key-value data processing, 4GL automated code gen, lineage metadata, and self-documenting systems. The team has won awards for its groundbreaking work in analytic UX and User-centered Design, introducing many innovations that have become standards across the industry.

But the team's most prescient innovation remains its first - realizing that better enterprise decisions depend upon a Structured Causal Model of the cross-functional dependencies within the firm, leading to the launch in 2000 of the first Enterprise Causal Intelligence platform. The Eyeris platform has since been used by some of the largest telecommunications, financial services, and transportation firms in the world, on problems as diverse as activity-based costing, customer-product profitability, customer-value-based service triage, network capex triage, proactive churn interdiction, next logical product, incentive compensation, and whole-enterprise system simulation.

Nearly 30 years later, Eyeris' pioneering work in Enterprise Causal Intelligence is now more relevant than ever, as thought-leaders widely recognize that Analytic AI cannot deliver on its promise without a good foundation of causal data.