Large People ModelHuman Operating Architecture

Template & Working Tool · LPM Knowledge Object

Data Lineage Map

A map for tracing data from source to transformation, metric, decision, control, and AI use.

Mapv1.0.0Information

Problem it solves

Leaders cannot make confident decisions when information is duplicated, stale, conflicting, or hard to trust.

Who should use it

Outcome owners, transformation leads, and cross-functional teams

Estimated time

30–45 minutes for a first working session

Three-Step Quick Start

1Choose a critical metric, decision, or AI workflow.
2Trace source systems and transformations.
3Identify ownerless, stale, or uncertified data paths.

Open the public PDF

The PDF action is direct and public. All available packaged formats are also public and require no registration.

Object Overview

What this object is

Data Lineage Map is a reusable LPM knowledge object that helps organizations make the path of trusted information visible before teams automate decisions or scale AI outputs. It gives teams a structured way to make information visible, owned, and reviewable.

Why it matters

As companies scale AI, weak operating-model structures become amplified. This object helps prevent ai accelerates the spread of outdated or conflicting information. by defining ownership, flow, and handoff boundaries.

Layer Alignment

Where it fits in LPM

Primary LPM layer

Information Ecology

Defines how trusted information is created, maintained, accessed, refreshed, and used across the enterprise.

Supporting layers

No secondary layer assigned.

Why it belongs here

This object sits in Information because it turns information into a concrete artifact with owners, evidence, review cadence, and action paths.

Weakness it exposes

AI accelerates the spread of outdated or conflicting information.

Usage

How to use it

1Select the business area, workflow, platform, or AI initiative being assessed.
2Identify the accountable owner and required participants.
3Complete the working DOCX version with the team.
4Use the PDF as the reference guide.
5Capture decisions, gaps, risks, and owners.
6Convert outputs into backlog items, governance actions, or Lapemo onboarding inputs.
7Review on the recommended cadence: Quarterly or when sources change.

File Formats

Which file should you use?

PDF

Executive/reference version

Best for education, pre-read, sharing, and workshops.

DOCX

Editable working artifact

Best for facilitation, implementation, and client or internal completion.

Markdown

Website/source version

Best for publishing, documentation, and content reuse.

JSON

Structured knowledge object schema

Best for future Lapemo ingestion, scoring, validation, prompts, and workflows.

Outputs

What the organization should expect

Clearer ownership

Better decision traceability

Reduced ambiguity

Evidence-backed conversations

Better AI readiness

Better handoff into Lapemo later

Lineage map

Trust gaps

Data ownership actions

Advanced specification, company-size variants, and future product notes

Company Scale

How this changes by company size

500+ employees

Use this to create baseline clarity.

Focus on named owners, simple governance, and reducing informal workarounds.

Included in this object.

5,000+ employees

Use this to standardize across functions and platforms.

Focus on cross-functional ownership, decision rights, evidence, and repeatability.

Included in this object.

10,000+ employees

Use this to create enterprise control and reviewability.

Focus on federation, risk tiers, governance bodies, AI boundaries, and auditability.

Included in this object.

Artifact Content

Source artifact

The full artifact content below is rendered from the Markdown source packaged with Data Lineage Map.

Reusable LPM Knowledge Object · Information Ecology / Platform Structure

Use this template to map how critical data moves through the enterprise before it becomes a dashboard, metric, decision, control, workflow, AI model, or agentic action.

Core principles

Principle	Meaning
Lineage is proof, not decoration	A dashboard, AI output, or decision-grade claim is only reliable when the upstream source, transformation path, owner, and freshness are visible.
Every data movement needs a reason	Data should not move through integrations, spreadsheets, reports, warehouses, or AI tools without a defined purpose and accountable owner.
Source, transform, consume, act	The map must show where data originates, how it changes, who consumes it, and what decisions or actions it drives.
AI requires lineage discipline	AI should not retrieve, summarize, recommend, classify, or act on data unless the source, quality, sensitivity, and human review boundary are known.
Data ownership and system ownership are not the same	The business owner defines meaning and acceptable use. The technical owner maintains reliability, access, integration, and observability.
Stale lineage creates false confidence	Lineage must include refresh cadence, last validation date, transformation logic, confidence rating, and review triggers.
Exceptions must be visible	Manual exports, shadow spreadsheets, duplicate dashboards, broken integrations, and undocumented transformations should be treated as operating-model risk.

Required fields

Field	Definition	Required
Lineage object ID	Unique identifier for the data lineage object, domain, data product, metric, workflow, or AI use case	Yes
Business domain	Customer, employee, product, finance, risk, sales, delivery, operations, platform, or AI	Yes
Business question / use case	The decision, metric, process, report, AI use case, or control this data supports	Yes
Source system	Original system where the data is created or mastered	Yes
Source object / table / document	Specific table, API object, file, event, document, record, or data product	Yes
Source owner	Business owner accountable for meaning, accuracy, and approved use	Yes
Technical owner	System, data platform, integration, or engineering owner accountable for reliability	Yes
Data steward	Role accountable for definition, metadata, quality checks, retention, and lifecycle hygiene	Required when material
Transformation steps	Joins, calculations, enrichment, cleansing, aggregation, model features, or manual changes applied to the source	Yes
Transformation owner	Role accountable for transformation logic and approval	Yes
Integration path	API, ETL/ELT, event stream, file transfer, webhook, manual upload, RPA, or agentic workflow	Yes
Storage / processing layer	Warehouse, lakehouse, operational store, BI model, vector index, feature store, cache, or knowledge base	Yes
Downstream consumers	Dashboards, metrics, apps, teams, controls, workflows, models, agents, vendors, or customers that consume the data	Yes
Decision or action supported	What the data helps decide, automate, approve, escalate, report, or govern	Yes
Data quality checks	Completeness, accuracy, timeliness, validity, reconciliation, duplicate checks, or exception thresholds	Yes
Freshness window	How current the data must be to remain decision-grade or AI-safe	Yes
Sensitivity / classification	Internal, confidential, restricted, regulated, customer-sensitive, employee-sensitive, legal-sensitive, or public	Yes
Access rule	Who can view, edit, export, query, embed, retrieve, summarize, or automate against the data	Yes
AI usage boundary	Whether AI can retrieve, summarize, classify, recommend, transform, update, or act on the data	Yes
Human review rule	When a human must validate the data, output, or action before it is used	Required when material
Evidence link	Proof source for the lineage path, such as catalog link, data contract, pipeline run, dashboard definition, or control record	Yes
Known gaps / exceptions	Manual steps, undocumented transforms, duplicate sources, stale fields, broken ownership, or ungoverned AI consumption	Yes
Confidence rating	High, medium, low, provisional, stale, disputed, or retired	Yes
Review date	Date the lineage path must be reviewed again	Yes
Version	Object version, owner, last reviewed date, and change history	Yes

Data lineage stages

Stage	What it proves	Examples	Owners	Common risk
1. Create / master	Where the data is first created or legally mastered	CRM account, HRIS employee record, policy repository, Jira initiative	Business owner, system owner, data steward	Unclear master, duplicate records, missing owner
2. Capture / ingest	How the data enters the platform or workflow	API, stream, ETL job, form, file upload, vendor feed	Integration owner, platform owner	Manual exports, hidden spreadsheets, undocumented feeds
3. Transform / enrich	How the data is changed, calculated, joined, modeled, or cleaned	Metric formula, semantic model, feature engineering, data cleansing	Data product owner, analytics owner, engineering owner	Unapproved formulas, untested logic, stale joins
4. Store / index	Where transformed data is persisted or made searchable	Warehouse, lakehouse, BI model, vector index, feature store	Platform owner, data owner, security owner	Uncontrolled copies, retention gaps, shadow indexes
5. Consume / interpret	Who uses the data and through what experience	Dashboard, report, app screen, AI assistant, governance review	Consumer owner, product owner, analyst owner	Conflicting dashboards, no context, weak definitions
6. Decide / act	What decision, workflow, automation, or escalation depends on the data	Prioritization, risk review, customer action, agent handoff, control approval	Decision owner, process owner, control owner	AI acts without review, stale data drives decision
7. Evidence / audit	How the organization proves the data path and decision were valid	Data catalog, lineage graph, pipeline log, decision log, evidence checklist	Governance owner, audit owner, source owner	No proof, no replay, no confidence trail

Lineage object types

Type	Lineage path	Examples	Primary owners	Failure mode
Metric lineage	Source to formula to dashboard to executive decision	Revenue, cycle time, AI ROI, risk score, adoption rate	Metric owner, data steward, analytics owner	Competing definitions and dashboard drift
Customer lineage	Customer master to engagement to action	Account status, contract, support signal, renewal risk	Sales / success owner, CRM owner, data owner	AI outreach based on stale or wrong context
Employee lineage	HRIS to access, org design, capacity, and AI workforce planning	Role, manager, team, skills, access, cost center	People owner, HRIS owner, identity owner	Bad routing, access errors, shadow org structure
Work lineage	Work intake to prioritization to delivery and outcome	Initiatives, dependencies, blockers, delivery health, benefits	Product owner, PMO owner, work system owner	Hidden work and false delivery status
Governance lineage	Policy/control to exception to evidence and audit trail	Risk, control, policy, approval, incident, exception	GRC owner, risk owner, control owner	Unreviewed exception or missing evidence
AI model / agent lineage	Training or retrieval data to prompt/model to output to human review	Model features, RAG source, prompt, recommendation, automated action	AI owner, data owner, governance owner	AI output treated as fact without source trace
Knowledge lineage	Knowledge object to published artifact to AI retrieval and use	SOP, playbook, framework object, policy, prompt library	Content owner, knowledge steward, AI retrieval owner	Stale documents becoming enterprise memory

Template: data lineage map

Map element	Entry	Guidance
Lineage ID	[DL-001]	Unique object ID
Business domain	[Customer / Employee / Finance / Work / Risk / AI]	Used for routing and governance
Business use case	[Decision, metric, dashboard, process, AI use case, control]	Defines why the lineage matters
Source system	[System of record]	Original or authoritative source
Source object	[Table, API object, data product, document, event, file]	Specific object being consumed
Source owner	[Name / role]	Accountable for meaning and approved use
Technical owner	[Name / role]	Accountable for system and integration reliability
Transformation steps	[Logic, joins, formula, enrichment, model features]	How data changes before use
Integration path	[API / ETL / event / file / manual / agent]	How data moves
Storage / index	[Warehouse / lakehouse / BI model / vector index / feature store]	Where data persists or is retrieved
Downstream consumers	[Dashboards, teams, workflows, models, agents, vendors]	Who or what depends on this path
Decision/action supported	[Decision, automation, escalation, report, control]	What the data influences
Quality checks	[Freshness, accuracy, reconciliation, completeness, duplicates]	Evidence that path is reliable
AI usage boundary	[Retrieve / summarize / recommend / classify / act / blocked]	Defines safe AI behavior
Known gaps	[Missing owner, stale data, shadow copy, manual step]	Exception list
Review date	[Date]	Next validation date

Version for 500+ employee company

Dimension	Recommended pattern
Design intent	Create basic visibility before the company scales into duplicated tools, unowned dashboards, and AI pilots using weak data.
Minimum lineage scope	Map the top 10 to 25 critical data paths: customer, employee, finance, delivery, product, risk, and active AI initiatives.
Primary systems	CRM, HRIS, work system, finance system, knowledge base, BI tool, and AI pilot workspace.
Operating pattern	Quarterly lineage review with business owners, data steward, technology owner, and executive sponsor.
AI focus	AI can only summarize or recommend from named sources with human review for customer, employee, financial, risk, or external-facing use.
Red flags	Spreadsheet exports, dashboard-only truth, founder-memory definitions, unowned AI prompts, and unclear data freshness.

Version for 5,000+ employee company

Dimension	Recommended pattern
Design intent	Move from local data knowledge to domain-owned lineage that supports cross-functional decisions and scaled AI use cases.
Minimum lineage scope	Map critical data products, enterprise metrics, system integrations, decision dashboards, governance controls, and AI model inputs.
Primary systems	CRM, HRIS, ERP, work management, service desk, data warehouse/lakehouse, BI semantic layer, GRC, IAM, AI platform.
Operating pattern	Domain lineage owners, data product reviews, data catalog entries, quality thresholds, and formal exception paths.
AI focus	RAG sources, model features, agent workflows, and AI outputs must reference governed sources, data contracts, and review rules.
Red flags	Multiple dashboards for the same metric, undocumented transformations, BI logic outside catalog, and model/agent consumption without lineage.

Version for 10,000+ employee company

Dimension	Recommended pattern
Design intent	Create enterprise-grade lineage as a control layer for decisions, risk, AI, audit, regulation, and operating-model resilience.
Minimum lineage scope	Map all Tier 1 and Tier 2 data domains, regulatory/control data paths, AI-critical datasets, executive metrics, and external reporting feeds.
Primary systems	Enterprise data catalog, MDM, data lakehouse, semantic layer, API gateway, event platform, GRC, IAM, model registry, agent orchestration, observability.
Operating pattern	Automated lineage capture, lineage control board, domain data councils, evidence packs, policy-as-code checks, and supersession management.
AI focus	AI models and agents require retrieval lineage, feature lineage, prompt/output logging, human review policy, model risk tier, and audit replay.
Red flags	Federated business units creating conflicting truth, unmanaged data sharing, vendor feeds without ownership, and AI agents acting on low-confidence data.

Scoring logic

Dimension	Score	What good looks like
Ownership clarity	0-5	Every source, transformation, consumer, and decision path has a named owner.
Source clarity	0-5	The authoritative source and upstream origin are clearly defined.
Transformation transparency	0-5	Calculation, enrichment, joins, model features, and manual steps are documented and owned.
Consumer visibility	0-5	Dashboards, decisions, workflows, controls, AI models, and agents using the data are known.
Evidence strength	0-5	Lineage is supported by catalog links, pipeline logs, definitions, data contracts, and review history.
Freshness discipline	0-5	Refresh cadence and stale-data triggers are defined and monitored.
AI safety boundary	0-5	AI retrieval, recommendation, update, and action boundaries are explicit and reviewable.
Exception management	0-5	Manual steps, duplicates, shadow sources, broken feeds, and disputed lineage have clear escalation paths.

Suggested readiness score: average the eight scores, then classify 0-1.9 as Fragile, 2.0-3.4 as Developing, 3.5-4.4 as Governed, and 4.5-5.0 as AI-ready.

AI prompts

Given this data object, identify the source system, transformation steps, downstream consumers, decision dependencies, and missing owners.
Compare the declared source of truth against actual downstream dashboards, workflows, AI tools, and manual exports. Flag duplicate or conflicting lineage paths.
Score this lineage path from 0 to 5 across ownership clarity, source clarity, transformation transparency, consumer visibility, evidence strength, freshness discipline, AI safety, and exception management.
Identify whether this dataset is safe for AI retrieval, summary, recommendation, classification, or autonomous action. Explain the human review rule needed.
Generate a lineage exception report showing stale data, undocumented transformations, shadow copies, missing owners, weak evidence, and high-risk AI consumers.
Create a migration plan to move this lineage path from manual documentation to governed Lapemo ingestion.

Validation rules

Every lineage object must have one accountable source owner and one technical owner.
Every transformation must have a documented owner, purpose, and approval state.
Every downstream AI use must reference an approved source, freshness window, sensitivity rule, and human review boundary.
Every decision-grade metric must have a named source, formula, steward, quality threshold, and review date.
No dashboard, model, agent, workflow, or external report should consume data from an unowned or disputed lineage path.
Manual exports, spreadsheet changes, and shadow copies must be flagged as exceptions unless explicitly approved.
If data is stale, disputed, low-confidence, or missing evidence, it cannot be used as authoritative truth without owner approval.
Status must never be encoded only by color; use labels such as approved, provisional, stale, disputed, retired, or exception.

Lapemo ingestion mapping

Lapemo object	Fields / entities	Use
Knowledge object	Data Lineage Map	Canonical reusable artifact for lineage readiness and AI-safe data consumption.
Ownership object	Source owner, technical owner, steward, transformation owner	Connects lineage to accountability.
Information object	Source, transformation, storage, consumer, evidence	Connects lineage to enterprise knowledge and data products.
Platform object	Systems, integrations, APIs, pipelines, BI, model registry, vector index	Connects lineage to enterprise systems under control.
Decision object	Decision/action supported, evidence link, confidence	Connects data to decisions and outcomes.
Governance object	Sensitivity, access, control, exception, review date	Connects lineage to risk, compliance, and auditability.
AI object	AI usage boundary, human review rule, output log, agent consumer	Connects data lineage to AI governance and control.

Reusable knowledge-object model

This artifact should exist in four synchronized forms: a human-readable guide, a downloadable template, a machine-readable JSON object, and a guided Lapemo skill. The knowledge object should be versioned, reviewed, scored, and connected to system data over time. It should not auto-update silently. Lapemo should flag stale lineage, missing owners, broken evidence, new AI consumers, and high-risk exceptions for human approval.

Future Lapemo Use

The JSON schema turns data lineage map into software.

Lapemo can use this knowledge object as a guided workflow, scoring model, evidence record, governance input, and operating intelligence object. The schema is public for inspection and evaluation; production ingestion and governed execution remain separate product capabilities.