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.

Data Governance Program: 12-Month Roadmap

A quarter-by-quarter plan for a governance programme that survives year one: what to fund, who to staff, what to demonstrate at each gate, and what to do when it stalls.

Most data governance programmes fail in year one, and they fail in a recognisable way: nine months of framework design, a catalog purchase, a steering committee, and nothing an executive can point at. The budget is not renewed, and the organisation concludes that governance does not work here.

The programmes that survive are sequenced differently. Each phase produces something visible enough to fund the next one, and the first visible thing arrives in month two rather than month nine. That is the entire design principle behind what follows.

This is a plan for the person who has been handed the mandate, has a budget that must show results within a year, and cannot reorganise the company to get them.

Phase 0: before the clock starts

Three things have to be true before month one, and a programme that starts without them is starting with a handicap it will not recover from.

A named executive sponsor with a stake in the outcome. Not an approver — someone whose own reporting or risk position improves if this works. The CFO who signs the regulatory submission, or the CRO who answers for the risk numbers. Governance sponsored by IT alone has a well-documented failure rate.

A single named programme lead. One person, not a committee. Their job is to make decisions happen, not to make them.

A written scope that says what is excluded. The most useful sentence in the charter names the domains you are not touching this year. Without it, scope arrives by accretion and the programme spreads until nothing is finished.

If you cannot get the sponsor, do not start the programme. Do the diagnostic described at the end of this article instead, and use it to get one.

Quarter 1: inventory and the first real artefact

Pick one domain. Customer data in a bank, citizen registry data in a ministry, claims data in an insurer. One domain deep beats five domains shallow, every time, and the reason is that shallow coverage cannot demonstrate anything.

Inventory the systems in it. Not every table — the systems, their owners, roughly what they hold, and how data moves between them. This is interview work as much as technical work, and it produces the first genuinely useful artefact: a map of a territory that previously existed only in fragments across people's heads.

Stand up the catalog and connect it. Harvest technical metadata and lineage automatically from the source systems in scope. Do not have humans type metadata a connector can extract; an organisation that populates a catalog manually has bought a wiki.

Gate at the end of Q1: a system inventory and a data flow diagram for one domain, plus harvested technical metadata. Show it to the sponsor. This is the first evidence that the programme produces things.

Quarter 2: definitions, and the arguments they start

Define the fifty to eighty terms that matter in this domain. Drafted from existing usage — report specifications, code comments, the analyst who maintains the spreadsheet — not written from first principles by the governance team. Multilingual from the start in this market: one canonical concept with Azerbaijani, Russian and English labels, not three parallel glossaries.

Expect this quarter to run slower and hotter than planned. Definitions are where governance stops being technical: two departments will discover they have been using active customer differently for years, both defensibly, and someone has to decide. That argument is the point, and resolving it is worth more than the catalog it lives in. The detail is in business glossary.

Bind every approved term to physical columns. Unbound definitions are prose and change no behaviour.

Keep a decision log. What was disputed, what was decided, by whom, on what date, and why. The same argument returns in eighteen months with new people, and the log is what stops it being re-litigated from scratch.

Gate at the end of Q2: an approved, bound, multilingual glossary for one domain and a decision log.

Quarter 3: ownership, stewardship and quality

Assign a named owner to every asset in scope. Named individuals, not departments. Ownership assigned to a team is ownership assigned to nobody.

Stand up a small governance forum. Six to eight people, meeting fortnightly, with authority to settle disputes. Not a committee of twenty that meets quarterly and escalates everything — that structure produces minutes rather than decisions.

Define quality rules where they are measurable and consequential. Completeness, validity, timeliness, referential integrity on the assets that feed regulatory reports. Start with rules that can be checked automatically and that someone will actually act on when they fail. An alert nobody owns is noise: every rule needs a named recipient and an agreed response.

Gate at the end of Q3: ownership assigned across the domain, a functioning decision forum with a visible decision rate, and automated quality checks with routed alerts on the top twenty assets.

Quarter 4: prove it, then extend

Take one regulatory report and trace it end to end through the catalog — from the submitted figure back to every source column, including the honest gaps where a spreadsheet breaks the chain. Column-level, harvested rather than drawn. See data lineage explained.

Demonstrate it in front of the people who answer for that figure. The CFO, the head of risk, whoever signs the submission. The demonstration is the deliverable; the graph is just the medium.

That demonstration is what funds year two. It converts governance from an abstraction into a capability the institution can see, and it is the single most reliable fundraising event in the whole programme.

Gate at the end of Q4: a demonstrated end-to-end lineage trace and a scoped plan for the next domain.

The team, honestly

Smaller than most plans assume, and the shape matters more than the size.

The programme lead, full time. A data architect or engineer, full time, owning the platform and the harvesting. Stewards in the business, two hours a week each, six to ten of them for one domain. The owners, senior business people, perhaps two hours a month. The forum, fortnightly, an hour.

That is roughly two full-time equivalents plus distributed part-time effort, and it is deliberately small. Programmes that start with a team of twelve spend their first quarter organising themselves.

The talent constraint in this market is real: there are not many experienced data stewards in Azerbaijan, and a programme built around a handful of exceptional individuals collapses when two of them leave. Design for it — make stewardship a defined part of existing business roles rather than a new specialist career, keep the per-steward load genuinely small, and automate everything automatable so human attention goes only where judgement is required.

Budget shape

Three lines, in rough proportion for a first year covering one domain.

Platform licence — the catalog. Meaningful, and not the largest line for most first-year programmes.

Implementation — connecting sources, configuring harvesting, building the initial glossary. For a scoped domain this is measured in weeks rather than quarters with a value platform, and in quarters with an enterprise suite. That difference is frequently large enough to fund the entire stewardship function, which is the most common reason organisations in this market choose the value option. The comparison is in OvalEdge vs Collibra vs Alation.

Internal effort — the two FTEs and the distributed steward time. Almost always understated, and it is the line that determines whether the programme is real.

Deliberately absent: a large first-year training budget, an enterprise-wide rollout, and any line item for a framework consultancy that produces a document.

When it stalls — and it will

Four common stalls, each with a specific response.

Definitions deadlock. Two departments cannot agree and the forum keeps carrying the item. Response: name the decision-maker for that domain in advance and impose a deadline. A definition carried to the next meeting is carried forever.

Stewards stop showing up. Almost always because their manager does not count it as work. Response: escalate to the sponsor, not to the stewards. This is a management problem wearing a governance costume.

Harvesting fails on the legacy system. The mainframe or the 1C instance will not yield lineage. Response: represent the gap honestly in the graph rather than papering over it, and proceed. A chain with an honest break is more useful to an auditor than a continuous chain that guesses.

Scope creep from a second domain. A senior person wants their area included. Response: add it to the year-two plan in writing, at the gate, in front of the sponsor. Saying no is easier when the plan already has a place to put it.

What to measure

Not row counts. Four behavioural measures, all of which can be reported to a board.

Catalog usage — unique users and searches, trending. Time from definition dispute raised to logged. Whether a regulatory figure can be traced on demand without preparation, which is binary. And the number of duplicate builds avoided, which is the direct countable cost of governance not existing.

How to start without a budget

The most common blocker is that the business case requires evidence and the evidence requires the programme.

Break the loop with a scoped diagnostic: one domain, four to six weeks, producing a system inventory, a sample of harvested lineage for one real report, and a documented gap assessment against your regulatory obligations. That artefact is what a board approves against — not a vendor deck.

Key points

Sequence so each phase funds the next. The first visible artefact must arrive in month two, not month nine.
Phase 0 requires a sponsor with a stake, one named lead, and a written scope that says what is excluded.
Q1 inventory and harvesting, Q2 glossary bound to columns, Q3 ownership and quality, Q4 an end-to-end lineage demonstration in front of the people accountable for the figure.
Roughly two FTEs plus distributed part-time stewardship. Teams of twelve spend the first quarter organising themselves.
Expect four stalls: definitions deadlock, absent stewards, legacy harvesting failure, scope creep. Each has a specific response and none is a surprise.
Measure catalog usage, dispute resolution time, on-demand traceability and duplicate builds avoided — never row counts.

We run this programme with OvalEdge deployed on-premise, and the governance assessment produces the Phase 0 diagnostic. Related reading: what is data governance and data governance in Azerbaijan.

Building a Data Governance Program: A 12-Month Roadmap