Data & Analytics

Lineage Is What Lets an Answer Survive the Question That Follows It

Every stalled AI program traces back to data nobody trusts. Governance is not paperwork and lineage is not a compliance artifact. They are the difference between an answer a business can act on and an answer that falls apart the first time a regulator, an auditor, or a board member asks where the number came from.
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See the tooling landscape
Why Infolob

Most Governance Programs Fail Because They Start at the Catalog

A catalog is where governance is recorded. It is not where governance is decided. The decisions that matter sit in the source systems, in the business logic nobody documented, and in the access model that security will or will not sign.

We run to the source system

Lineage that does not stop at the pipeline

Most lineage implementations trace back to the ingestion layer and stop. Ours runs into the Oracle schema, the ERP table, and the PL/SQL that transformed the value, because we have spent 17+ years inside those systems.

Tool-agnostic, deliberately

We do not resell a catalog

We implement across hyperscaler-native, platform-native, and independent tooling. The right answer depends on your estate shape, not on which vendor pays a referral fee. Frequently the answer is two tools, not one.

Governance as AI enablement

Not as a compliance tax

Retrieval quality, agent access boundaries, and model risk documentation all depend on the same governance layer. Built once, it serves both the auditor and the agent.

One partner, strategy through steady state

Advise

Advisory

Build

Implementation

Migrate

Migration

Run

Managed Services

Extend

Custom Development

Optimize

FinOps

The tooling landscape

Three Categories, Different Answers

Governance tooling divides into three groups with genuinely different economics and failure modes. Most enterprise estates end up spanning at least two of them, which is the part vendors do not tell you.

Hyperscaler-native

Deepest integration with the cloud estate they belong to, weakest coverage of everything outside it. Strong choice when the data gravity sits in one cloud.
Microsoft

Microsoft Purview

Catalog, classification, lineage, and policy across the Microsoft estate, extending into Fabric and Power BI. The strongest fit where Azure and Fabric are the center of gravity.

Azure · Fabric · Microsoft 365

Google Cloud

Dataplex Universal Catalog

Unified catalog, metadata, quality, and lineage across BigQuery and the wider Google Cloud data estate, with governance applied at the lake and warehouse layer together.

BigQuery · Google Cloud Storage

AWS

Glue Data Catalog and Lake Formation

Catalog and fine-grained access control across the AWS analytics stack, with governed data sharing between accounts and business domains.

S3 · Redshift · Athena

Oracle

OCI Data Catalog and EDM

Technical cataloging across OCI data assets, integrated with Enterprise Data Management for reference data, hierarchies, master data, and governance domains.

OCI · Oracle Database · Fusion

Platform-native

Governance built into the data platform itself rather than layered above it. Enforcement is real rather than advisory, which is the significant advantage. Coverage stops at the platform boundary.
Databricks

Unity Catalog

Governance, access control, and lineage enforced natively across lakehouse assets at query time, covering data, models, notebooks, and files.

Delta · ML models · Volumes

IBM

Knowledge Catalog and Manta

Enterprise cataloging with automated lineage extraction, including the deep code-level lineage that Manta parses out of ETL, stored procedures, and reporting layers.

watsonx.data · Legacy ETL

Open source

OpenMetadata, DataHub, Atlas

Credible open options where licensing economics dominate or where the estate is too heterogeneous for any single vendor. Lower cost, higher engineering ownership.

Self-managed

Independent

Built to sit above a multi-vendor estate. The right answer when data spans several clouds and platforms and no native tool can see the whole picture.
Catalog

Collibra

Enterprise governance, stewardship, and policy management with accountable ownership, embedded across business operating models.

Multi-cloud

Catalog

Alation and Atlan

Catalog and discovery weighted toward adoption. Strong where the constraint is analysts not finding or trusting data rather than policy enforcement.

Multi-cloud

Quality

Ataccama, Monte Carlo

Data quality and observability with rules, profiling, and anomaly detection that identify pipeline issues before they impact reports, dashboards, and decisions.

Quality · Observability

Access

Immuta and Informatica

Policy-based access control and enterprise data management across heterogeneous estates, including dynamic masking and attribute-based policy.

Access · MDM

How we choose

The selection framework

Tool selection is a two-week exercise if the questions are the right ones. Six criteria decide almost every case.
01

Estate topology

How many clouds, how many platforms, and where the data gravity actually sits. A single-cloud estate rarely justifies an independent catalog. A three-cloud estate rarely survives without one.

02

Enforcement or documentation

Whether governance has to be enforced at query time or recorded for audit. Platform-native tools enforce. Catalogs mostly document. Confusing the two is the most common failure.

03

Lineage depth required

Column-level or table-level, and whether lineage has to run into legacy ETL and stored procedure logic. This single question eliminates most of the market.

04

Regulatory surface

What a regulator will ask for and in what form. Basel, DORA, GxP, and HIPAA each imply different evidence, and the tool has to produce it without a manual assembly step.

05

Stewardship capacity

Who will actually own domains day to day. A governance tool with no stewards becomes an expensive inventory nobody updates.

06

AI dependency

Whether agents and retrieval will consume this data. If so, access boundaries and lineage stop being governance concerns and become architecture constraints.

Capabilities

What we deliver

Assess

Current state and gap
  • Data/metadata inventory
  • Lineage gap assessment
  • Policy, ownership & stewardship review
  • Regulatory requirements mapping
  • Tool fit assessment
  • Maturity benchmark & target state

Design

Operating model & architecture
  • Domain model & data products
  • Stewardship roles & decision rights
  • Policy & classification framework
  • Access & least-privilege model
  • Quality rules & SLAs

Implement

Build and integrate
  • Catalog deployment and metadata harvesting
  • Automated lineage extraction
  • Column-level lineage
  • Quality monitoring
  • Access controls & masking
  • AI governance integration

Operate

Sustain and prove
  • Stewardship adoption
  • Continuous quality management
  • Audit reporting
  • Policy drift monitoring
  • Governance managed services
  • Domain coverage expansion

Where this connects

Governance Is the Precondition for Agentic AI

An agent inherits every weakness in the data beneath it, and it inherits them at machine speed. Retrieval quality depends on classification. Access boundaries depend on the policy model. Model risk documentation depends on lineage that runs back to the system of record.

This is why we do not sell data governance as a standalone compliance program. It is built once and serves both obligations, and organizations that treat it as an audit exercise end up building it twice.

What that changes

  • AI Governance: Model risk, evaluation & oversight
  • Data Platforms: Governance enforcement layer
  • Vector Databases: Governed retrieval sources
  • Agentic AI: Access-controlled architecture
  • Database Engineering: End-to-end lineage to source systems

One governance layer, two obligations

Partner ecosystem

Cloud-agnostic by design. We follow the best technology for your business, not vendor allegiance.

Oracle

Microsoft

Google Cloud

Databricks

IBM

Start here

Find out what your lineage actually covers before an auditor does

The Governance Diagnostic is a structured review of your metadata, lineage coverage, policy model, and stewardship capacity. It produces a coverage map, a tooling recommendation against the six criteria, and a sequenced plan that starts with the domains where exposure is highest.

Request a Governance Diagnostic

What you walk away with

  • Metadata and lineage coverage map by domain
  • Gap analysis against your regulatory evidence requirements
  • Tooling recommendation with rationale, not a vendor pitch
  • Access model and stewardship capacity assessment
  • Maturity benchmark against comparable organizations
  • Sequenced remediation plan by exposure and effort

[email protected] · infolob.com