Data governance and data catalog for Azerbaijani enterprises — OvalEdge, deployed on-premise.
A governed catalog of every source, every owner and every hop — deployed on your perimeter and operated by Yukon Labs.
Governance fails quietly, then all at once.
Nobody decides to lose control of their data estate. It happens one undocumented pipeline at a time, and it only becomes visible when a regulator, an auditor or a board member asks a question that should be easy.
No owner, no definition
The same metric is calculated three ways in three departments. Each is defensible on its own; together they mean the number in the board pack cannot be reconciled.
Access approved by hand
Requests move through email and tickets. Nobody can answer who has standing access to sensitive tables today, let alone who had it last quarter.
Lineage lives in people
The two engineers who know how the reporting layer is assembled are the lineage documentation. Their calendar is the critical path for every audit.
Sensitive data in unexpected places
Personal data leaks into staging tables, exports and BI extracts. Without classification, the exposure is unknown rather than accepted.
Cost of inactionEvery audit becomes a fire drill staffed by your most senior engineers, every AI initiative stalls at the data-quality review, and the organisation carries an unpriced compliance risk it cannot describe.
One governed surface over every source.
OvalEdge captures active metadata across the estate, classifies what is sensitive, infers lineage automatically and enforces access with steward workflows — so governance is a property of the platform rather than a project.
AI-driven data catalog
150+ connectors with active metadata capture and semantic search, so business users find governed data by asking in their own words.
Automated classification
PII detection, sensitivity tagging and steward assignment run continuously instead of as a one-off discovery exercise.
End-to-end lineage
Column-level flows inferred from the systems themselves, so any number on a report can be walked back to the system it was born in.
Access control with workflow
Role-based enforcement with request, approval and revocation routed through Slack, Jira or ServiceNow — and logged.
askEdgi
A natural-language interface that lets business users explore governed data without a SQL seat or a ticket.
Deployed on your perimeter, run by us.
Yukon Labs deploys OvalEdge inside your network or sovereign cloud, connects it to your sources, and configures classification policy and steward workflows against your own compliance framework.
Governance rules are kept as code in your CI/CD, so a policy change is reviewed and versioned like any other change — not clicked into a console and forgotten.
We stay on as the operating team: connector upgrades, policy tuning, steward onboarding and audit support, with knowledge transfer to your team on an agreed schedule.
How lineage is actually assembled.
Lineage is not drawn by hand. OvalEdge reads the systems that already know — query logs, ETL definitions, BI models — and reconciles them into one graph that is refreshed as the estate changes.
1 · Connect
Read-only connectors attach to databases, warehouses, object storage, ETL tools and BI platforms. Nothing is copied — only metadata is collected.
2 · Classify
Profiling and pattern matching tag sensitive columns and assign candidate stewards, who confirm or correct in the catalog.
3 · Infer lineage
Query history and transformation definitions are parsed into column-level edges, then reconciled across tools so one graph spans the whole path.
4 · Enforce
Policies bind to catalog objects, so access decisions and masking follow the data rather than the system it happens to live in.
Frequently asked questions
What is data governance, and what does it actually change day to day?
Data governance is the operating model that decides who owns each data asset, what it means, where it came from and who may use it.
Day to day it changes four things: every important table has a named owner, contested terms like active customer have one approved definition, any figure can be traced back to its source columns, and access follows data sensitivity rather than old tickets.
What is OvalEdge — is it a database?
No — OvalEdge is not a database, and it does not store your data. OvalEdge catalogs metadata about the systems you already run — names, definitions, ownership, sensitivity and how data flows.
It connects with read-only credentials, reads structure and statistics, and writes only metadata to its own store. Your data never moves, which is the answer to the first question every security review asks.
Can OvalEdge be deployed on-premise or in an air-gapped environment?
Yes — OvalEdge can be deployed on-premise or in an air-gapped environment, and that is the normal deployment here rather than an exception, which is why it survives the first filter in Azerbaijani banking and government evaluations.
It needs its own database and network access to the systems it reads. Nothing requires internet connectivity at runtime.
How long does a typical OvalEdge implementation take?
A typical OvalEdge implementation takes six to twelve weeks to a working catalog for one domain, with harvested lineage and a glossary people use.
Installing, connecting and harvesting take three to four weeks and are largely mechanical. The rest is the human phase — definitions, ownership, classification — which slows down in proportion to how many domains you attempt at once.
Which data sources does OvalEdge connect to?
OvalEdge ships more than 170 pre-built native connectors, covering Oracle, SQL Server, PostgreSQL, Teradata, SAP, 1C, mainframe extracts, object storage, Snowflake, Databricks and the major BI tools.
Ask about your systems rather than the total, and ask specifically whether lineage is supported for each — many products can read a source's metadata but cannot parse its transformation logic.
Does a data catalog replace our data warehouse?
No — a data catalog does not replace a data warehouse. The warehouse stores and serves data; the catalog stores metadata describing that data and everything in your other systems. They are used together.
Deploying a catalog changes nothing about your pipelines or reports — it makes them findable, documented and traceable.
What is the difference between a data catalog, a data dictionary and a business glossary?
A dictionary describes one database's structure for developers. A glossary defines business concepts like net exposure for the business. A catalog is the platform that spans the whole estate and holds both.
What makes the catalog more than a document is the binding: each glossary concept is linked to the physical columns that implement it.
What is column-level data lineage, and why does it matter during an audit?
Column-level lineage traces a single column from its source, through every transformation, to the figure in a report — which is exactly the shape of the supervisory question show us how this number was calculated.
Without it the answer is a reconstruction from memory that takes days. Insist on lineage parsed automatically from your SQL: hand-drawn diagrams are misleading within a year.
Who should own data governance — IT or the business?
Data governance is owned by the business, with heavy technical support from IT.
IT can implement a catalog and harvest lineage; it cannot decide what active customer means. When governance sits entirely inside IT, definitions get made by people without the business context to make them, and the business ignores the result.
Do we need a data governance program in place before we start doing AI?
You do not need a full data governance programme before starting AI, but you do need governance on the specific data your AI will use — and that is more than most organisations have.
Gartner expects over 40% of agentic AI projects to be cancelled by end-2027, with inadequate controls among the causes. Catalog and classify the one domain your first use case depends on: a few weeks, not a year.
How does OvalEdge handle Azerbaijani, Russian and English metadata in one catalog?
OvalEdge holds trilingual metadata as one canonical concept carrying a label in each language, rather than three parallel glossaries.
`müştəri`, `клиент` and `customer` share no characters, so no automated matching will connect them — a person who knows the business in all three languages has to decide they are one concept. Design for this on day one; retrofitting it is expensive.
Does OvalEdge help satisfy Central Bank of Azerbaijan reporting requirements?
OvalEdge supplies the evidence CBAR requirements depend on, though no software makes an institution compliant on its own.
CBAR's Regulation on Information Security Management in Banks, in force since April 2022 and built on ISO/IEC 27000, expects asset ownership, classification and access control tied to it. A catalog produces that evidence from the systems themselves rather than from memory.
How do you measure whether a data governance program is working?
Measure a data governance programme by behaviour change, not by row counts. A catalog with fifty thousand documented assets and no users is a more expensive spreadsheet.
The metrics that mean something: analysts checking the glossary before building a report, engineers checking lineage before changing a schema, and how long it now takes to answer a regulator's question about a figure.
What does data stewardship involve, and how many stewards do we need?
A steward is a named person in the business accountable for a set of assets: approving definitions, confirming ownership, settling disputes. Roughly two hours a week.
Fewer than you expect — one per domain, so ten to twenty across a large bank. Make it part of an existing role, because a programme built around a few exceptional individuals collapses when two of them leave.
Is Yukon Labs an OvalEdge reseller or an implementation partner?
Yukon Labs is an OvalEdge implementation partner, not a reseller. We deploy, configure and operate OvalEdge inside your environment rather than selling a licence and leaving.
That means connectors against your real systems, lineage validated on your own transformation code, the multilingual glossary modelled correctly from the start, and the change management that decides whether the catalog gets used.