Description

A focused course, tailored for you

The Enterprise Architect's Course on Building a Unified Data Catalog When Legacy Silos Stifle Innovation

Turn fragmented data stores into a single source of truth so you can deliver AI projects on schedule and keep leadership confident.

Stop spending every Friday afternoon hunting for missing schema definitions while AI project deadlines keep slipping.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

You spend weeks hunting for the latest schema definitions across multiple spreadsheets, SharePoint sites, and undocumented APIs. Every new AI model request forces you to rebuild data lineage maps from scratch, delaying delivery and raising doubts from the CFO.

Your current tooling - a mix of ad-hoc Excel trackers, email threads, and siloed governance portals - creates endless hand-offs and manual reconciliations. When the quarterly audit arrives, the evidence pack is incomplete, and senior managers question whether the data architecture can support the next growth wave.

If this continues, you risk missing the strategic AI rollout deadline, losing budget credibility, and seeing your role reduced to fire-fighting data chaos rather than shaping enterprise-wide strategy.

What you walk away with

Produce a live data catalog that is searchable and trusted by all stakeholders.
Document end-to-end data lineage for any AI model in under two days.
Create a reusable governance checklist that passes audit without extra effort.
Establish a quarterly cadence for data quality reviews with clear ownership.
Communicate a strategic data roadmap that secures executive funding.

The 12 modules

Module 1. Mapping the Current Landscape

Identify every data source, owner, and existing documentation artifact.

Module 2. Designing a Unified Metadata Model

Define the taxonomy and attribute set that will power a single catalog.

Module 3. Automating Ingestion Pipelines

Set up scripts to pull metadata from databases, data lakes, and APIs.

Module 4. Building the Data Catalog Interface

Configure a searchable UI that surfaces lineage and ownership details.

Module 5. Establishing Governance Workflows

Create approval routes and RACI tables for new data assets.

Module 6. Integrating with AI Model Pipelines

Link catalog entries to model training jobs and feature stores.

Module 7. Generating Audit-Ready Evidence Packs

Produce ready-to-submit documentation for quarterly reviews.

Module 8. Implementing Data Quality Dashboards

Deploy visual scorecards that track completeness and freshness.

Module 9. Running Quarterly Data Governance Cadence

Facilitate a repeatable meeting rhythm with clear action items.

Module 10. Communicating Impact to Leadership

Craft executive briefs that translate catalog metrics into business value.

Module 11. Scaling the Catalog Across Business Units

Roll out the solution to new domains with minimal rework.

Module 12. Continuous Improvement and Roadmap Planning

Embed feedback loops to evolve the catalog as the enterprise grows.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Module 1 covers Mapping the Current Landscape , exactly the inventory sprint you face when senior management asks for a complete source list on short notice.

Module 5 covers Establishing Governance Workflows , precisely the approval bottleneck you hit when new data assets need rapid onboarding for an AI model.

Module 7 covers Generating Audit-Ready Evidence Packs , the exact step you need when the quarterly audit committee demands a clean data provenance report.

What you get with this course

A populated metadata model template with example attributes.
A reusable data ingestion script library.
A ready-to-launch data catalog UI mockup.
A governance RACI matrix for data owners.
An audit evidence pack checklist.
A data quality scorecard dashboard example.
A quarterly governance meeting agenda.
An executive briefing slide deck template.
A rollout plan checklist for new business units.
A continuous improvement log sheet.

What you will have in hand by Day 1, Week 1, Month 1

Day 1: tailored playbook in hand, metadata model template pre-populated for your environment, ingestion script starter kit ready.

Week 1: first version of the data catalog live with core sources, evidence pack checklist completed for the upcoming audit.

Month 1: quarterly governance cadence operating, data quality dashboard reporting to leadership, and a rollout plan for additional business units.

Before and after

Before

Your data landscape lives in scattered Excel sheets, old Confluence pages, and email threads. Evidence for audits is assembled manually, often missing recent pipelines, and the team loses days each month reconciling inconsistencies. Leadership sees only fragmented views and questions the reliability of AI inputs.

After

A live, searchable data catalog is populated with auto-ingested metadata, complete lineage, and ownership tags. Quarterly governance meetings run on a shared dashboard, and audit evidence packs are generated with a click. You now discuss strategic AI roadmaps with confidence, backed by documented data assets.

What happens if you do not address this

If you ignore this now, the next AI rollout will be delayed by weeks, the audit committee will request a remediation plan, and your credibility with the CFO will erode. The lack of a unified catalog will force the team to continue manual reconciliations, draining resources and jeopardizing the upcoming budget cycle.

Who it is for

An enterprise architect who spends most of the week aligning business and technology teams, curating data dictionaries, and coordinating governance workshops. You operate in a fast-moving org where AI initiatives are top-priority, but your data assets remain scattered across legacy systems and undocumented spreadsheets.

Who this is NOT for. This is not for someone who needs a basic introduction to data catalog concepts or a vendor recommendation instead of an operating method.

How it arrives

Within 24 hours of purchase your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it. The playbook is hand-built around your specific situation, not LLM-generated boilerplate.

Time investment. 6 hours of focused work spread over a week, saving an estimated 40-60 hours of internal scaffolding effort.

Why $199 is the right number

A half-day consultant would charge $2K-$5K to map your data landscape, a generic data governance certification runs $800-$2K, and building the catalog yourself can consume 60+ hours. For $199 you get a proven method, reusable artefacts, and a tailored playbook that delivers faster ROI.

FAQ

Do I need prior experience with specific catalog tools?

No, the course works with any catalog platform and provides generic configurations you can apply.

Will the course cover how to get buy-in from data owners?

Yes, modules on governance workflows and executive communication address stakeholder alignment.

What if my organization already has a partial data inventory?

The playbook helps you consolidate existing artifacts into the unified catalog without starting from scratch.

How much time will I need each week to complete the course?

Allocate about 3-4 hours per week for focused work and you’ll finish in a month.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.