Most advisory firms don't fail at technology because they picked bad tools. They fail because they picked great tools in isolation — and those tools don't talk to each other. This guide walks you through a six-step framework for evaluating wealthtech holistically, so your stack works as a system rather than a collection of disconnected applications.
The Evaluation Mistake Most Firms Make
When a firm decides it needs a new CRM, the evaluation process typically begins by searching for "the best CRM for RIAs." When it needs a new portfolio management system, it searches for "the best portfolio software." Each selection process runs independently, optimizing for its own category.
The result: great individual products that create a fragmented data environment. The CRM holds contact history but can't see portfolio performance. The portfolio system tracks positions but doesn't know which advisor owns the relationship. Compliance data sits in a third system. Planning data in a fourth. Each tool is excellent at its job — and invisible to every other tool in the stack.
The six steps below reframe the evaluation process. Instead of asking "what is the best CRM?", the question becomes "what is the best CRM for a firm with our current stack, our data requirements, and our growth trajectory?" That shift changes every decision that follows.
Map Your Current Stack
Before evaluating any new technology, you need an accurate inventory of what you have today. Most firms discover during this exercise that they're running more tools than anyone realized — and that the connections between them are more fragile than assumed.
Your Stack Inventory Checklist
For each tool in your current environment, document the following:
- Tool name and vendor — including version and contract renewal date
- Primary function — what does this tool do, and who uses it daily?
- Data it holds — client records, transaction history, positions, documents, activity logs
- Current integrations — what does this tool send data to or receive data from?
- Integration method — native API, third-party connector, manual export, or no integration
- Pain points — what does this tool do poorly? What do users work around?
- Data portability — can you export your full dataset? In what format? Under what terms?
- Annual cost — total cost including implementation, support, and per-seat fees
Document this in a spreadsheet. Then draw the connections — literally map which tools exchange data with which other tools, and how. This visual frequently surfaces redundant systems, broken integrations, and data that lives nowhere.
Identify Your Data Gaps
Once your stack is mapped, the gaps become visible. A data gap is any business question your firm cannot answer today because the data either doesn't exist, lives in a system you can't access programmatically, or exists in multiple systems that disagree with each other.
Common Data Gaps in Advisory Firms
- Household view across custodians. When a household holds accounts at Schwab, Fidelity, and a direct indexing provider, can your advisors see the complete household picture in one place — or do they toggle between three portals?
- Advisor productivity. Can you measure which advisors are growing AUM, retaining clients, and completing review meetings on schedule — or is that data spread across your CRM, portfolio system, and calendar in formats that don't reconcile?
- Client profitability. Do you know your revenue per client after servicing costs — or do you know revenue per client from your billing system and servicing costs separately, with no way to join them?
- Compliance evidence. When regulators ask for evidence that you completed required reviews, suitability analyses, and disclosure deliveries, can you produce that evidence programmatically — or does it require manual reconstruction from multiple systems?
- Pipeline and AUM attribution. Can you attribute new AUM to a specific referral source, marketing campaign, or prospect interaction recorded in your CRM — or does that connection break at the point of account opening?
- Model drift and rebalancing trigger history. Does your portfolio system maintain an auditable history of when accounts drifted outside tolerance and what actions were taken — accessible to compliance without manual extraction?
Your data gap analysis defines the requirements that must be satisfied by any new technology you buy. A tool that doesn't close your gaps — or that creates new ones — isn't a solution.
Define Your Evaluation Criteria
Vendor demos are designed to impress. Without defined criteria evaluated before the demo cycle begins, selection committees are inevitably swayed by whichever vendor tells the most compelling story. The following ten criteria create an objective framework for comparison across any wealthtech category.
Integration Breadth
How many of your current tools does this vendor natively integrate with — and how deeply? Native APIs that sync bidirectionally matter more than one-way data exports.
Data Model Quality
Does the vendor's data model reflect the complexity of wealth management — households, accounts, positions, relationships, advisors, entities? Or does it force your data into a generic CRM or accounting schema?
AI Capabilities
Are AI features built on top of the vendor's proprietary data model, or on top of your unified data? The former limits AI to what the vendor knows; the latter lets AI learn from your firm's full history.
Time to Value
How long from signed contract to production-grade data? Platforms with pre-built connectors and normalized data models deliver value in weeks; custom-built integrations deliver value in quarters or years.
Security & Compliance
SOC 2 Type II, encryption at rest and in transit, role-based access controls, audit logging, and alignment with GLBA, FINRA, and SEC requirements. Request the vendor's security questionnaire, not just their marketing materials.
Scalability
Can the platform handle your projected AUM, account count, and data volume in three to five years — not just today? Ask for reference clients at your target scale, not just your current size.
Ownership & Portability
Who owns your data? Can you export it in full, in a machine-readable format, at any time? What happens to your data if you terminate the contract? Vendors who add conditions to these answers are signaling lock-in risk.
Total Cost of Ownership
Subscription fees are only part of the cost. Model total cost including implementation services, internal staff time, data migration, training, ongoing support, and the opportunity cost of delayed value delivery.
Vendor Stability
Is the vendor funded, profitable, or backed by an acquirer with a strategic interest in this market? Research their funding history, leadership tenure, customer retention rate, and whether they've made recent acquisitions or been acquired.
Implementation Support
Who is responsible for implementation — the vendor's professional services team, a third-party implementation partner, or your firm's internal IT team? Get implementation commitments in writing, with milestone dates and escalation paths.
Score each vendor against each criterion before demos begin. Adjust weights based on your firm's priorities — a firm with strong internal engineering capacity may weight implementation support lower; a compliance-heavy broker-dealer may weight security and compliance highest.
Choose Your Architecture
Before selecting specific vendors, decide on the architectural approach that fits your firm's size, complexity, and internal capability. Two primary models exist in the wealthtech market: all-in-one platforms and best-of-breed stacks with a data layer.
All-in-One Platform
- Single vendor manages CRM, portfolio, reporting, and billing
- Integrations are handled internally by the vendor
- Lower implementation complexity and vendor management overhead
- Faster initial time to value for firms starting from scratch
- Best for firms under $1B AUM with limited internal technology resources
- Trade-off: each individual module may be less capable than a best-in-class point solution
- Trade-off: vendor lock-in risk increases as dependence deepens
Example: Orion
Best-of-Breed + Data Layer
- Best CRM for your firm + best portfolio system + best planning tool
- Each module selected independently for capability in its domain
- Data layer (like Milemarker) connects all tools into a unified schema
- Analytics, AI, and reporting run on complete, normalized data across all systems
- Best for firms above $2B AUM or with complex multi-custodian operations
- Trade-off: higher initial implementation complexity
- Trade-off: requires a data layer investment to realize the full benefit
Where Milemarker fits
The architectural choice is not permanent. Many firms begin with an all-in-one platform for simplicity and later add a data layer as complexity grows. The key is to make the architectural decision explicitly, before selecting individual tools, rather than arriving at an architecture accidentally through a series of disconnected product evaluations.
Run a Proof of Concept
The single most reliable predictor of implementation success is whether a firm runs a structured proof of concept before signing a contract. POCs reveal the gap between what a vendor demos in a controlled environment and what their software does with your actual data.
What to Test in Your POC
- Real data, defined scope. Use a defined subset of real client data — one custodian feed, one team of advisors, 200 to 500 representative client accounts. Synthetic or sample data does not reveal integration problems.
- Your specific data gaps. Return to the gaps you identified in Step 2. The POC should answer whether those gaps are closed. If the vendor cannot answer your specific questions during the POC, they will not be able to in production.
- Workflow completeness. Run one complete end-to-end workflow — from prospect creation through account opening, first review meeting, rebalancing event, and billing cycle. Gaps in the workflow surface here.
- Integration reliability. Test how data flows between the new tool and your existing systems. Manual reconciliation requirements during the POC predict manual reconciliation in production.
- Performance at scale. If your firm has 5,000 accounts, test with 5,000 accounts — not 50. Performance bottlenecks do not appear until the system is under realistic load.
POC Duration and Success Criteria
A thorough POC runs 30 to 60 days. Define success criteria in writing before the POC begins — not after. Success criteria should be specific and measurable: "household performance reports run in under 3 seconds for all client households" rather than "reporting feels fast." Assign an internal project owner to manage the engagement daily, with authority to escalate and make decisions.
If a vendor declines to run a proof of concept, treats POC as optional, or insists on using only sample data — treat that as a significant risk signal. Vendors confident in their product welcome POCs because POCs become their best sales tool. Vendors who avoid them are protecting a gap.
Negotiate and Implement
Contract negotiation is where firms either protect themselves from implementation failures or expose themselves to them. The most important negotiating leverage you have is before signing — not after.
Implementation Timeline Expectations by Category
| Technology Category | Realistic Timeline | Key Driver of Variance |
|---|---|---|
| CRM (with data migration) | 60 to 120 days | Quality of historical contact and activity data |
| Portfolio management system | 90 to 180 days | Number of custodian connections and historical position import |
| Data layer / data platform | 60 to 120 days | Number of integrations and complexity of existing data model |
| Financial planning software | 30 to 60 days | Whether existing plan data needs migration |
| Compliance platform | 45 to 90 days | Workflow configuration and integration with existing systems |
| Custom AI or analytics layer | 90 to 270 days | Data quality, model training requirements, and scope of use cases |
Common Implementation Pitfalls
- Underestimating data migration. Historical data is almost always messier than expected. Budget for a data cleanup phase before migration begins — not after the vendor discovers the problem mid-implementation.
- No internal owner. Every successful implementation has an internal project owner with authority to make decisions and allocate team time. Implementations without this role consistently run over time and budget.
- Going live on everything at once. Phase your go-live by priority. Start with the integrations and workflows that deliver the most immediate value — not every integration the vendor supports.
- Skipping advisor training. Advisor adoption is the metric that determines whether the investment pays off. Budget for formal training before go-live, not on-demand documentation after.
- No milestone commitments in the contract. Vendors who miss timelines without consequence have no incentive to prioritize your implementation. Negotiate milestone-based payment schedules and escalation paths before signing.
The Role of the Data Layer
Regardless of which CRM you choose, which portfolio management system you select, or whether you go all-in-one or best-of-breed — every firm that manages meaningful complexity eventually needs a data layer. The question is whether you build it or buy it.
What a Data Layer Does
A data layer sits between your individual technology tools and your analytics, reporting, and AI applications. It pulls data from every source — your CRM, portfolio system, custodians, planning software, billing platform — normalizes it into a single schema, resolves conflicts, and makes clean, consistent data available to any downstream consumer.
Without a data layer, every new tool you add creates a new point-to-point integration problem. Your CRM needs to connect to your portfolio system. Your portfolio system needs to connect to your reporting tool. Your compliance platform needs to connect to your CRM. As your stack grows, the number of required connections grows exponentially. With a data layer, each tool connects once — to the layer — and the layer handles the rest.
Where Milemarker Fits
Milemarker is a wealth management data layer purpose-built for advisory firms operating complex, multi-system environments. It connects 130+ integrations — across custodians, CRMs, portfolio management systems, planning tools, and compliance platforms — into a single, normalized data warehouse that your team controls.
Milemarker is not a CRM replacement. It is not a portfolio system. It is the connective tissue between your existing tools and the analytics, compliance, and AI applications your firm needs to operate at scale. You choose the best CRM for your firm. You choose the best portfolio system. Milemarker connects them and makes the data from both — along with every other system in your stack — available as a unified, queryable dataset.
The firms that accelerate fastest aren't the ones who found the perfect all-in-one platform. They're the firms that built a connected stack — with the best tool for each job and a data layer that makes them work as a system. That's the architecture that enables AI, scales with AUM growth, and survives vendor changes without losing institutional data.
Build vs. Buy
Some firms with strong engineering teams consider building a data layer internally. The build path is viable but rarely faster or cheaper than expected. The hidden costs include: maintaining API connections to dozens of vendors who change their APIs without notice; building and updating a financial data model that accounts for the edge cases in wealth management data; and staffing the ongoing maintenance as custodians change formats and vendors release new versions. For most advisory firms, the build path costs more in engineering time than a purpose-built solution — and delivers value 12 to 18 months later.
