One workflow to turn raw enterprise data into a trusted product
Pinnova gives data teams one six-step discipline, from first inventory to everyday use, so data becomes something the business relies on. Proven on Supply Chain and Finance, it works for any data domain. It installs inside your firewall and is free to start.
Register data products with real, named ownership
Every data product has named owners on both sides of the business, so nobody has to guess who to call when something breaks.
- Technical and business owners, both responsible and accountable, on every product
- Each product maps to one governed data entity and its source system
- Plain-language description kept alongside the technical definition
- Owners come from a data steward directory, with email and phone on file for each
- Business users request access to a functional group's products; the group's leader (or an administrator) approves — no ticket queue
Know every dataset you actually have
Every data entity — a table, a view, a HANA calculation view — is registered once with its connection, schema, and object name, and connectivity-tested before anything runs against it.
- Entities mapped to connection, schema, and object across every source system
- One-click connectivity tests before profiling or validation runs
- Ten database platforms: SAP HANA, Microsoft SQL Server, MySQL, PostgreSQL, Oracle, Snowflake, Databricks, and DB2 over ODBC, plus SAP Business Data Cloud and SAP BW over their REST / OData APIs
Understand the shape of your data before you build on it
Every run captures row counts and full schema metadata — column name, data type, length, and scale — plus downloadable samples, so teams find data problems before production does. A schema comparison report lines up every data product against every other and flags column overlap, gaps, and data type mismatches.
- Row-count volume tracked and charted by data product, run over run
- Full column schema — name, type, length, scale — refreshed automatically
- Downloadable data samples for every profiled entity
- Pairwise schema comparison across products, side by side
- Mark a data product Sensitive to keep row counts and schema but skip sample extraction
Reconcile source and reference data down to the row
Every validation checksums your source entity against a reference entity — grouped by block and subblock, filtered to the records that matter, and compared within a defined tolerance. When they don't match, Pinnova drills down to the exact rows.
- Checksum reconciliation with configurable block, subblock, and tolerance
- Automatic drill-down to mismatched rows, exported as CSV
- Either side can count rows instead of summing, so a view can be proved against the table under it
- Proposed validation rules, drafted from two profiled schemas with a confidence score each, ready to review and save
- Pass rate and quality score tracked per data product, run over run
Watch quality and volume over time — and know the moment a check breaks
Pinnova tracks quality and volume trends per data product. When a run fails, it emails the responsible owner, and escalates to the accountable owner if the check keeps failing. Each run also records its runtime, so slow-downs show up before they become incidents.
- Average and peak runtime tracked per data product
- Trend charts across every historical run
- Email alerts to the responsible owner on every validation failure, escalating to the accountable owner after repeated failures
- Every notification attempt — sent or failed — is logged to an audit trail
- Admin > Databases checks what is registered against what each source actually holds, so a renamed view, a dropped grant, or a missing rule column shows up before the run, not as a failed job at 02:00
Point every data product at where people can actually see it
A trusted data product nobody knows about changes no decisions. Each data product registers against one or more dashboards — Power BI today — so people know where to look and who to ask.
- Data products map many-to-many to registered dashboards, so one report can serve several products
- Every dashboard has a named responsible and accountable owner, not just a link
- Each employee gets a "My Data Products" view of the functional groups they're approved for — Roadmap: full-text search as the catalog grows
- Optional Microsoft Entra ID single sign-on alongside local accounts, and an optional single-email-domain restriction
Sixteen procure-to-pay analyses as SAP HANA views
The Reference Data Models download (Enterprise) includes KPI views — 115 SAP HANA views across 16 purchase-to-pay analyses — that answer the questions procurement, finance, and audit teams ask of SAP S/4HANA data. Deploy them on SAP HANA with python tools/deploy.py views; their thresholds are rows of a settings table, changed with one UPDATE and no redeployment. Any tool that reads a table reads them, and Pinnova BDS profiles, validates, and governs them like any other data: register a view as a data entity, profile and validate it, and put a data product with named owners over it.
- Three-way-match exceptions, duplicate invoice detection, and P2P controls & fraud signals
- Maverick spend, price variance, supplier spend concentration, and category spend planning
- Contract utilization & expiry, payment terms & discount optimization, inventory & working capital
- Material and vendor master data quality checks, GR/IR aging and close, and AP processing efficiency
See the platform on your own data
Download the free tier and run it against your own SAP HANA, SQL Server, or other data sources today.