As finance organizations modernize their data and planning ecosystems, Data Warehouse and Data Lake platforms such as Snowflake and Databricks have become the backbone of analytics and data engineering, while enterprise performance management (EPM) and corporate performance management (CPM) platforms such as OneStream and Oracle EPM Cloud are increasingly central to planning, consolidation, and forecasting. In parallel, many organizations are investing in master data management (MDM) platforms to create consistency across all these systems.
On paper, these initiatives appear aligned. In practice, they often collide because the same hierarchies, entities, and accounts must serve Data Warehouse, EPM, and ERP simultaneously. Understanding how Profisee actually operates in this ecosystem is therefore essential before finance leaders select it as their MDM backbone.
At a high level, Profisee is designed to act as an enterprise master data hub. It sits upstream from analytical platforms such as Snowflake, Databricks and Power BI to produce curated master data that those platforms consume. ERP, CRM, and operational systems feed Profisee, which applies matching, survivorship, stewardship workflows, and hierarchy management. The resulting "golden records" are then published to Data Warehouses, where they become the dimension tables and reference data that analytics and data science teams rely on.
For enterprise analytics, this is a strong and well-understood architecture. Data Warehouse and Lakes benefit from cleaner, standardized dimensions. Data engineering teams gain a governed source for master data that fits neatly into modern cloud pipelines.
Finance does not treat hierarchies as descriptive reference data. Account, Entity, Cost Center, and Product hierarchies are calculation engines. They determine how revenue rolls up, how eliminations occur, how forecasts aggregate, and how regulatory and management reports are produced. A parent-child relationship in OneStream or Oracle EPM is not just a link; it is part of the company’s financial operating model.
Data Warehouses, however, have no understanding of that. They are exceptionally powerful data platforms, but they do not understand finance semantics. To them, a hierarchy is simply a relationship in a table. Profisee can publish hierarchies into these platforms, but it does not natively validate them against the rules of OneStream, Oracle EPM, or ERP. From the platform’s point of view, a hierarchy is correct as long as it loads. From a finance team’s point of view, it is only correct if it produces the right numbers in planning, consolidation, and close.
For example, in a typical Profisee-to-Snowflake or Profisee-to-Databricks implementation, the data platforms are downstream consumers. Profisee pushes tables. Snowflake or Databricks ingests them. If something is wrong, it is usually discovered after the fact, when a reconciliation breaks, a variance looks wrong, or an EPM load fails. Governance becomes detective work rather than preventive control.
The problem compounds because analytics and data engineering teams almost always reshape hierarchies inside Snowflake or Databricks to make them more useful for reporting and analysis. They flatten trees, create alternate rollups, or build bridge tables that serve different management views. None of this naturally flows back into Profisee. Over time, our Data Warehouse teams develop their own versions of finance hierarchies that drift away from what EPM and ERP are using.
At that point, the organization is running multiple financial realities. One lives in EPM for close and forecast. Another lives in Data Warehouse for analytics. Reconciling them becomes a permanent activity for the finance team.
This is not because Profisee is inadequate, but because it was designed to master data for enterprise use, not to serve as the control system for finance’s calculation structures across multiple applications.
EPMware treats applications like Power BI, Databricks, EPM, and ERP as equal subscribers to a single, finance-governed metadata model. Hierarchies are not generic trees; they are finance structures with application-aware validation, shared members, alternate rollups, and release controls. Changes are approved through finance workflows, validated against the requirements of EPM engines, and deployed in controlled releases that respect close and forecast calendars.
Data Warehouses still perform analytics, but they receive the same governed hierarchies that EPM and ERP use. There is no separate analytics truth and finance truth. There is only one.
For finance leaders evaluating Profisee in a Snowflake- and Databricks-centric architecture, the most important questions are not about whether Profisee can load tables into those platforms. The real questions are whether it can guarantee that those tables will always match the structures used in EPM and ERP, whether it can enforce finance-specific validation before publishing, and whether it can manage controlled releases with rollback when something goes wrong.
If those controls live in custom pipelines, manual governance, or downstream reconciliation, then the platform is not truly governing finance metadata; it is simply feeding it.
Profisee combined with Data Warehouse is a powerful analytics and MDM architecture. EPMware, combined with those same platforms, is a finance governance architecture. When planning, consolidation, reporting, and analytics all depend on the same hierarchies, that distinction determines whether finance teams are in control of their numbers or chasing them.
Interested in seeing examples of how EPMware governs and shares finance master data across ERP, EPM and Data Warehouse? Schedule a conversation with our team using the link below.