Gabe
Gotthard

In 1999, when most enterprise IT conversations were still about raw capacity, Gabe Gotthard was already arguing about something different. At a startup called 3ParData, he coined a phrase - "utility storage" - that reframed the entire conversation from hardware specs to business outcomes. HP eventually agreed with that framing, to the tune of $2.5 billion.

That instinct for category creation - identifying the thing nobody has named yet, then building the market around it - has defined Gotthard's career. A Master of Engineering in Solid State Devices from Cornell University (1983) gave him the technical fluency. Fifteen years at Hewlett-Packard across sales, systems engineering, and product development gave him the commercial translation layer. And a run of advisor roles at Silicon Valley companies - ONStor, AdapTec, Panasas - sharpened his eye for which bets were worth making.

Then, around 2011, a Polish startup caught his attention. Founded by three computer scientists at Wrocław University of Technology who were frustrated with slow database performance, PiLab S.A. had built something unusual: a hybrid graph-relational database engine that could analyze connections across massive, fragmented datasets without moving the data itself. Gotthard joined as the company's bridge to Silicon Valley and U.S. government markets.

He renamed the company DataWalk. Listed it on the Warsaw Stock Exchange. Then went about winning contracts that Palantir had been holding.

By 2020, DataWalk had been selected by the U.S. Department of Justice's Money Laundering and Asset Recovery Section (MLARS) - displacing Palantir as the incumbent. The win was part of a $500 million DOJ analytics Blanket Purchase Agreement framework. Since then, the U.S. Department of Defense's Office of Inspector General, the USDA, and the U.S. Department of State have all followed.

The pitch is precise: Palantir-level investigative capability at 75% lower cost, deployed in 19 weeks versus Palantir's 12-18 months. No data scientists required. Analysts using no-code graph queries can investigate cryptocurrency flows, map criminal networks, and run multi-hop transaction traces without writing a single line of code.

Most analytical tools treat data as rows and columns. DataWalk treats data as a network of relationships. When a bank needs to detect a money laundering ring - not a single suspicious transaction, but a network of shell companies, beneficial owners, correspondent banks, and cryptocurrency wallets spanning 12 jurisdictions - row-and-column thinking fails. Graph thinking finds it.

DataWalk ingests data from disparate sources - core banking systems, sanctions databases, blockchain explorers, government records, internal CRMs - and resolves it into a unified knowledge graph without moving or copying the underlying data. Analysts then run visual queries: show me every entity connected to this account within three hops, filtered by transaction volume above $50,000, in the past six months.

What makes Gotthard's pitch unusual is the no-code emphasis. Legacy investigation platforms required data science teams to set up and maintain analytical pipelines. DataWalk's environment is designed for financial crime analysts and government investigators - people who understand the domain, not the database. That's the 19-week deployment claim: less implementation, more investigation.