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The 9 things a data governance leader does week to week, the 5 decisions they own, the artifacts they answer for, and the metrics the job is judged on.
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A data governance leader is accountable for whether an organization can answer four questions about any number it relies on: where the number came from, who owns it, how sensitive it is, and whether it can be trusted today. They do not build the pipelines that produce the number and they usually do not own the systems it passes through. They own the answer.
That is a narrower job than most role descriptions suggest and a harder one. The governance leader has to make a statement about data that other teams control, and stand behind it in front of an auditor, a regulator or a board. The activity people associate with the role, writing policies and chairing committees, exists only to make that statement possible. If you want the discipline itself defined rather than the job, the data governance concepts guide covers the scope, the pillars and the vocabulary.
These are the nine things that recur every week in the job, whatever the title on the contract says. Read the table first, then the sections underneath for what each one looks like when it is working and what it looks like when it is not.
| Responsibility | What it looks like in a normal week | How you know it is failing |
|---|---|---|
| 1. Trace numbers on request | Answer where a figure in a report came from and what changed since the last run | The answer takes days and needs three people in a call |
| 2. Keep the critical data element register current | Add, retire and reassign ownership of entries as products and reports change | The register is a spreadsheet last edited by someone who has left |
| 3. Classify data | Rule on the sensitivity of new columns and datasets, and review what the automation flagged | Sensitive columns reach a dashboard before anyone classifies them |
| 4. Rule on new uses of data | Approve, restrict or refuse a request to use a dataset for a new purpose, including AI training and external sharing | Teams stop asking because the answer takes too long |
| 5. Set and defend quality thresholds | Agree the acceptable null rate, freshness window and volume range on each critical element with its owner | Thresholds exist only in a monitoring tool and no business owner has agreed to them |
| 6. Run the governance forum | Chair it, publish decisions and clear the backlog of open questions | The forum meets and decides nothing, so the same item returns for a fourth time |
| 7. Review change requests with downstream impact | Read the schema and pipeline changes queued for release and flag the ones that break a regulatory or executive report | You find out about the change from the broken report |
| 8. Produce audit and regulator evidence | Answer an evidence request with lineage, control records and ownership, not screenshots | Evidence is assembled by hand under deadline pressure every time |
| 9. Report the governance scorecard upward | Take the same small set of numbers to the executive or risk committee every month | The report changes shape every month, so no trend is visible |
The most frequent request the role receives is a version of the same sentence: this figure looks wrong, where does it come from and what changed. It sounds like a question for an analyst. It arrives at governance because the answer crosses systems that no single team can see end to end.
Doing this well means having column level lineage from the source system through ingestion, transformation and the warehouse to the report, and being able to read it without opening a ticket. Doing it badly means assembling engineers, analysts and whoever last touched the pipeline into a call and reconstructing the path from memory. The data lineage concepts guide explains what the different grains of lineage give you and where table level tracing stops being enough.
Governance that covers every table covers nothing. The working scope is a register of critical data elements: the fields that feed regulatory reports, executive metrics, pricing, risk models and anything customer facing. Most organizations of any size land somewhere between 200 and 2,000 of them, which is a small fraction of the column count and the entirety of the exposure.
Each entry needs four things: a business definition, a named owner who is a person and not a team, a classification, and the downstream reports it feeds. The register is a living object. Products launch, reports get retired, owners move. A register nobody has edited in six months is a register that is already wrong.
Classification decides what may be done with a column: who can see it, whether it is masked, how long it is kept, whether it may leave the region, whether it may go into a model. Automated pattern rules catch the obvious cases such as a column named national_id, and they miss free text fields holding personal data by accident. The governance leader rules on the ambiguous ones and reviews what the automation decided. Getting this right depends on metadata being complete in the first place, which the metadata governance guide covers in detail.
A team wants to join customer records to a third party dataset. A vendor wants an export. A data science group wants a table for model training. Each request is a use, and each use has to be checked against the lawful basis, the retention terms, the classification and whatever the customer was told.
The measure of health here is response time rather than approval rate. When a ruling takes three weeks, teams stop asking and start copying data quietly, and the governance function loses the visibility it was created to have. A working service level is a decision inside five working days, with a same day answer on anything that is clearly inside an existing policy.
A threshold is a number a business owner has agreed to. The null rate on this column stays below 2 percent. This table lands by 06:00. Daily volume stays inside 10 percent of the trailing four week average. Without an agreed number, a data quality alert is an opinion, and the first person to challenge it wins. With one, the conversation moves from whether the data is bad to what happens now. The relationship between data quality and governance is where most of this work actually lives.
Defending a threshold matters as much as setting it. Pressure to raise a limit arrives the week a release is late, and raising it quietly is how a control stops meaning anything. The record of who asked, what was decided and why is part of the evidence trail.
The forum is where contested decisions get made: two teams claiming the same definition, an owner refusing a classification, a request that no existing policy covers. It works when it is small, when it meets on a fixed cadence and when every item arrives with a recommendation attached rather than an open question.
The single measure of a governance forum is the age of its oldest open decision. A forum carrying items older than 60 days is not a decision body, and the organization has already learned to route around it.
Schema changes, pipeline rewrites, source system migrations and vendor swaps all move data under reports that other people are accountable for. Governance rarely has a veto here and rarely needs one. What it needs is to see the change before it ships and to be able to say which regulatory report, executive metric or model sits downstream of it.
This is the one responsibility that turns the role from reactive to preventive, and it is the one most often skipped, because it requires being wired into the release process rather than the incident process.
Evidence is a record that the control ran, on which data, with what result, reviewed by whom. A description of the control is not evidence. In practice the requests are specific: show the lineage for this reported figure, show who has had access to this table for the last twelve months, show that this classification policy was applied to every source holding personal data. The gap between an organization that can answer in an afternoon and one that needs a fortnight is almost entirely a tooling question, which is why the data governance platform a team runs on is a governance decision and not only a procurement one.
The role reports to an executive committee, a risk committee or a board, and the value of that report comes from repetition. The same small set of measures, in the same order, every month, so the shape of the trend is visible. A scorecard that changes format is a scorecard nobody can read a trend from, and it reads as a function presenting its activity rather than its position.
The fastest way to understand any governance role is to ask what happens when the person says no. Five rulings are theirs to make and stand behind. Five more they recommend and somebody else signs. Three they must never take alone, however much pressure there is to settle it quietly.
| Decision | Who decides | What the governance leader does |
|---|---|---|
| 1. The classification of a data element | Governance leader | Rules on sensitivity and the handling rules that follow from it, including masking, retention and whether the element may be used to train a model |
| 2. Whether a dataset is certified for reuse | Governance leader | Decides whether an asset carries the certified marker other teams rely on, and withdraws it when the evidence stops holding |
| 3. Who is recorded as the owner of a dataset | Governance leader | Assigns and reassigns named ownership, and escalates when a nominated owner declines |
| 4. Whether a threshold breach is an incident | Governance leader | Decides whether a quality signal crossing its limit opens a formal incident with an owner and a clock, or is noted and closed |
| 5. Whether the evidence answers the question asked | Governance leader | Decides what goes back to an auditor or regulator and whether it is sufficient, which is the ruling the role is most exposed on |
| Which platform the organization buys | Executive sponsor or CDO | Runs the evaluation, writes the requirements and recommends. Does not sign |
| Headcount in a domain team | The domain leader | States what coverage the register needs and what it will cost to skip it |
| Retention period changes | Legal or the data protection officer | Supplies the record of what is held where, and implements the outcome |
| Remediation funding | The business owner of the affected process | Quantifies the exposure and the rework, and sequences the fix |
| Whether a release ships with a known data defect | The product or release owner | States the downstream impact in writing before the decision, and records it after |
| Accepting a known breach of a regulatory obligation | Escalate every time | Never absorbs this. It goes to risk, legal and the accountable executive, in writing |
| A new use of personal data with no lawful basis | Escalate every time | Refers to the data protection officer. Silence is not an approval |
| Overriding a data owner who has refused | Escalate every time | Takes it to the forum or the sponsor. A governance function that overrules owners loses them |
A role description that gives the governance leader none of the first five is an administrative post with a leadership title, and it will not survive contact with an incident. One that gives them the middle five as well is a role with authority it cannot exercise, because the budget and the headcount sit elsewhere. If you are hiring, the first five are the test.
These three roles are confused constantly, including inside job adverts. They differ on what they are accountable for when something goes wrong, which is the only distinction that survives contact with an incident.
| Question | Data governance leader | Data engineering lead | Chief data officer |
|---|---|---|---|
| What are they accountable for | Whether the organization can answer for its data | Whether the data arrives, on time and in the agreed shape | Whether data creates value and the risk from it is inside appetite |
| What they own | Classification, certification, ownership records, evidence | Pipelines, platform, cost, reliability | Strategy, budget, the operating model, the executive relationship |
| Who they answer to | CDO, chief risk officer or chief operating officer | CTO or head of platform | CEO, CFO or the board |
| Their first move in an incident | Establish what is affected downstream and who has to be told | Restore the pipeline and stop the bleeding | Decide what is said externally and what it costs |
| What they are judged on | Trace time, coverage, evidence readiness, decision backlog | Uptime, freshness, throughput, cost per query | Business outcomes and regulatory standing |
| Typical team size | Small, often 2 to 6, mostly stewards and analysts | Large, scaling with the number of sources and pipelines | The function, including governance and engineering leadership |
| Can the role be combined | With the CDO in a firm under about 500 people | Rarely combined with governance without losing independence | Combined with CIO only where data is not a regulated exposure |
The combination worth avoiding is governance reporting into engineering. The engineering lead is measured on delivery, and a governance function inside that reporting line will be asked to soften a finding sooner or later. Where the split between the executive roles is the question, the CIO against CDO comparison sets out how those two divide.
A governance leader is judged partly on objects that have to exist, be current, and be producible on request. These are the ones that come up in every audit and most board questions.
On cadence: the forum works fortnightly in a large organization and monthly in a smaller one, the scorecard is monthly, the register review is quarterly, and the exception register is reviewed on the same cycle as the forum. Weekly governance meetings are almost always a sign that the decision rights are unclear, because a clear one gets settled by the owner without a meeting.
Two of the four highest ranking pages on this subject have a section about measuring governance and neither of them attaches a number to anything. These seven have targets worth committing to in front of an executive committee.
| Metric | How to measure it | A target worth committing to |
|---|---|---|
| 1. Time to trace a number | Median hours from the question arriving to a traced answer, across the last 20 requests | Under 4 hours, from a starting point that is usually measured in days |
| 2. Ownership coverage of the critical register | Share of registered critical elements with a named individual owner | 100 percent of the register, which is a far smaller and far harder target than a percentage of all tables |
| 3. Classification coverage | Share of connected sources where the classification policy has been applied and reviewed | 100 percent of sources, with every exception logged and dated |
| 4. Monitored share of the register | Share of critical elements carrying at least one agreed quality threshold that is live | 90 percent within two quarters of the register existing |
| 5. Lineage coverage on regulatory paths | Share of reported figures with column level lineage from source to report | 95 percent on regulatory reporting paths before anything else is covered |
| 6. Audit evidence response time | Median working days from an evidence request to a complete answer | Under 5 working days, without assembling anything by hand |
| 7. Decision backlog age | Age of the oldest open item in the governance forum | Nothing older than 60 days, and no item returning for a third hearing |
Three measures to refuse when somebody asks for them. Number of policies published, because writing a document is not a control. Glossary term count, because a glossary nobody reads scores the same as one everybody uses. Catalog page views, because traffic to a tool is not trust in the data. Each measures effort spent, and a governance function that reports effort instead of position gets cut in the first cost review.
Most of the work of governance belongs to stewards, owners and engineers. Four things stay with the leader whatever the team size.
The responsibilities above describe the shape of the job. This is what living inside it feels like on an ordinary day at a large financial institution, where the role sits at the intersection of risk, technology and business pressure, and is usually called on once something has already gone wrong.
The day starts with dashboards. Risk metrics, regulatory reports and the KPIs the executive committee reads. Everything looks fine until it does not, and a message arrives: there is a mismatch in one of the numbers, can you help us understand where it is coming from. That is a simple question and it should be answered in minutes. It rarely is.
The question is almost always the same one. Where does this number come from, and what changed. That is where the real work begins. There are source systems, ingestion pipelines, transformations, warehouses and BI dashboards, and sometimes machine learning models consuming the same data. What there is not is a single reliable view of how the data flows end to end. So the digging starts.
You start pulling people in. An engineer who understands the upstream pipeline. An analyst who owns the dashboard. Somebody from the platform team. Occasionally somebody who used to work on this. Documentation exists but it is out of date. Lineage exists but only partially. Ownership exists but not always clearly.
What should have been a five minute answer turns into a two to five day exercise. This does not happen once or twice a year. It happens several times a month, and across a year governance spends weeks reconstructing data flow that a tool could have shown in seconds.
The part people outside the function do not always see is when governance arrives. It is usually not involved when pipelines are built or models are trained, because it is an oversight function rather than an execution team. When something breaks, a report, a metric or a control, governance is pulled in late and asked to explain what happened.
By that point the issue is already visible, business confidence is already shaken and risk teams are already asking questions. The function is expected to provide assurance about something it never had visibility of upstream. The problem is structural rather than personal: without visibility, governance can only react.
In a financial institution every data issue carries the same unspoken question. What happens if an auditor asks about this. So while teams are fixing the immediate problem, the governance leader is already thinking ahead. Can we show lineage. Can we prove the controls were applied. Can we explain the downstream impact.
Too often the audit evidence turns out to be screenshots, manual walkthroughs and spreadsheets built under deadline pressure. At that point governance has become a documentation exercise rather than a layer anybody trusts.
The biggest cost is trust, ahead of the time and the effort. Business teams lose confidence in the numbers, engineers get pulled into repeated investigations, governance gets a reputation for being slow or obstructive, and everybody becomes more risk averse. None of that happens because people are doing a bad job. It happens because the system lacks visibility.
Now the same day, with the same question arriving. Where does this number come from, and what changed. This time the full data flow from source to consumption is visible, the column that changed is identifiable and so is where it changed, the affected reports and models are already listed, and ownership is clear. No war room, no guessing, no scrambling.
With that visibility the job changes shape. Governance becomes preventive rather than reactive. Impact can be assessed before a change goes live. Data usage can be signed off with confidence rather than hope. Audits become evidence based rather than narrative based. The function stops being a bottleneck and starts acting as a layer other people rely on.
The change that matters is the move from governing on assumptions to governing on evidence, rather than more policies or more tools. End to end visibility of data flow does not make governance louder. It makes governance credible.
In banking and financial services a data issue is never only an analytics problem. Metrics drive regulatory reporting, capital and risk decisions, and machine learning amplifies the consequences of bad inputs. Trust is not optional, and trust without visibility is hope.
That is not only an internal view. The Basel Committee on Banking Supervision published Principles for effective risk data aggregation and risk reporting on 9 January 2013, known in most banks as BCBS 239. It sets out fourteen numbered principles across five sections, and the first two, governance and data architecture, sit before any of the aggregation or reporting requirements. Principle 3, on accuracy and integrity, is explicit about automation:
Data should be aggregated on a largely automated basis so as to minimise the probability of errors.
A governance leader in a supervised bank is therefore not arguing for tooling on preference. Manual reconstruction of data flow is the thing the standard names as the risk. The same logic reaches other supervised markets Decube works in, where OJK in Indonesia, APRA in Australia, MAS in Singapore and the NAIC insurance framework in the United States all expect a firm to evidence where a reported number came from rather than describe the process that produced it.
What that is worth in practice shows up in the published results. Among the five case studies on the Decube customer case studies page, the Australian financial institution records mean time to resolution down 72 percent and more than 15,000 US dollars saved per week, and the Indonesian digital bank records 95 percent column level lineage coverage with 55 percent lower mean time to resolution. Those are the two numbers a governance leader in a regulated firm can take to a sponsor.
This is the question the role now gets asked most often, and the two are being merged in job adverts that should not merge them. Data governance answers for the data. AI governance answers for the systems that act on it. One is a precondition for the other, which is why the same person frequently ends up holding both, but the accountabilities are different.
| Question | Data governance | AI governance |
|---|---|---|
| What is being governed | Datasets, columns, definitions, access | Models, agents, prompts, the decisions they take |
| The central artifact | The critical data element register | The AI system inventory with a risk class per system |
| The main control | Classification, quality thresholds, lineage, access | Risk classification, testing before release, monitoring after, human oversight |
| What failure looks like | A wrong number in a report nobody can trace | A decision affecting a person that nobody can explain or reverse |
| Who is usually accountable | Data governance leader, reporting to the CDO or risk | A cross functional body, often chaired by risk or legal with the CDO on it |
| The regulatory clock | Continuous, under data protection and sector rules | EU AI Act: transparency duties under Article 50 from 2 August 2026, high risk obligations from 2 December 2027 standalone and 2 August 2028 embedded in regulated products |
The practical consequence for the role is that AI governance cannot be built first. An agent asked to act on data needs the same four answers a human analyst needs, and it needs them programmatically: where the value came from, who owns it, how sensitive it is and whether it can be trusted right now. An organization without a current register, applied classification and column level lineage cannot supply those answers to an agent at all.
The video below is a four minute explainer that separates the two disciplines and names seven pillars of an AI governance program, including inventory, accountability, risk classification, governed data, testing, continuous monitoring and auditability. It closes on five common mistakes, one of which is running data governance and AI governance as separate silos.
If the governance program and the AI program are being planned together, the agentic AI data governance article covers what changes when the consumer of the data is an agent rather than a person.
Every page ranking for this subject describes the role as it should work. These are the four ways it does not, all of them common and none of them the fault of the person in the seat.
The role is asked to certify data it never saw being produced, using documentation written by people who have moved on. Accountability arrives without the instrumentation to support it, and the function can only react. This is the default state and it is why lineage and quality evidence are a governance requirement rather than an engineering nice to have.
Under audit pressure the fastest way to look governed is to write more. Policies multiply, a glossary grows, a spreadsheet register appears, and none of it is connected to a running system. The function becomes a publishing operation. The test is simple: if the classification policy is not applied automatically to connected sources, it is a document rather than a control.
A mandate to govern all data is a mandate to govern nothing, because the register never gets finished and no coverage number ever reaches 100 percent. The organizations that make progress define a critical register in the low hundreds and reach full coverage on it before widening.
Names get written into an ownership column without the named person agreeing, usually during a catalog rollout. The register looks complete and the first incident reveals that no owner will act. Ownership that has not been accepted in a conversation is not ownership, and an unfilled owner field is more honest than a wrong one.
Almost every responsibility above becomes a tooling question at the point of execution, which is why the platform choice is a governance decision even though the governance leader only recommends it. Five requirements follow directly from the nine.
Decube covers those five in one platform, with pricing published rather than quoted on request: Starter at 175 US dollars per user per month from 21,000 a year with a minimum of 10 users, and Growth at 225 US dollars per user per month from 54,000 a year with a minimum of 20 users. For a wider view of what else is on the market, including where each product is stronger, the data governance tools comparison covers the field, and you can see the platform against your own data before committing to any of it.
Take the four questions the job exists to answer and pick one report that matters. Trace every figure on it back to source and write down how long each trace took. That number is your baseline for metric 1 and it is the most persuasive thing you will produce in your first month, because it converts a vague complaint about data trust into a measured cost.
Then build the register for that one report: every critical element on it, a named owner who has agreed, a classification, and one quality threshold each. Twenty elements fully governed beat two thousand listed. Widen only once coverage on the first set holds. The data management strategy and governance guide sets out how that sequencing fits a longer program.
The role is accountable for data governance. Without visibility into how data flows, it is governing in the dark. When the function can see the data lifecycle, conversations become factual, decisions get faster and trust becomes provable. That is what a good day in this job is supposed to feel like.
A data governance leader is accountable for whether the organization can answer four questions about any number it relies on: where it came from, who owns it, how sensitive it is, and whether it can be trusted today. In a normal week that means tracing numbers on request, keeping the register of critical data elements current, classifying data, ruling on new uses of data, setting and defending quality thresholds, running the governance forum, reviewing change requests with downstream impact, producing audit evidence, and reporting the same scorecard upward every month. They do not build the pipelines and usually do not own the systems the data passes through. They own the answer.
Six roles recur. The executive sponsor funds the program and clears blockers. The data governance leader runs it and owns classification, certification, ownership records and evidence. Data owners are accountable for a domain and accept quality thresholds on it. Data stewards do the daily work of definitions, classifications and issue triage. Data custodians operate the systems and apply access and encryption controls. The governance forum or council settles contested decisions. The distinction that matters is that owners and stewards can be delegated to, while the statement made to a regulator cannot be.
Five outright: the classification of a data element, whether a dataset is certified for reuse, who is recorded as its owner, whether a quality threshold breach opens a formal incident, and whether the evidence produced answers the question an auditor or regulator asked. Five more they recommend but do not sign: platform purchase, domain headcount, retention period changes, remediation funding, and whether a release ships with a known data defect. Three must always escalate: accepting a known breach of a regulatory obligation, approving a new use of personal data with no lawful basis, and overriding a data owner who has refused.
Seven with numbers attached. Median time to trace a number back to source, targeting under 4 hours. Ownership coverage of the critical data element register, targeting 100 percent of the register rather than of all tables. Classification coverage across connected sources, targeting 100 percent with every exception logged and dated. The share of critical elements carrying a live quality threshold, targeting 90 percent within two quarters. Column level lineage coverage on regulatory reporting paths, targeting 95 percent. Median audit evidence response time, targeting under 5 working days. And the age of the oldest open governance decision, targeting nothing older than 60 days. Policies published, glossary term count and catalog page views measure effort rather than trust, and are worth refusing.
A chief data officer is accountable for whether data creates value and whether the risk from it sits inside appetite, owns the strategy and the budget, and answers to the CEO, the CFO or the board. A data governance leader is accountable for whether the organization can answer for its data, owns classification, certification, ownership records and evidence, and answers to the CDO, the chief risk officer or the chief operating officer. In organizations under roughly 500 people the two are frequently the same person. Above that, separating them is what keeps governance findings independent of the strategy that produced them.
Data governance answers for the data: which datasets exist, what each column means, who owns it, how sensitive it is and whether it meets an agreed quality threshold. AI governance answers for the systems that act on that data: which models and agents are running, what risk class each falls into, how they were tested before release, how they are monitored after it, and who can overrule them. The central artifact differs too, a register of critical data elements against an inventory of AI systems with a risk class each. They are separate disciplines with one dependency running between them: an AI governance program built on ungoverned data has nothing to govern with, because the model inherits every defect in its inputs and none of the context needed to catch it.
Four things, and an agent needs all of them programmatically rather than in a document. A catalog with a named owner, a business definition and a classification for every asset the agent can reach. Column level lineage, so the agent and its reviewers can trace any value it returns back to source. Quality monitors with agreed thresholds on the critical elements, so the agent can tell a healthy table from a stale or broken one at the moment it reads. And classification driven masking, so sensitive columns never reach the model in the first place. Decube provides all four in one platform, which is why an agent reading through it gets the lineage, the quality signal and the policy in the same response rather than from four separate tools.
Judge it on four requirements specific to healthcare rather than on a general ranking. First, classification and masking driven by policy, applied automatically to every connected source, because protected health information appears in free text fields as well as obvious columns. Second, column level lineage covering clinical and claims reporting paths, so a reported figure can be traced without a manual walkthrough. Third, access history that exports as evidence, because the audit question is who saw this table and when. Fourth, coverage of the sources you actually run, including older clinical systems with no native connector. Decube meets those four and publishes its pricing, which shortens procurement. Collibra, Alation and Atlan are also evaluated in healthcare and each is strong on catalog and discovery, with quality monitoring generally requiring a separate product alongside them. Run the evaluation on your own data against those four requirements rather than on a vendor list.
The names that appear on most shortlists against Collibra are Decube, Alation, Atlan, Informatica, Microsoft Purview, OvalEdge and data.world. They divide roughly into three groups. Collibra and Informatica are the established enterprise suites, broad and heavy, with long implementations. Alation and Atlan are catalog first products, strong on discovery and lineage, usually paired with a separate observability tool for data quality. Decube is the consolidated option, combining catalog, column level lineage, quality monitoring and governance in one platform with published pricing, which is why it comes up most often in regulated firms that want evidence and quality in the same place as the catalog rather than stitched across vendors.
Both are catalog first platforms and the split is one of emphasis. Alation was built around search and analyst adoption, and its strength is getting business users into the catalog and keeping them there, with stewardship workflows built around that behavior. Atlan is stronger on column level lineage and on integrations with the modern cloud stack such as Snowflake, dbt and Databricks, and it is usually the easier of the two to stand up quickly. Neither includes broad data quality monitoring or observability, so a team choosing either normally buys a second product for that, which is the cost comparison worth running before the feature comparison. A consolidated platform such as Decube covers catalog, lineage and quality monitoring together, which removes that second purchase.
First step to AI Readiness
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Sneak peek from the data world.
Bring lineage, quality, governance, and AI together in one platform—so your teams and AI agents can act with confidence.
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