Every finance PM says they are data-driven. Very few have actually built the instrumentation, KPI frameworks, and escalation paths needed to make that true in a regulated environment where data quality, data lineage, and access controls all have compliance implications.
Being data-driven in a consumer product is relatively straightforward: you instrument user behaviour, run experiments, measure outcomes, and iterate. In finance product management, the data landscape is significantly more complex. And the consequences of making a bad decision based on bad data are significantly more serious.
The Finance PM's Data Challenge
Finance platforms generate multiple categories of data with very different characteristics. Operational data (transaction volumes, processing times, error rates) behaves like standard product analytics data and can be instrumented with similar tools. Financial data (balances, P&L, cash flow) is subject to audit requirements, access controls, and data lineage obligations that standard product analytics tools aren't designed to handle. Regulatory data, the outputs that feed external reports to tax authorities and financial regulators, has the most stringent quality and lineage requirements of all.
A product team that doesn't understand these distinctions will instrument their platform in ways that create compliance gaps. The data they collect may be accurate as an operational measure and simultaneously unusable as evidence in an audit.
Building a KPI Framework for Finance Platforms
The KPI framework I've used across multiple finance platform roles has four layers. Platform health metrics (uptime, error rates, processing latency) measure whether the system is working. Process efficiency metrics (straight-through processing rate, exception rate, manual intervention rate) measure whether the process is working. Financial accuracy metrics (reconciliation variance, posting error rate, period-end close duration) measure whether the financial output is correct. Compliance metrics (audit trail completeness, access review completion rate, regulatory submission on-time rate) measure whether the governance obligations are being met.
Each layer requires different instrumentation, different ownership, and different escalation paths. Combining them into a single dashboard without understanding their different characters produces a misleading view of platform health.
Data Governance Is a Product Responsibility
The most common gap I see in finance product teams is treating data governance as someone else's problem: the data team's, the compliance team's, the engineering team's. In practice, data governance decisions are product decisions: what data is retained, for how long, with what access controls, with what lineage documentation.
In one engagement, I worked with the data team to establish a data classification framework for the finance platform: four categories of data, each with defined retention policies, access control requirements, and audit trail obligations. Building this into the product design, rather than retrofitting it later, saved us a significant remediation effort and produced a platform that the audit team could actually work with.
The Decision-Making Discipline
Data-driven decision making ultimately requires the discipline to wait for data before deciding, to distinguish between correlation and causation, and to update your position when the data contradicts your assumptions. In regulated industries, it also requires the discipline to document your decision rationale. Not for the sake of bureaucracy, but because the decisions you make today will be reviewed by auditors tomorrow, and "the data suggested it" is not a sufficient explanation without knowing which data, how it was interpreted, and what alternatives were considered.