Data Platform for TAMPs

The TAMP Data Challenge

Turnkey Asset Management Platforms operate at a fundamentally different scale and complexity than traditional RIA firms. Where an RIA manages a single book of relationships, a TAMP operates as a platform—serving hundreds of advisor relationships, each with their own clients, custodian preferences, model allocations, billing arrangements, and reporting requirements.

Scale That Breaks Standard Reporting

A mid-sized TAMP managing $5 billion across 200 advisory relationships and 40,000 client accounts generates a volume of data that overwhelms standard portfolio reporting tools. Those tools were designed for individual firms, not multi-tenant platforms. The result is a patchwork of custodian statements, model performance exports, spreadsheet-based billing reconciliation, and advisor-specific reports assembled manually by back-office teams.

Multi-Custodian Complexity

TAMPs typically operate across multiple custodians—Schwab, Fidelity, Pershing, and others—because each advisor network has existing custodial relationships. Every custodian delivers data in proprietary formats, on different schedules, with different levels of completeness. Consolidating position and transaction data into a unified view requires either expensive custom integrations or manual reconciliation across custodian portals.

Sub-Advisory Relationships Multiply Complexity

When a TAMP sources model portfolios from multiple sub-advisors, the data complexity compounds. Performance attribution must trace from individual client accounts up through advisor relationships to the model composite, then compare across sub-advisory relationships. Billing must flow correctly through the fee waterfall—TAMP platform fees, sub-advisor model fees, and advisor fees—reconciling against custodian statements that aggregate these charges in ways that obscure the underlying calculations.

Advisor-Level Reporting Demands

Each advisor expects to see their own clients' data presented clearly, often under their own brand. The TAMP must produce hundreds of variations of the same underlying report—customized for each advisor's firm name, logo, and client roster—while maintaining a single accurate source of underlying data. Without a purpose-built data platform, this reporting burden falls on back-office staff manually customizing reports for each relationship.

What TAMPs Need from Their Data

A TAMP's data requirements span the full lifecycle of the advisor relationship—from onboarding and model allocation through ongoing performance monitoring, billing, and compliance. The data platform must serve multiple stakeholders simultaneously: TAMP operators who need enterprise-wide visibility, advisors who need their own book-level analytics, and compliance teams who need documentation across all relationships.

Model Performance Attribution Across Advisors

TAMP operators need to know whether their model portfolios are performing consistently across the advisor network. An advisor who has implementation drift—substituting securities or deviating from model weights—will show performance that diverges from the composite. The data platform must attribute performance at the account level, roll it up to model composites, and flag advisors whose accounts deviate materially from the model's intended exposure.

Sub-Advisory Billing Reconciliation

Billing accuracy is a critical operational function for TAMPs. The platform must calculate billable AUM at the account level, apply the correct fee schedule based on the advisor's model and tier, sum fees across all accounts in each advisor relationship, and reconcile against custodian billing records. Discrepancies between calculated and collected fees—whether from incorrect AUM balances, fee schedule misapplication, or custodian billing errors—must surface automatically.

Advisor Adoption Analytics

Understanding how advisors are using the TAMP's platform is essential for growth. Which advisors are allocating the most AUM to each model? Which ones have stalled in onboarding? Where is there opportunity to expand usage within an existing advisor relationship? These questions require analytics that aggregate across all advisor accounts—something no individual custodian report can provide.

Compliance Monitoring Across Multiple Relationships

TAMP compliance teams must monitor adherence to investment policy statements, suitability requirements, and regulatory thresholds across every advisor relationship and every underlying client account. Automated monitoring flags accounts that drift outside permitted ranges, generates documentation for regulatory examinations, and maintains audit trails across the full network of relationships.

White-Label Reporting for Advisors

Advisors expect to receive their performance and account data under their own brand—not under the TAMP's. The data platform must support advisor-branded report templates that draw from a single, authoritative data source. Each advisor sees only their own clients' data, formatted to their specifications, without any manual customization effort from the TAMP's back office.

Client-Level Analytics Across the Full Book

TAMP leadership needs visibility across the entire book—not just advisor-level aggregates. Client-level analytics reveal concentration risk in specific securities or strategies, identify underperforming model implementations, surface client churn signals before assets leave the platform, and quantify the TAMP's total economic exposure to any individual sub-advisor's model performance.

How TAMP Data Differs from RIA Data

TAMPs and RIAs both work with investment data, but the architectural requirements of a TAMP data platform are fundamentally different from what an RIA data platform requires. Understanding these differences explains why RIA-designed tools consistently fail when TAMPs attempt to use them at scale.

The multi-tenant requirement is the most consequential architectural difference. An RIA's data platform stores and analyzes one firm's data. A TAMP's data platform must store data for hundreds of advisor relationships in a way that maintains strict data isolation—each advisor can see only their accounts—while enabling TAMP operators to run analytics across the full dataset. Row-level security, tenant-aware data models, and role-based access controls are not optional features for a TAMP platform; they are foundational requirements.

Model-level analytics add another layer of complexity absent in RIA implementations. An RIA measures account performance against a benchmark. A TAMP must construct and maintain model composites, calculate time-weighted returns at the composite level, attribute performance across every account assigned to the model, and produce GIPS-compliant composite statistics—all while accounts are continuously flowing in and out of the composite as advisors onboard new clients.

Core Capabilities of a TAMP Data Platform

A purpose-built TAMP data platform delivers six core capabilities that collectively transform how the TAMP operates its advisor network, manages model portfolios, and satisfies regulatory requirements.

Snowflake Data Sharing for TAMPs: The Killer Feature

Of all the capabilities a TAMP data platform delivers, Snowflake secure data sharing stands apart as the feature that most fundamentally changes the advisor relationship. It solves the problem that has plagued TAMP data distribution since the beginning: how do you give each advisor accurate, timely access to their data without creating dozens of separate data copies that immediately diverge from each other?

Why This Matters for TAMP Operations

Before data sharing, TAMP back offices spent enormous effort producing and distributing reports. PDF performance reports went out monthly. Data extracts went out weekly. Every time an advisor called with a question, someone had to pull a report. The data was always at least a few days stale, and different advisors often had different versions of the same data depending on when they last received an export.

With Snowflake data sharing, sophisticated advisors—those with their own analytics teams or BI tools—connect directly to their share and build their own dashboards, models, and reports against live TAMP data. They stop calling with data questions because they have direct access. They stop receiving monthly PDFs because they can query any metric at any time. The TAMP's back office is freed from report production and focused on higher-value work.

Sub-Advisory Data Sharing

The same mechanism applies to sub-advisors managing model portfolios for the TAMP. A sub-advisor overseeing five model strategies can receive a Snowflake share containing the performance, attribution, and composite data for those five models—updated daily, directly queryable from their own systems. They can monitor composite construction, verify attribution calculations, and pull data for their own investment committee reporting without requesting extracts from the TAMP.

Maintaining the Single Source of Truth

The fundamental advantage of Snowflake data sharing is that it distributes data access without distributing data custody. The TAMP never loses control of the authoritative dataset. Every advisor, sub-advisor, and internal team works from the same underlying data. Discrepancies between what different parties see—a chronic problem with CSV-based data distribution—become impossible by design.

The Milemarker Approach to TAMP Data

Milemarker was built on the premise that wealth management data infrastructure should not require years of custom development or army-sized engineering teams to implement. For TAMPs, this means a pre-built data model designed for multi-tenant TAMP architecture, an integration library that covers every custodian and portfolio system a TAMP is likely to use, and a Snowflake-native foundation that makes data sharing a configuration decision rather than an engineering project.

130+ Pre-Built Integrations

Milemarker's integration library covers the full ecosystem a TAMP operates within: custodians (Schwab, Fidelity, Pershing, TD Ameritrade, Interactive Brokers, and others), portfolio management systems (Orion, Black Diamond, Tamarac, Envestnet, Addepar), model marketplaces, CRM platforms, and compliance systems. Each integration is maintained, monitored, and updated by Milemarker—not by the TAMP's internal engineering team. When custodians change their data formats or API specifications, Milemarker absorbs that change so the TAMP doesn't have to.

Snowflake-Native with Data Sharing Built In

Milemarker's architecture is Snowflake-native. All data lands in a Snowflake data warehouse that the TAMP controls. There is no proprietary black-box data store. The TAMP can query, transform, and extend the data using standard SQL. Snowflake data sharing is a first-class capability, not an afterthought—TAMPs can configure advisor-level shares and sub-advisor shares through the Milemarker platform without custom development.

Pre-Built TAMP Data Model

Rather than starting from raw custodian data and building a data model from scratch—a process that typically takes 12 to 18 months for a bespoke implementation—Milemarker provides a pre-built TAMP data model. This model includes the multi-tenant advisor relationship structure, the model portfolio and composite architecture, the billing waterfall schema, and the compliance monitoring framework. TAMPs implement against a tested, production-proven data model rather than designing one from scratch.

Frequently Asked Questions