Finance MDM & Data Foundation
Demand cluster · Finance MDM
Trusted finance data is a precondition — not a project.
Every modern finance initiative — EPM modernization, AI in close and FP&A, board-ready reporting, M&A integration — collapses without a trusted data foundation. We design, build, and operate Finance MDM hubs: golden records, chart-of-accounts hierarchies, legal-entity dimensions, semantic layers, and lineage that hold up to audit.
Audience:
- Controllers
- Chief Data Officers
- Finance Systems leaders
- Heads of FP&A
What's blocking your downstream stack
Senior finance roles are now explicitly demanding MDM/MDH expertise, COA harmonization, semantic-layer design, and finance data hubs. The reason is consistent: every report disagrees, every consolidation is manual, every M&A requires a multi-quarter remap, and AI projects fail the audit because the inputs aren't governed.
- Three different COAs across legal entities after the last acquisition — and no plan to harmonize
- Vendor master, customer master, and product master all owned by different teams with different rules
- Reports that reconcile to themselves but not to each other
- AI and analytics projects stalled because the underlying dimensions can't be trusted
Our approach
We treat finance master data as a product, with owners, SLAs, lineage, and observability — not as a one-time cleansing project. Our consultants combine controllership rigor with data engineering depth.
Golden record design
Survivorship rules, source-of-truth designation, and stewardship workflows for vendor, customer, employee, and finance dimensions.
COA & hierarchy management
Chart-of-accounts design, alternate hierarchies, legal-entity rollups, and consolidation structures that survive M&A and reorgs.
Finance data hub
A governed semantic layer over the ledger and subledgers — one place where FP&A, reporting, and AI agree on what 'revenue' means.
Lineage & observability
End-to-end lineage from transaction to report, with data quality SLAs and automated freshness/quality monitoring.
M&A data harmonization
Acquired-entity COA mapping, vendor/customer dedup, and Day-1 / Day-30 / Day-60 master-data integration playbooks.
Stewardship & governance
Stewards, councils, change controls, and audit evidence — the operating model that keeps the data trustworthy after we leave.
Platforms we work in
We're platform-fluent across MDM, semantic layer, data quality, and lineage tools — and we'll often combine the best of native ERP capabilities with a dedicated MDM platform.
- MDM platforms: Reltio, Informatica MDM, Profisee, Stibo, SAP MDG, Oracle MDM
- Semantic & metrics layer: dbt, Cube, AtScale, LookML, Power BI Datasets
- Data quality & catalog: Alation, Collibra, Great Expectations, Soda, Monte Carlo
- Data platforms: Snowflake, Databricks, BigQuery, Synapse, Iceberg
- ERP / source: SAP S/4HANA, Oracle Fusion, NetSuite, Workday, Federal financial systems
- Integration: Boomi, MuleSoft, Fivetran, Airbyte, Informatica
Outcomes we measure
Master data is judged by how reliably the downstream stack runs on top of it.
- 1 — Defensible source of truth per critical finance dimension
- ↓ — Days to onboard an acquired entity into consolidated reporting
- ↑ — Data quality SLA pass rate across golden records
- Auditable — Lineage from transaction through report and AI output
Why Artisan Analytix for finance MDM
Our team has spent years inside federal financial systems where dimension integrity is non-negotiable, and inside commercial finance transformations where M&A constantly reshapes the data. That dual lineage shows up in how we design, govern, and operate finance MDM.
- Controller-grade design discipline paired with modern data-engineering practice
- ISO 27001 and 9001 certified delivery — security and quality controls auditors recognize on day one
- Pairs with EPM Modernization, AI-Powered Finance, and M&A Integration practices
- Vendor-neutral platform recommendations grounded in your actual workload
Frequently Asked Questions
Do we need a dedicated MDM platform?
Not always. For smaller scopes, the ERP plus a strong semantic layer can be enough. We run a structured assessment that evaluates platform need against your dimension volume, M&A cadence, and downstream consumption.