The business of data, from the inside.

01

Zero to one go-to-market

You have a real product and no repeatable way to sell it. I have done this from a standing start four times, beginning with Cleveland Research Company in 2006. The work is unglamorous: positioning a technical buyer will respect, an ICP list that is actually reachable, and a pitch that survives the first twenty conversations.

02

Data monetization and partnerships

You are sitting on data with commercial value and no clear view of what it is worth or who buys it. I built and ran that operating model from scratch at 90 West Data, with distribution through AWS, IEX, BattleFin, Eagle Alpha, Neudata, Snowflake, Crux, Exabel, Maiden Century, CloudQuant, Neuravest, Datarade and SS&C.

03

Getting in front of the right people

The hard part is rarely the product. It is knowing who actually buys, how they buy, and what earns a meeting. Sometimes my client is the one selling the data. Sometimes it is the one trying to find the right data, or the right partner. I have been at both ends of that call.

The buyer has broadened a long way past the hedge fund, and the list of people worth knowing broadened with it. Data owners, corporate buyers, vendors, operators, investors. I make introductions when they are genuinely useful to both sides, and I decline when they are not. This is not a lead list. It is judgment about who belongs in a room together.

04

Alternative data evaluation

You are about to spend six figures on a dataset and need to know whether it is worth it. I screen the vendor landscape, pressure-test the claims, and run the commercial diligence before you sign. Writing Alternative Data Weekly since 2020 means I have seen more vendor pitches than most buyers ever will, and the index on this site is the paper trail. When the answer needs real machine learning rather than judgment, I bring in a specialist evaluation partner.

05

AI-native operations

You know AI should be carrying more of your team's work, and you have no working example to copy. I do. I run my entire operation on an AI system I built: memory that compounds, automated research, competitive intelligence, weekly publishing. An engagement starts with your document-driven, rules-bound workflows, the ones whose output can be verified, and ends with your team running a system like mine. I lead the engagement; a build partner I trust does the heavy engineering with me.

06

Data discovery and readiness

You cannot price, sell, or even govern data you have not inventoried, and most companies do not actually know what they hold. It sits in warehouses, CRMs, file shares and aging databases with no map. I help you build the map, judge what is commercially valuable, and make it ready for the monetization work above. Specialist platforms handle the crawl; I handle what it means commercially.