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DataOps · 02 of 6

Problems it solves

Four common data problems, where the loop solves each, and the first use cases.

Six frictions we look for
  1. Slow analytics
  2. Fragmented stack
  3. Inconsistent quality
  4. Limited governance
  5. Rising cost
  6. AI stuck in proof of concept
01

Four problems and where they get solved

Four problems recur in data estates. Point at one to see where the loop solves it.

ProblemWhere it is solved
DataOps loopplan → ingest → … → operate

Point at a source to see what it feeds, or at a result to see where it comes from.

02

From the usual data estate to DataOps

Six frictions we look for in an assessment, and what changes once the platform is in place.

Typical data estate With DataOps
Slow analyticsReports arrive late; each new source is a project.Ingestion templates and orchestrated pipelines with service levels.
Fragmented stackEach team picks its tools and copies data between them.One platform: shared storage, catalog and CI/CD, a domain per team.
Inconsistent qualityDashboards disagree; the business finds the errors.Tests and contracts in every pipeline; a failed check stops the run.
Limited governanceAccess by ticket, unknown lineage, audit evidence by hand.Policy-driven access; lineage and audit logs from the platform.
Rising costWarehouse and compute bills grow; nobody owns them.Cost per workload and team; idle compute scaled down.
AI stuck in proof of conceptModels and agents built on a one-off extract, then stall.Governed data access for ML, RAG and agents in production.
03

First use cases, tied to business pain

We start from the pain the business feels, then pick the pipeline and platform change that removes it.

SourcesNGT DataOps service offering drafts (Sep–Oct 2026)NGT DataOps & Data Platform Ops service catalogue (Sep 2026)NGT DataOps one-page offering and operating model (Sep 2026)NGT AI-First DevOps Transformation service offering (Oct 2026)NGT Google Cloud service offering v0.1