N
NNormalize
One model for rules, inventories and thresholds.
A single rule schema and inventory shape standardised across departments. The end of "every team defines it their own way".
The Problem
Data quality is broken across your org.
Every team has its own rules. Every department its own thresholds. Spreadsheets everywhere, fixes without owners, failures without traces.
No single source of truth. No accountability. No way to see how quality evolves over time.
NEPTUNO
There's a better way to do this.
Seven principles. One platform.
The Acronym
What NEPTUNO stands for.
Each letter is a commitment. Together, they replace the chaos with a system.
E
EExecute
One unified, reproducible engine.
An engine-agnostic core that runs the same rule on Apache Spark, Databricks — or whatever comes next. Same definition, same result.
P
PPersist
Data lake AND relational store.
Results live in your data lake AND in a relational database. Historical analysis and time-series evolution of data quality become possible — something most DQ frameworks simply do not do.
T
TTrack
End-to-end audit log.
Every fix, every annotation. Author, timestamp, response time. Traceability isn't a feature, it's the design.
U
UUnify
One inventory. One view.
A single visualization layer over a single inventory, queryable cross-team. One source of truth instead of N spreadsheets.
N
NNotify
Alerts with ownership and escalation.
Workflow-driven alerts. Every error has an owner, an SLA, and an escalation path. Silent failures stop being silent.
O
OOptimize
Continuous performance gains.
Continuous improvement of execution thanks to the unified engine and process telemetry. Faster suite, fewer cycles, lower cost.
The Pillars
Five pillars. Seven letters.
Every letter feeds one or more of the pillars that hold the platform together.
Organization
Standardised rules and a unified inventory turn N teams into one.
Governance
Notify-driven workflows assign ownership and escalation, replacing tribal knowledge with policy.
Visualization
A single visual surface for inventory, runs, results, and history.
Traceability
Audit log of every fix, run, and annotation — author, timestamp, SLA.
Optimization
Engine-level performance, persistent results, and continuous tuning compound over time.
The End State
Data quality, finally under control.
From scattered spreadsheets to a single platform with traceability, ownership, and history.