Data Cataloging & Business Glossary
Just like your data gives you insight into your business, metadata gives you insight into your data. By cataloging your data estate and enriching it with business context, you make data discoverable, understandable and trusted, for people and, increasingly, for AI.
In practice
Every data consumer asks the same three questions: what does this mean, where does it come from, and can I trust it? A data catalog and business glossary exist to answer them at scale. The catalog automatically harvests technical metadata from your sources, platforms and reports, and shows lineage: where data originates, how it is transformed, and where it is consumed. The glossary adds the business layer: agreed definitions for the terms your organization runs on, from 'active customer' to 'net revenue'.
The payoff is self-service analytics. When business users can find, understand and correctly use data independently, your data team stops being a bottleneck, your platform investment starts paying off in adoption, and 'shadow definitions' in local spreadsheets lose their reason to exist.
A mature catalog goes beyond inventory. The most valuable assets deserve to be packaged as data products: curated, governed combinations of data with an owner, a description, quality expectations, published in an internal data marketplace where consumers discover, understand and request access in one place. Data stops being something you hunt for and becomes something you shop for.
For organizations further in their maturity, we take this a step further with semantic layers and ontologies: turning agreed definitions into computable models that power consistent metrics across every report, and give AI agents and Copilot the business context they need to answer with your definitions rather than guesses. The glossary you build today is the grounding your AI needs tomorrow.
A data catalog makes data findable and traceable; a business glossary makes it mean the same thing to everyone. Together they turn a data estate into something people and AI can navigate with confidence.
The symptoms we see most often
- Analysts spend more time finding and decoding data than analyzing it.
- The same KPI has different values in different reports, and nobody can explain why.
- Knowledge about critical datasets lives in the heads of two people, one of whom is leaving.
- A catalog was bought once, filled once, and quietly died within a year.
- Copilot gives confident answers based on the wrong interpretation of your business terms.
- Everyone extracts their own copy of the same dataset, because there is no trusted, ready-to-use version to subscribe to.
These are metadata problems, and they compound quietly until an audit, a migration or an AI rollout makes them loud.
Our services
Data catalog implementation
We design and implement your catalog: connecting sources, configuring scanning and classification, and structuring the catalog around your data domains so it mirrors how your organization thinks.
Business glossary curation
Definitions are agreements, not documentation. We facilitate the working sessions where business and data teams settle terms, capture them with clear ownership, and link them to the physical data they describe.
Automated lineage
End-to-end lineage from source to report, automated where the platforms allow and engineered where they don't, so impact analysis and root-cause hunting take minutes instead of days.
Semantic layers & ontologies
We turn agreed definitions into computable models: semantic models that guarantee consistent metrics, and ontologies that describe how your business concepts relate, ready to ground AI agents in your reality.
Data products & marketplace
We help you move from inventory to offering: identifying the assets worth productizing, defining what a data product means in your organization (owner, description, quality expectations, terms of use), and publishing them in a data marketplace where consumers discover, understand and request access in one governed flow. The catalog tells you what exists; the marketplace makes the best of it consumable.
How an engagement runs
Fair warning: this is business-heavy work, not a purely technological project. The tooling is the easy part; the value comes from workshops with your data owners, decisions about definitions, ownership and priorities, and the change management that makes new habits stick. We facilitate exactly that, alongside the implementation.
Scope & connect
Prioritize domains and sources, connect them to the catalog, and get automated scanning running.
Curate with the business
Glossary sessions with the people who own the terms, and business contribution to the data products: which assets to productize, and what quality and terms consumers can expect. Stewardship assigned as definitions and products land.
Automate & enrich
Lineage, classifications, quality signals and AI-assisted descriptions enrich the catalog continuously.
Drive adoption
Onboarding, embedding the catalog in daily workflows, publishing the first data products in the marketplace, and measuring usage, because an unused catalog is shelfware.
Typical deliverables
What we work with
We work with the leading data catalog platforms: Microsoft Purview, OvalEdge, DataHub, Ataccama and Informatica, each with their own strengths. Technology choices always follow your architecture and ambitions, never the other way around.
- Microsoft Purview
- OvalEdge
- DataHub
- Ataccama
- Informatica
Frequently asked questions
We already have a catalog nobody uses. Can this be fixed?
Usually yes, and usually the cause is the same: the catalog was treated as a tool project instead of an adoption project. We re-anchor it in your domains, assign stewardship, curate the glossary with the business, and measure usage until it sticks.
How long before we see value?
A first domain with connected sources, curated glossary terms and lineage is typically live within a few months. We deliberately scope the first increment to something a business audience will notice.
What is the difference between a glossary and a semantic layer?
A glossary is for humans: agreed definitions in business language. A semantic layer is those definitions made computable, so every report and AI agent calculates 'net revenue' the same way. The glossary comes first; the semantic layer gives it teeth.
Know where you stand in a few weeks
The fastest first step is a Data Governance Maturity Scan: an objective view of your current maturity, a benchmark against peers, and a prioritized roadmap.