The Data Provider's Paradox: Why Financial Data Companies Can't Analyze Their Own Operations

The Question the CFO Can't Answer

The Head of Finance at a major financial data provider opens a board presentation. The slide reads: "Strategic Objective: Reduce Customer Churn by 15%."

To build the strategy, they need to answer: "Which customer segments have highest churn? Which products drive retention? How does pricing correlate with customer lifetime value?"

The data team responds: "We can't answer these questions reliably."

Not because they lack data. They have vast amounts of customer interaction data, product usage telemetry, transaction histories. The problem: it's fragmented across incompatible taxonomies from multiple acquisitions.

• Platform A (acquired 2015): Customers classified by "account type" (Tier 1, Tier 2, Tier 3) based on contract value
• Platform B (acquired 2018): Customers segmented by "client category" (Buy-side, Sell-side, Corporate, Government) based on industry
• Platform C (acquired 2021): Customers tagged by "engagement level" (Power User, Standard, Light) based on product usage

Same customers across all three platforms-but classified three different ways. The analytics team can't consolidate churn metrics because the taxonomies are incompatible.

This is the data provider's paradox: selling data quality, analytics, and business intelligence to thousands of clients while unable to analyze their own operations effectively.

How Financial Data Providers Develop Internal Taxonomy Chaos

Growth Through M&A Creates Layered Complexity

Most major financial data providers have grown through acquisition. A typical trajectory:

2010-2015: Core platform established

• Market data feeds for equities, fixed income, FX
• Customer taxonomy: Simple seat-based licensing
• Product hierarchy: Data feeds, terminal software, APIs
• Internal operations: Single CRM, straightforward reporting

2016-2018: Acquisition of analytics specialist

• Brings quantitative analytics, backtesting tools, risk models
• Different customer classification (quant-focused vs. generalist)
• Product bundling doesn't map to original hierarchy
• Separate customer success organization with different CRM codes

2019-2021: Acquisition of alternative data provider

• Satellite imagery, web scraping, ESG data, sentiment analysis
• Completely different customer profile (data scientists vs. traders)
• Usage-based pricing vs. subscription model
• Third taxonomy for data products

2022-2024: Acquisition of risk intelligence platform

• KYC, AML, sanctions screening, PEP databases
• Regulatory compliance focus vs. investment focus
• Fourth customer segmentation model
• Different sales organization, different account structure

Post-integration, the company now has four overlapping customer master data structures, four product taxonomies, four pricing models-and no unified view of operations.

External Data Products Are Pristine, Internal Operations Are Fragmented

The paradox deepens when you recognize these companies are experts at data standardization for external products:

What they sell to clients (immaculate):

• Standardized security identifiers (ISINs, CUSIPs, SEDOLs, PermIDs)
• Curated corporate actions data
• Normalized financial statements across jurisdictions
• Harmonized ESG metrics
• Clean, versioned reference data

What they run on internally (fragmented):

• Customer records duplicated across acquired CRM systems
• Product catalogs from different eras using incompatible hierarchies
• Financial reporting that requires manual reconciliation
• Usage metrics from platforms that don't use common event taxonomy
• Sales pipeline data with conflicting opportunity classifications

The company employs data scientists who curate market data for clients but struggle to analyze their own customer churn patterns because internal data isn't standardized.

Each Acquisition Brings a Complete Data Ecosystem

When a financial data provider acquires a competitor or specialist, they're not just buying products-they're acquiring:

• Customer master data: 5,000-15,000 institutional clients with unique IDs, classifications, hierarchies
• Product catalogs: 50-200 products with distinct taxonomies, bundling logic, licensing models
• Pricing structures: Enterprise licenses, seat-based, usage-based, tiered packages-all coded differently
• Usage telemetry: Activity tracking, feature usage, API calls-different event schemas
• Financial systems: Revenue recognition rules, cost allocation, margin calculations-separate GL structures
• Operational metrics: Customer success KPIs, support ticket classifications, onboarding workflows-incompatible

Integration teams focus on customer-facing systems (ensuring clients can still log in, access data feeds, get support). But internal operational data standardization gets deferred-"we'll clean that up later."

Years pass. "Later" never comes. The taxonomy debt compounds.

Seven Ways Internal Taxonomy Chaos Destroys Value

1. Customer Analytics Impossible: Can't Identify Churn Patterns

Customer churn is the existential threat for subscription-based data providers. Losing a $500k/year institutional client impacts both revenue and valuation multiples.

But predicting churn requires understanding:

• Product usage patterns (declining engagement signals risk)
• Support ticket trends (increasing complaints predict churn)
• Competitive product adoption (client using competitor data feeds)
• Pricing sensitivity (clients consolidating to fewer vendors)
• Economic exposure (client firms facing budget pressure)

The problem: These signals live in systems using incompatible taxonomies.

Usage data from Platform A tracks "sessions" and "queries." Platform B tracks "API calls" and "data downloads." Platform C tracks "user actions" and "workflow completions." They're measuring similar engagement patterns but can't be aggregated because the event taxonomies differ.

Support tickets from CRM A categorize issues as "Technical," "Billing," "Training." CRM B uses "Product," "Commercial," "Operational." CRM C uses severity codes: "P1," "P2," "P3." Same underlying issues, incompatible classification.

Real scenario: The churn that wasn't predicted

A major buy-side institution-$2M annual contract-gives 90 days notice. The customer success team is blindsided.

Post-mortem analysis reveals warning signals existed:

• Platform A: Usage declined 40% over 6 months (but this system doesn't flag trends)
• Platform B: Support tickets increased 3x (but coded differently, not aggregated with Platform A data)
• Platform C: Competitive intelligence showed client adopted rival's alternative data (but this insight sits in separate system)

Each signal individually looked normal. Together, they screamed "churn risk." But no unified view existed to connect them.

Financial impact: A data provider with 10,000-15,000 institutional clients and 8% annual churn loses 800-1,200 clients/year. If better churn prediction could retain even 10% of at-risk accounts (80-120 clients), that's £10M-£20M annual revenue saved.

2. Cross-Sell Intelligence Trapped: Can't Identify Product Affinity

The most valuable customers buy multiple products. A trading desk might use:

• Real-time market data feeds
• Quantitative analytics platform
• Risk management tools
• Execution management system
• Regulatory reporting solution

Understanding product affinity drives:

• Bundling strategy (which products to package together)
• Sales targeting (who to approach with what offering)
• Pricing optimization (discount structures for multi-product customers)
• Product roadmap (which integrations deliver most value)

But product affinity analysis requires unified customer and product taxonomies-which don't exist post-M&A.

Platform A customers are keyed by "Account ID." Platform B uses "Client Code." Platform C uses "Organization GUID." Same customer, three different identifiers. Without master data management linking these, cross-platform product usage can't be analyzed.

Product hierarchies are equally fragmented:

• Platform A organizes by "Data Type" (Equities, Fixed Income, FX, Commodities)
• Platform B organizes by "Function" (Analytics, Risk, Compliance, Trading)
• Platform C organizes by "Delivery Method" (Real-time Feed, API, Desktop Terminal)

These aren't wrong-they reflect different product philosophies. But they make cross-sell analysis impossible.

The missed opportunity: A data provider discovers (through manual analysis taking 3 months) that customers using equity analytics + alternative data have 60% higher retention than single-product customers. This insight should drive sales strategy immediately.

But rolling it out requires:

• Identifying which customers have equity analytics (Platform B taxonomy)
• Identifying which have alternative data (Platform C taxonomy)
• Linking customer records across platforms (no unified key)
• Validating the analysis (manual reconciliation, weeks of work)

By the time the sales campaign launches, market conditions have changed and the opportunity has passed.

3. Product Portfolio Optimization Blocked: Can't Identify What to Sunset

Post-M&A, financial data providers often have overlapping products. Three acquired companies each have an "equity analytics" offering. Which one becomes the go-forward platform? Which get sunset?

This decision should be data-driven:

• Which has highest customer satisfaction?
• Which generates highest revenue per user?
• Which has best retention rates?
• Which has lowest cost to serve?
• Which has best technical architecture for future development?

But answering these requires comparing products classified in incompatible taxonomies.

Product A's revenue is recorded against "Market Data - Equity Analytics" in the legacy GL structure. Product B's revenue sits under "Analytics Solutions - Quantitative Tools." Product C is classified as "Data Services - Premium Tier."

They're functionally similar products, but financial systems code them completely differently. Consolidating P&L by product requires manual mapping-which changes quarterly as products evolve and new bundles are introduced.

The consequence: Product decisions get made politically rather than analytically.

The loudest product manager wins. The product from the largest acquisition survives by default. Technical debt accumulates as overlapping products continue operating in parallel because no one can definitively prove which should be discontinued.

Financial impact: A data provider supporting redundant analytics platforms across three acquired companies might spend £3M-£5M annually in duplicated engineering, infrastructure, and support. Clear product rationalization could eliminate this-but requires unified product taxonomy to make the case.

4. Pricing Strategy Fragmented: Can't Implement Dynamic or Usage-Based Models

Modern pricing strategies for data products increasingly use:

• Usage-based models (pay for API calls, queries, data volumes)
• Dynamic pricing (adjust based on market conditions, demand, customer segment)
• Value-based pricing (tie pricing to client outcomes, trading volumes, AUM)

But these sophisticated pricing approaches require understanding:

• How customers actually use products (usage telemetry)
• Which features drive value (feature adoption tracking)
• Price sensitivity by segment (elasticity analysis)
• Competitive positioning (price benchmarking by product category)

Taxonomy fragmentation blocks pricing innovation:

Platform A charges by "seat" (number of users). Platform B charges by "data volume" (GB consumed). Platform C charges by "entitlement tier" (Gold, Silver, Bronze). Post-merger, the company wants to move to unified value-based pricing-but can't compare revenue and usage patterns across these incompatible models.

The pricing team tries to analyze: "What would usage-based pricing look like across our portfolio?"

They discover:

• Platform A doesn't track individual user actions (just seat count)
• Platform B tracks data volume but not what clients do with the data
• Platform C tracks "feature usage" but uses completely different event taxonomy than Platform B's "data consumption"

Building a unified usage metric requires reconciling three incompatible telemetry taxonomies. The project stalls. Pricing remains seat-based while competitors move to more attractive usage models.

5. Customer Success Operations Fragmented: Can't Optimize Service Delivery

Post-M&A, customer success teams often operate separately for each acquired platform. This makes sense initially-specialist knowledge is required. But long-term, it creates inefficiency:

• Customers using multiple products interact with multiple CSMs
• Support tickets routed to different teams using different systems
• Onboarding workflows differ by product
• Customer health scoring uses different metrics per platform

The taxonomy problem: Can't consolidate customer success metrics because operational taxonomies don't align.

Platform A tracks "customer health" using: Product usage + NPS + Support ticket volume + Billing issues

Platform B tracks "engagement score" using: Login frequency + Feature adoption + Training completion + Renewal probability

Platform C tracks "account status" using: Contract value + Usage trend + Executive engagement + Risk flags

They're measuring similar constructs (customer health) but can't be aggregated because the underlying taxonomies differ.

The operational consequence:

A large institutional customer uses products from all three platforms. They have three different CSMs, each working in separate systems with different health metrics. When the customer signals dissatisfaction:

• CSM A sees it in their system (Platform A usage declining)
• CSM B misses it (Platform B shows usage as "stable")
• CSM C sees different signal (Platform C shows increased support tickets but codes them as "training" not "satisfaction")

No unified escalation happens. The customer churns. Post-mortem reveals signals existed across all three platforms but weren't visible to any single team.

6. Financial Reporting Requires Manual Reconciliation

CFOs of financial data providers need to report:

• Revenue by product line
• Revenue by customer segment
• Revenue by geography
• Gross margin by product/segment
• Customer acquisition cost (CAC) and lifetime value (LTV)

But post-M&A, financial taxonomies are incompatible across acquired entities.

Platform A's GL structure organizes revenue by "Solution Type" (Data, Analytics, Platforms). Platform B uses "Customer Vertical" (Banking, Asset Management, Insurance). Platform C uses "Delivery Method" (SaaS, Managed Service, On-Premise).

To produce a consolidated product line P&L, finance teams must:

1. Export GL data from each platform
2. Manually map revenue accounts to unified product taxonomy
3. Reconcile customer overlaps (same customer across multiple systems)
4. Allocate shared costs (infrastructure, sales, G&A)
5. Validate totals (ensure nothing double-counted or missed)

This process takes 8-12 FTEs working 2-3 weeks each month. The board gets financial reports 3-4 weeks after month-end. Strategic decisions are delayed waiting for data.

The strategic cost: Slow financial reporting means the company can't respond quickly to market changes, competitive threats, or customer shifts. By the time they see a product line declining, the trend has been negative for months.

7. AI and Advanced Analytics Projects Blocked

Financial data providers recognize AI opportunity:

• Predictive churn models
• Dynamic pricing optimization
• Product recommendation engines
• Customer segmentation using ML
• Automated anomaly detection

But AI requires training data-and training data requires standardized taxonomy.

The GenAI deployment that couldn't train:

A data provider invests in machine learning to predict customer churn. The data science team needs:

• Historical customer data (3-5 years)
• Product usage patterns
• Support interactions
• Billing/payment history
• Account team notes

They discover historical data uses four different taxonomies (pre-merger platform, three acquired platforms). Training a model requires:

1. Mapping customer IDs across systems (manual, error-prone)
2. Standardizing product usage metrics (different event schemas)
3. Unifying support ticket categorizations (four different taxonomies)
4. Harmonizing customer segmentation (each system classifies differently)

The data preparation takes 9 months. By the time the model is ready, the business context has changed (new products launched, pricing restructured). The model trains on outdated taxonomy structure and performs poorly.

Project abandoned. £300k-£400k investment, zero production deployment.

Why Financial Data Providers Can't Fix This Internally

They're Experts at External Data, Not Internal Operations

The skills required to curate market data for clients are different from those needed for post-M&A taxonomy integration:

External data curation (their core competency):

• Standardizing security identifiers
• Normalizing corporate actions
• Harmonizing financial statement data
• Creating reference data taxonomies

Internal operations integration (different skill set):

• Customer master data consolidation
• Product hierarchy unification
• Financial system integration
• Operational metrics standardization

Internal data teams excel at the first. They've spent careers perfecting it. But post-M&A operational taxonomy standardization requires different expertise-cross-industry best practices from manufacturing, hospitality, banking operations.

Internal Teams Lack Cross-Functional Authority

Taxonomy standardization spans all divisions:

• Finance (GL structure, revenue recognition)
• Product (product hierarchy, feature taxonomy)
• Sales (customer segmentation, opportunity classification)
• Customer Success (health scoring, operational metrics)
• Engineering (telemetry, event taxonomies)

Each division has entrenched interests. Product teams resist changing product codes (breaks their reporting). Sales teams resist new CRM structure (disrupts their workflows). Finance resists GL changes (impacts audit trails).

Internal data teams don't have authority to override these objections. Taxonomy initiatives stall in endless working groups.

Integration Resources Committed to Customer-Facing Systems

Post-M&A, integration teams prioritize:

• Ensuring clients can log in (SSO integration)
• Billing continuity (payment processing)
• Support availability (ticketing systems)
• Data feed reliability (core product delivery)

These are rightly prioritized-customers must be served. But internal operational taxonomy standardization gets perpetually deferred. "We'll clean that up once integration is complete."

Years pass. Integration is "complete" in the sense that customers are served. But internal taxonomy chaos persists indefinitely.

What Systematic Internal Taxonomy Standardization Looks Like

FireCherry's approach to financial data provider taxonomy standardization recognizes this isn't about criticizing internal teams-it's about bringing external perspective and cross-industry best practices they don't have internally.

Post-M&A Operations Taxonomy Integration (22-28 weeks)

Week 1-4: Cross-Platform Taxonomy Audit

• Document customer master data structures across acquired platforms
• Map product hierarchies and classification schemes
• Inventory financial reporting structures and GL taxonomies
• Review operational metrics (usage telemetry, customer health, support categorization)
• Analyze sales and marketing data structures (CRM, opportunity classification)
• Interview stakeholders across Finance, Product, Sales, Customer Success, Engineering

Week 5-8: Unified Operations Taxonomy Design

• Create master customer taxonomy that accommodates all acquired platforms while enabling consolidated analytics
• Design unified product hierarchy that supports brand differentiation while enabling portfolio optimization
• Build operational metrics framework standardized across platforms
• Develop financial reporting taxonomy aligned with board/investor requirements
• Create semantic mapping rules from platform-specific codes to unified taxonomy

Week 9-18: Data Transformation and Historical Reconciliation

• Build automated ETL pipelines translating platform-specific data to standardized format
• Transform historical customer data (3-5 years) to unified customer master
• Map product revenue and usage data to unified product taxonomy
• Reconcile financial data for consolidated reporting
• Validate transformation accuracy against known business outcomes
• Create real-time data flow from platforms to unified analytics layer

Week 19-24: Analytics Platform Integration & Enablement

• Load standardized data into business intelligence platforms
• Build customer analytics dashboards (churn prediction, LTV, segmentation)
• Enable product portfolio analytics (profitability, usage patterns, affinity)
• Create financial reporting using unified taxonomy
• Test AI/ML models against standardized data structures

Week 25-28: Validation, Training & Knowledge Transfer

• Validate analytics against known business outcomes
• Train Finance, Product, Sales, Customer Success teams on new capabilities
• Document governance for maintaining taxonomy standards as business evolves
• Handover with ongoing support protocols

Deliverables:

• Unified customer master data (all platforms, all acquired entities)
• Standardized product taxonomy (all offerings, all brands)
• Automated data transformation pipelines (platform-to-unified semantic mapping)
• Historical operational data transformed and validated (typically 3-5 years)
• Consolidated analytics platform delivering business intelligence previously impossible
• Governance framework maintaining standards as company evolves

Why Financial Data Providers Choose FireCherry

Cross-industry perspective. We bring taxonomy standardization experience from manufacturing M&A, hospitality multi-property integration, banking regulatory compliance, and cruise line operations. Your internal teams have deep financial data expertise but haven't seen how other industries solve post-M&A taxonomy challenges.

Speed: 22-28 weeks vs 3-5 years. Internal approaches rely on working groups, consensus building, competing priorities. Our dedicated engagement delivers complete standardization in months. Board-ready analytics become available immediately.

External authority. We have no internal politics, no legacy system attachment, no organizational turf battles. Our recommendations are based purely on what enables best business outcomes.

Rapid ROI. Elimination of manual reconciliation labor (£5M-£10M annually) and improved business intelligence (enabling better customer retention, pricing optimization, product decisions) typically deliver full return on investment in the first year. Subsequent years are pure value creation.

"Financial data providers excel at curating market data for clients-standardizing security identifiers, normalizing corporate actions, harmonizing financial statements. But post-M&A, their internal operations run on fragmented taxonomies from acquired platforms. The CFO can't answer basic questions about customer churn, product profitability, or cross-sell opportunities because internal data isn't standardized. Most data providers discover this 2-3 years post-acquisition-after integration teams have moved on and the problem has become permanent operational drag."

Related reading: See our guide on why enterprise codesets need formal specifications, or explore how manufacturing companies tackle similar post-M&A taxonomy challenges.