Reports Blog

Data Stewardship & Market Signal Review

Written by Gui Costin, Founder, CEO | Aug 13, 2026, 2:45:08 PM

The Network Found Its Feet

We thought we had the top financial advisors covered. Customer feedback said otherwise. This is the story of how Dakota's data team built a best-in-class FA database from the ground up.

Chapter One — The Starting Line

We thought we had it covered. Our customers told us otherwise.

"We realized that this was a very important fundraising channel for our customers, one that we thought we had the top financial advisors and that was enough. But clearly, upon further inspection and feedback from our customers, we didn't. This is the story of how our data team set out and built a best-in-class financial advisor database."
— Gui Costin, Founder & CEO, Dakota

In February 2025, Dakota Marketplace held 18,423 individual financial advisor contacts and 5,659 wirehouse and broker "Team" accounts, a real foundation and, by most measures, a respectable starting point. But respectable wasn't the bar. For a firm whose entire differentiator is global coverage across every LP channel, a thin FA layer was the most visible gap in the map, and customer feedback made that gap impossible to ignore.

Family offices, RIAs, pensions, endowments, insurance general accounts, sovereign funds. Dakota had built depth everywhere else. The individual financial advisor and FA team layer was the next frontier, and the data team set out to build it the way Dakota builds everything: complete, accurate, and update-ready from day one, not scraped once and left to rot.

18,423 — Individual FAs, Feb 2025
5,659 — FA Team Accounts, Feb 2025

What happened next wasn't a slow, organic trickle. The monthly record reads like two distinct construction projects, run back to back, each with its own timeline and its own reason for being. This is the story of how Dakota closed the gap.

Chapter Two — Two Waves, One Curve

The individual FA database grew 14.3x, mostly in two months.

Individual advisor contacts sat essentially flat near 17,000 through the first quarter of 2025. Then the curve broke twice: once in April 2025, and again in October 2025.

April 2025 (+59,082) and October 2025 (+74,798) together account for 133,880 records, roughly two-thirds of the entire FA population Dakota holds today. Between those two waves and after them, the base kept climbing on a steadier, more organic slope: research-driven additions, client requests, and the ordinary churn of an actively maintained channel, running from November 2025 through the current month at a pace of a few hundred to a few thousand records per month.

By August 2026, the individual FA layer stood at 264,245 contacts: a scale that puts Dakota's advisor coverage in the same conversation as its family office, RIA, and institutional datasets, built for completeness first, not assembled opportunistically.

Chapter Three — The Team Layer Catches Up

FA team accounts stayed quiet for a year, then ramped hard, about twelve months behind the individuals.

Wirehouse and broker "Team" accounts moved only gradually from December 2024 through March 2026, drifting from 5,478 to 7,193. Then, starting in April 2026, the team layer began its own build-out.

From April 2026 (+5,962) through July 2026 (+2,430), team accounts nearly tripled in four months (7,193 to 22,405) before settling into a steadier pace in August. The sequencing tells its own story: individuals first, so every advisor is captured and correctly attributed; teams second, so those same advisors are properly rolled up into the practice groups they actually work in. It's the same discipline Dakota applies everywhere in its data model: get the atomic record right, then build the structure around it.

Why This Order Matters

A financial advisor without a correctly linked team record is a lead with no context. Sequencing the individual build first and the team roll-up second means every team account added from April 2026 onward arrived with its member advisors already resolved, not bolted on after the fact.

Chapter Four— Where We Stand

From a starting roster to a channel-complete FA database.

Eighteen months of deliberate, sequenced ingestion turned a modest FA presence into one of the largest independently maintained advisor datasets built specifically for fundraisers.

The ratio is worth sitting with: roughly twelve individual advisors for every team account, which tracks with how wirehouse and broker practices are actually structured. Most teams carry several advisors, and Dakota's data now reflects that reality at scale instead of approximating it.

Two ingestion waves and one team ramp turned an 18,000-contact starting point into a quarter-million-record advisor channel, without sacrificing the linkage between an advisor and the team they belong to.

Chapter Five — Why It Matters

This is the moat, not just the metric.

The growth curve is impressive on its own. What makes it strategically meaningful is what it does to Dakota's core differentiators.

  • Global coverage, no more blind spots. Dakota's LP database has always spanned every channel and every jurisdiction by design: family offices, RIAs, pensions, endowments, insurance, sovereign wealth. The FA and FA-team layer was the remaining gap next to Fintrx's US-only RIA/FA focus and Preqin's near-total absence of advisor depth. That gap is now closed at meaningful scale.
  • Freshness compounds. A quarter-million records captured once and left static would be a liability, not an asset. Dakota's daily-update engine is what turns this load into a durable advantage rather than a one-time data dump that decays the way competitors' datasets do.
  • Built by fundraisers, for fundraisers. The sequencing (individuals resolved first, teams correctly rolled up second) reflects how a practitioner thinks about a territory, not how a data aggregator thinks about a spreadsheet.
  • The Claude Connector gets sharper. Every query run through the Dakota Marketplace connector now draws on a materially deeper advisor graph: better answers on team composition, coverage gaps, and territory planning, with no extra work required from the user.

The Bottom Line

Competitors chose narrow paths: Fintrx stayed US-only, Preqin skipped the advisor channel almost entirely, With Intelligence stayed Europe-and-news-focused. Dakota chose completeness. Eighteen months of disciplined ingestion just proved that choice can scale 14.3x on the individual side and 4.0x on the team side without breaking the data model that makes it useful.