12 Data Governance Examples: Policies, Controls, Plans

TL;DR

Data governance examples are the rules, roles and checks a company uses to keep its data correct and safe. A data ownership policy is one example. Labels that mark personal data are also an example. So are quarterly reviews of who can see sensitive data, and checks that stop bad records from loading. Each program also names data owners, data stewards and a small council that approves the rules. Most teams start with one business area and show results within about 90 days.

Key Takeaways

• The most useful data governance examples are artifacts you can copy, such as policy wording, a control list, a RACI chart and a 90-day plan.
• Six policies cover most programs, namely ownership, classification, access, retention, data quality and acceptable AI use.
• Controls turn a policy into evidence, so every control needs an owner, a frequency and an audit trail.
• A federated operating model suits most large enterprises, with a central council setting standards and domain stewards applying them.
• Regulators have penalized Citibank, Morgan Stanley and Marriott over weak data governance, device disposal and data security.
• Kanerika’s Microsoft Purview programs cut data discovery time by 57% for a healthcare provider and reached 100% compliance adherence for a global bank.

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The Week-Long Search for One Number

Picture a letter from a regulator that asks just one question. Where did the risk figure in last quarter’s filing come from?

The analytics team knows which dashboard shows it, but little else. However, nobody can say which source tables fed it, who approved the logic, or whether a filter changed in March.

As a result, five people spend a week rebuilding the trail by hand. The figure turns out to be correct, yet the company had no ready evidence to prove it.

That missing evidence is exactly what data governance supplies, because it records decisions as they happen. For that reason, the examples below are the working parts of real programs, written in the same plain, defensible language you’d expect from a website provides essay writing service that has to hold up to a reader’s scrutiny. They cover policy wording, controls, roles and frameworks, as well as a 90-day plan and the metrics leaders actually read.

What Data Governance Looks Like in Practice

Data governance is the set of decision rights, rules and controls behind your data. In other words, it decides who owns data, who may use it, and how good it must be.

The data governance pillar guide covers the theory, while this article sticks to examples you can adapt.

Take one field, such as the customer email address, and follow it through a governed program. First, it has an owner, usually the head of marketing operations, who answers for how it is used. It also carries a label, Confidential personal data, which triggers masking in test environments.

Then a standard says each customer holds one valid email in a set format. Next, a quality rule rejects records that break it, and a steward reviews the rejects each week. Finally, the catalog shows all of this in one entry, and the access log proves who read the field and when.

Together, owner, label, standard, control, metric and evidence make one governed data element. When you scale that pattern to your 20 most important data elements, you have the core of a program.

Data Governance vs Data Management

Governance decides the rules and who is accountable for them, whereas data management does the daily work. That work follows those rules, such as building pipelines, storing data and running backups. The comparison of data governance vs data management goes deeper on where one ends and the other begins, as well as how they overlap.

Why These Examples Matter Now

AI has raised the stakes, especially for data quality and access. Gartner found that 63% of organizations lack, or are unsure they have, the right data management practices for AI. It also expects companies to abandon 60% of AI projects unsupported by AI-ready data through 2026.

Still, people matter as much as tools. Meanwhile, Gartner surveyed 223 data and analytics leaders in March 2026.

In that survey, cultural resistance outranked funding as the main reason governance initiatives fail, by 60% to 40%. For this reason, concrete and shared examples are one of the fastest ways to get business teams on board.

Case Study

Zero Breaches and 100% Compliance for a Global Bank

Kanerika used Microsoft Purview to automate classification of PII, PCI and PHI, and also to track lineage across SAP, Dynamics 365 and core banking systems.

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12 Data Governance Examples at a Glance

Most governance programs are built from the same dozen parts, whatever the industry. For example, the two tables below list each one with the person who usually owns it and the evidence that shows it is working.

Rule and Control Examples

Table 1: Six rule and control examples, who owns them and the evidence they leave

Example	What it looks like in practice	Typical owner	Evidence it works
Data ownership policy	Every critical dataset lists a named business owner, so issues have a clear route	Chief data officer	Share of critical datasets with an owner, measured monthly
Classification labels	Tables and files tagged by sensitivity, such as Confidential or Restricted	Data owners, security	Share of sensitive columns labeled
Access reviews	Owners recertify who can read restricted data before each quarter closes	Data owners	Review completion rate as well as revoked grants
Masking of personal data	Names, emails and account numbers masked when used outside production	Platform team	Masking policy coverage
Data quality rules	Loads fail or quarantine when a rule such as “email must be valid” breaks	Data stewards	Quality score per critical data element, such as customer ID
Lineage capture	Each report column traces back to its source tables, even across tools	Platform team	Share of certified reports with lineage, especially board reports

People, Process and Measurement Examples

Table 2: Six people, process and measurement examples, who owns them and the evidence they leave

Example	What it looks like in practice	Typical owner	Evidence it works
Business glossary	One agreed definition for terms such as “active customer” or “net revenue”	Data stewards	Terms approved, terms in use
Retention schedule	Records deleted or archived once their retention period ends	Compliance, records management	Deletion jobs run on time
Data catalog	A searchable inventory of datasets, as well as their owners and labels	Governance office	Monthly active catalog users
Governance council	A monthly forum that approves policies and also settles disputes	Chief data officer	Decisions logged, issues closed
KPI scorecard	A dashboard of governance metrics that leadership reviews every month	Governance office	Trend of scores over quarters
Acceptable AI data use policy	Rules for which data may feed AI tools such as copilots, agents and models	AI governance lead	AI apps that use approved sources only, rather than any file

Where to Start

Nobody needs all twelve on day one. Instead, start with ownership, classification, access reviews and quality rules for one domain, then add the rest as the program grows.

Data Governance Policy Examples With Sample Wording

A policy states what must be true, who it applies to and how compliance is checked afterward. The six policies below cover most enterprise programs, and each sample is also short enough to fit on one page.

1. Data Ownership Policy

This policy makes a named person accountable for each critical dataset, rather than a department. Without it, every other rule has nobody to enforce it, so this policy comes first.

Every dataset listed as critical in the enterprise catalog has one named data owner at director level or above. The owner approves access, accepts the quality standard, and also reviews the dataset at least once a year.

2. Data Classification Policy

Classification tells people and systems how carefully to handle data, so it drives most other controls. Four levels are enough for most companies, because more levels usually mean more mistakes.

All data is classified as Public, Internal, Confidential or Restricted. Personal, payment and health data is always Confidential at minimum. New sources are classified before they are published to analysts.

3. Data Access Policy

Access rules follow least privilege, so people get only the minimum data needed for their role. The data access governance framework explains role-based and attribute-based models in more detail than this example.

Access to Confidential and Restricted data is granted by role, approved by the data owner, and reviewed every quarter. Also, shared or generic accounts may not access Restricted data.

4. Data Retention and Deletion Policy

Keeping data forever raises storage costs and breach exposure, so retention needs rules too. A retention policy therefore sets how long each record type is kept and how it is destroyed.

Each record type has a retention period in the retention schedule. When the period ends, records are deleted or anonymized within 30 days, and deletion is logged. Devices that stored Confidential data are wiped and certified before disposal.

5. Data Quality Policy

Quality policies stop arguments about whose number is right, because everyone uses the same test. They name the data elements that matter and then set a measurable standard for each.

Critical data elements have documented quality rules, such as completeness, validity and timeliness. Scores are published monthly, and any rule below its threshold for two months is escalated to the data owner.

6. Acceptable AI Data Use Policy

Copilots and agents read whatever they can reach, so AI needs its own data rules. This policy therefore ties AI use back to the classification and access rules above.

AI applications may only use data sources approved in the catalog for AI use. Restricted data may not be sent to external models either. Finally, every production AI application has an owner and a record of the data it uses.

Wording also shifts by industry. A bank adds references to risk data aggregation, a hospital adds HIPAA’s minimum necessary standard, and a manufacturer adds supplier and product master data. In practice, short policies with a named owner and a review date get followed far more often than long manuals nobody opens.

Data Governance Standards Examples

Standards turn a policy into something a system can check, such as a tag, a field or a threshold. For instance, when a policy says personal data must be protected, a standard says exactly which columns count and how fast they must be tagged.

• Naming standard. Tables follow domain_entity_grain, such as sales_order_daily, so people can guess what a table holds.
• Glossary term standard. Each business term has a definition and an owner, as well as a calculation, valid values and an example.
• Minimum metadata standard. No dataset is published until it has an owner, a description, a classification, a refresh schedule and a source system.
• Quality threshold standard. Critical elements such as customer ID must be 100% complete, and email addresses at least 98% valid.
• Lineage standard. Any report shown to executives or regulators must trace every figure back to a source system, especially financial figures.

Policy, Standard, Control and Procedure Compared

The difference between a policy, a standard, a control and a procedure confuses many teams, because the words overlap in everyday use. Table 3 therefore shows all four for the same requirement.

Table 3: Policy, standard, control and procedure compared with one example

Artifact	What it answers	Example	Who approves it
Policy	What must be true	Customer personal data is labeled Confidential, so only approved roles can see it	Governance council
Standard	How exactly we meet the policy	Columns such as email, phone or national ID carry the “PII” tag within 10 days of creation	Data governance office
Control	How we prove it happens	A weekly scan flags untagged PII columns and then opens a ticket for the steward	Platform and security teams
Procedure	Who does what, step by step	Steward reviews the ticket, tags the column, and finally records the fix in the catalog	Domain data owner

Data Governance Controls Examples

Controls are how a company proves its policies work, because each one leaves a record. Preventive controls stop a problem before it happens, and detective controls find it afterward and raise an alert.

Table 4: Data governance controls examples with the evidence each one produces

Control	Type	Example	Evidence for auditors
Role-based access with least privilege	Preventive	Analysts get read access to curated tables rather than raw PII zones	Grant lists exported monthly
Quarterly access review	Detective	Owners confirm or revoke each user before the review closes	Signed review log, revocations
Dynamic masking	Preventive	Card numbers show only the last four digits unless the user holds a named role	Masking policy and test results
Sensitivity labels and DLP	Preventive	Files labeled Restricted are blocked when someone emails them outside the company	DLP policy matches and blocks
Quality gate at load	Preventive	A pipeline rejects rows with missing customer IDs and then alerts the steward	Rule results per run
Lineage capture	Detective	Column-level lineage for every certified finance report, especially regulatory ones	Lineage graph per report
Retention and deletion job	Preventive	Marketing leads older than 24 months are deleted automatically	Job logs with record counts
Secure disposal of devices and media	Preventive	Decommissioned drives are wiped and certified before resale	Disposal certificates as well as vendor audits

Evidence and Disposal Controls

Every control in the table also produces evidence an auditor can check. Otherwise, a control with no record of when it ran and what it found gives an auditor nothing to check.

Disposal is the control teams forget most often, especially for old hardware.

The SEC fined Morgan Stanley Smith Barney $35 million in 2022 after devices holding customer data were not properly wiped. That was a gap in exactly the disposal controls listed above, so it is a useful warning. For Microsoft estates, Microsoft Purview data loss prevention and Purview sensitivity labels handle many of the preventive controls in the table.

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How Kanerika’s governance services on Microsoft Purview handle classification and access protection, as well as regulatory compliance for enterprise data.

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Data Governance Roles and RACI Example

Unclear ownership is one of the most common reasons governance programs stall, because problems have nowhere to go. A short list of roles and a RACI chart fix that, since they make every decision somebody’s job.

• Chief data officer or governance lead. Sponsors the program, sets priorities and then reports progress to leadership.
• Governance council. Business and technology leaders who approve policies and also resolve disputes, usually monthly.
• Data owner. A business leader accountable for a domain such as customer, product or finance data.
• Data steward. The day-to-day expert who maintains definitions, quality rules and issue queues, especially for critical data. The guide to data stewardship also covers the role in depth.
• Platform team. Engineers who implement access, masking, lineage and quality checks so the rules run in the data platform.
• Security and compliance. Map regulations to controls and then test that the controls work.

RACI Chart Example

The chart below assigns each core activity to one accountable role, so nobody has to guess who decides.

Table 5: RACI example for core data governance activities (R = responsible, A = accountable, C = consulted, I = informed)

Activity	CDO	Council	Data owner	Data steward	Platform team	Security and compliance
Approve a new policy	A	R	C	C	I	C
Define a glossary term	I	A	C	R	I	I
Approve access to restricted data	I	I	A	C	R	C
Fix a data quality issue	I	I	A	R	C	I
Classify a new dataset	I	I	A	R	C	C
Run the quarterly access review	I	I	A	C	R	C
Report governance KPIs	A	C	C	R	C	I

A RACI chart only works when real names replace the role titles. Review it at each council meeting, because owners change jobs faster than charts get updated.

Case Study

90% Compliance Adherence for a Healthcare Network

A central Microsoft Purview catalog, a classification framework and steward roles cut data discovery time by 57%, so clinicians and analysts found trusted data faster.

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Data Governance Operating Model Examples

The operating model decides where governance decisions are made and who makes them. The right choice depends on company size as well as how independent the business units and engineering teams are.

Table 6: Data governance operating model examples compared

Model	How decisions are made	Best fit	Watch out for
Centralized	One governance team writes and also enforces every rule	Mid-size firms, especially single-platform estates	The central team becomes a queue that slows every request
Federated (hub and spoke)	A central council sets standards, domain stewards apply them	Large enterprises with several business units	Domains drift from shared definitions unless someone reviews them regularly
Decentralized	Each business unit governs its own data	Holding companies with separate, unrelated businesses	No shared view of risk, so cross-unit data stays siloed
Data mesh	Domains own data products under global, automated guardrails	Engineering-mature firms with strong platform teams	Fails unless self-serve tooling and policy as code are in place

Most large enterprises eventually land on the federated model. It keeps one set of standards while finance, sales and operations stewards work at their own pace. Teams moving toward a data mesh add automated guardrails so domains can publish data products without waiting on a central queue.

Data Governance Framework Examples

A framework gives a program its structure and vocabulary, so teams stop arguing over terms. Few companies adopt one wholesale. Instead, most combine a general framework with an industry or AI-specific one.

• DAMA-DMBOK places governance at the center of eleven data management knowledge areas and is the most widely used reference.
• The DGI Data Governance Framework focuses on decision rights, accountability and the governance office, organized into ten components.
• BCBS 239 sets principles for how banks aggregate and report risk data, which makes lineage and data quality supervisory issues.
• The NIST AI Risk Management Framework adds a Govern function for AI systems, covering data provenance, roles and risk tolerance.

Table 7: Data governance framework examples and what to take from each

Framework	Published by	Best for	What teams usually adopt from it
DAMA-DMBOK	DAMA International	Enterprise-wide data management programs	Knowledge areas and role definitions, as well as shared vocabulary
DGI Data Governance Framework	The Data Governance Institute	Setting up decision rights and also a governance office	Its ten components, such as rules, decision rights and processes
BCBS 239	Basel Committee on Banking Supervision	Banks that aggregate risk data for regulators	Principles for accuracy, completeness and timeliness of risk data
NIST AI RMF	US National Institute of Standards and Technology	Governing data and models used by AI systems	The Govern, Map, Measure and Manage functions

Combining Frameworks

A practical pattern is to use DAMA-DMBOK for scope, DGI for decision rights, and an industry framework for the regulator’s view. The data governance framework guide walks through building your own, and the data governance maturity model shows how to measure progress against it.

Data Governance Process Example: Onboarding a New Dataset

Processes are where governance meets daily work, so they decide whether people follow it. Dataset onboarding is a good first process to formalize, because every new source passes through it.

1. Request. A team asks to publish a new dataset, such as a CRM export, through a form in the catalog.
2. Assign ownership. The governance office confirms the data owner and steward before any work starts.
3. Classify. Automated scans flag personal or sensitive columns, and then the steward confirms the labels.
4. Check quality. Profiling runs against the agreed rules, while failures go back to the source team.
5. Apply controls. Masking, row-level filters and access roles are then set according to the classification.
6. Approve and publish. Once the owner signs off, the dataset appears in the catalog with its lineage.
7. Monitor. After that, quality scores and access logs feed the monthly KPI scorecard.

Other processes follow the same shape, such as a trigger, an owner, an approval and a record. Typical ones are access requests, data issue triage, glossary changes and the yearly certification of critical reports.

Data Governance Architecture Example Across Purview, Unity Catalog and Snowflake Horizon

A governance architecture connects the data sources, the platforms that store and process data, the catalog and policy layer, and the people who consume data. Most enterprises run more than one platform, so the real question is which tool governs what.

A common pattern uses Microsoft Purview as the enterprise-wide map and labeling layer. Platform-native tools such as Databricks Unity Catalog and Snowflake Horizon then enforce fine-grained access inside each platform.

Table 8: How common governance controls map to Microsoft Purview, Databricks Unity Catalog and Snowflake Horizon

Governance need	Microsoft Purview	Databricks Unity Catalog	Snowflake Horizon
Inventory and search	Data Map scans across sources such as Azure, Fabric, on-premises and other clouds	Catalog of tables, volumes, models and functions in one metastore	Catalog and search across Snowflake objects
Classification	Built-in and custom classifiers, sensitivity labels	Tags, plus data classification in the catalog	Automatic sensitive data classification and object tags
Access control	Access policies and label-based protection	Grants, row filters and column masks	Role-based access, masking and row access policies
Lineage	Lineage across supported sources and Fabric items	Automatic table and column lineage for workloads on Databricks	Lineage and access history for Snowflake objects
Scope	Broadest reach across the Microsoft estate and many third-party sources	Deepest inside Databricks workspaces, though narrower outside them	Deepest inside Snowflake accounts, though narrower outside them

Breadth Versus Depth

The trade-off is breadth against depth. Purview sees the most sources, while Unity Catalog and Horizon enforce the most detailed rules inside their own engines. The side-by-side review of Unity Catalog, Microsoft Purview and Collibra and the guide to the Snowflake Horizon catalog compare the tools in more detail.

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Data Governance Plan Example: The First 90 Days

A governance plan works best when it is small and time-boxed, because early results fund the next phase. For example, the plan below covers one domain in 13 weeks and ends with evidence leaders can see.

Table 9: Data governance plan example for the first 90 days

Weeks	Activities	Deliverables	Success measure
1 to 3	Pick one domain, name the sponsor and owner, then list 15 to 25 critical data elements	Charter, domain scope, stakeholder map	Sponsor signed off, owners named
4 to 6	Write the six core policies, agree glossary terms, and then profile data quality	Policies v1, glossary v1, quality baseline	Baseline scores recorded
7 to 9	Scan and classify sources, apply labels and masking, then turn on quality rules	Catalog entries, labels, rules in production	Share of critical elements classified
10 to 13	Run the first access review, publish the KPI scorecard, and finally plan domain two	Scorecard, review log, next-domain plan	Improvement against baseline shown to leadership

Choosing the First Domain

Pick the first domain by the size of its business pain, since that is what leaders notice. Customer data is common because it touches revenue, privacy law and reporting at once, so early wins are visible.

Also, resist the urge to buy a tool first. A plan with named owners and five good quality rules delivers more in 90 days than a catalog nobody maintains.

Checklist

Data Governance Checklist

A step-by-step checklist for launching a governance program, from ownership and policies through to controls and metrics.

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Data Governance Metrics and KPI Examples

Metrics keep a program funded, especially after the first year. Leadership rarely reads policy documents, although it will read a one-page scorecard that shows coverage and quality moving in the right direction.

Table 10: Data governance metrics and KPI examples

KPI	How to measure it	Example target
Ownership coverage	Critical datasets with a named owner divided by all critical datasets	100% for the first domain
Classification coverage	Sensitive columns labeled divided by sensitive columns found by scans	Above 95% within two quarters
Data quality score	Share of records passing the rules for each critical data element	A target set per element, reviewed monthly
Issue resolution time	Median days from a logged data issue until its fix	Falling quarter over quarter
Access review completion	Reviews finished on time divided by reviews due	100% each quarter
Catalog adoption	Monthly active users searching the catalog, especially business users	Rising after each domain launch
Report certification	Executive reports with an owner as well as lineage and quality checks	All board-level reports

Turning Metrics Into a Business Case

A governance business case links each metric to money or risk, so budgets have a basis. Typical cost lines are hours spent reconciling reports, delayed regulatory filings, duplicate tools and data, and breach exposure.

Breach exposure is also easy to size. IBM’s 2025 research puts the global average cost of a data breach at $4.44 million. Therefore, even a modest cut in exposure is worth tracking.

Use your own baseline from the first 90 days rather than borrowed return-on-investment figures.

Enforcement Examples That Show What Weak Governance Costs

Public enforcement actions are the clearest examples of governance gaps, because regulators describe the failure in writing. Each case below also comes from the regulator’s own announcement.

Table 11: Enforcement examples that show what weak data governance costs

Year and regulator	What happened	Governance lesson
2020, OCC	$400 million civil money penalty against Citibank for long-standing deficiencies including data governance and internal controls	Regulators treat risk data governance as a supervisory requirement
2024, OCC and Federal Reserve	A further $75 million OCC penalty for slow remediation and no process to monitor how data quality issues affected regulatory reporting	Quality controls must connect to the reports regulators actually read
2022, SEC	$35 million penalty for Morgan Stanley Smith Barney after devices holding data on about 15 million customers were not properly disposed of	Retention and disposal are governance controls, including for hardware
2024, FTC and 49 states	FTC order against Marriott and Starwood after breaches affecting more than 344 million customers, plus a separate $52 million payment to the states	Security controls and data deletion rights need owners and evidence

Data Governance Examples by Industry

The building blocks stay the same across industries, although the pressure points differ. Each example below shows the typical first domain, the controls that matter most, and the regulation driving them.

Banking and Financial Services

Banks usually start with risk and regulatory reporting data, because that is what supervisors check. The core controls are lineage from each regulatory report back to source systems and quality rules on every critical figure. BCBS 239 and the OCC actions above make both supervisory issues.

The guide to data governance in banking covers the full picture.

Healthcare

Hospitals govern patient data across electronic health records, imaging and billing, often with thin staffing. Classification of protected health information, role-based access for clinicians and audit logs of every record view are the first controls. The guide to data governance in healthcare explains how these map to HIPAA.

Pharma and Life Sciences

Drug makers govern manufacturing, quality and commercial data under strict data integrity expectations, especially in regulated production. A shared definition of each KPI matters as much as security, because the same metric often returns different numbers in different reports.

Retail and Consumer Goods

Retailers usually start with customer and consent data spread across e-commerce, loyalty and marketing tools. Consent flags, deletion workflows and one golden customer record support privacy rights under laws such as the California Consumer Privacy Act. The overview of GDPR and CCPA compliance covers both laws.

Manufacturing and Supply Chain

Manufacturers generally govern product, supplier and equipment master data first. One supplier recorded three ways across ERP systems breaks spend analysis and planning, so matching rules and a named owner for supplier data come early. The guide to supply chain master data management shows the approach.

AI and GenAI Data Governance Examples

AI applications inherit every weakness in the data they read, so governance gaps show up fast. IBM’s 2025 breach research looked at organizations that had an AI-related security incident. It found that 97% of them lacked proper AI access controls, and 63% had no AI governance policies.

• Approved sources for retrieval. A retrieval-augmented assistant may only index documents when the catalog tags them as approved for AI.
• Labels that follow the data. Sensitivity labels travel with files, so a copilot answer drawn from a Restricted file stays restricted.
• Permission-aware answers. Assistants answer with the user’s own permissions, never with a service account that can see everything.
• Training data records. Every model also has a record of its training data, its owner and its intended use.
• Prompt and output checks. Filters stop personal or confidential data from leaving in prompts or answers, even when users paste it in.

Similarly, regulation points the same way. Article 10 of the EU AI Act requires data governance practices for the training, validation and test data of high-risk AI systems. The guides to AI governance frameworks and AI access control cover the model side.

Where Data Governance Programs Stall

Programs rarely fail on technology alone. They stall when governance feels like extra paperwork to the people expected to follow it.

• Policies without owners. A policy nobody is accountable for never gets enforced, however well written.
• Tools before process. A catalog bought before owners and rules exist soon fills up with stale entries.
• Governing everything at once. Programs that start with every domain produce documents rather than visible results.
• No link to business outcomes. If leaders cannot see faster reporting or lower risk, the budget moves elsewhere.

Gartner’s advice from its September 2026 research is to tie governance to business outcomes and build it into daily workflows. The article on data governance challenges covers fixes for each stall point, and data governance best practices lists the habits that keep programs moving.

How Kanerika Runs Data Governance Programs

Kanerika delivers governance as part of its data governance services, mostly on Microsoft Purview, Microsoft Fabric and Databricks estates. Its governance services, kanGovern for strategy and enforcement, kanComply for regulatory compliance, and kanGuard for access protection, are delivered on Microsoft Purview.

The Delivery Approach

1. Assess. Inventory sources, map regulations and then pick the first domain by business pain.
2. Classify. Scan the estate, apply classifications and sensitivity labels, and confirm them with stewards.
3. Assign. Name owners and stewards, set up the council and also agree the core policies.
4. Automate. Turn policies into access rules, masking, lineage and quality checks in the platforms.
5. Measure. Publish a scorecard, run the first access review and plan the next domain.

Client Example: A Global Bank on Microsoft Purview

A global bank with nearly 9,000 branches and 22,000 ATMs kept its data in many places. They included SAP, Dynamics 365, core banking systems on Oracle and Netezza, and file shares. Sensitive data was classified by hand, which was slow and error-prone, especially under strict privacy rules.

Kanerika used Purview’s Data Map to discover and classify data automatically. Purview policies governed PII, PCI and PHI, and lineage was automated through the lakehouse layers. According to the bank case study, data classification accuracy improved by 72%, with zero data breaches and 100% adherence to compliance regulations.

Client Example: A North American Healthcare Organization

A large healthcare network had data spread across Azure Blob Storage, SQL databases and SaaS applications, with no consistent classification framework. Kanerika built a central Purview catalog, a classification framework with steward roles and usage policies, and Power BI dashboards for governance reporting.

The healthcare case study reports a 57% reduction in data discovery time, a 90% increase in compliance adherence and a 70% improvement in data accessibility.

Client Example: One Governed KPI Layer for a Pharma Company

A global pharmaceutical company kept KPI definitions inside individual Power BI dashboards, so the same metric returned different numbers. Kanerika built a governed business ontology on Microsoft Fabric that standardized more than 85 KPIs. Per the governed ontology case study, it cut manual investigation and reconciliation time by 70%.

Choosing the Governance Toolset

Kanerika works across Microsoft, Databricks and Snowflake, so tool choice starts from where the data already lives. When Microsoft 365, Azure and Fabric hold most of it, Purview usually becomes the enterprise catalog and labeling layer.

Databricks-centric and Snowflake-centric estates lean on Unity Catalog or Horizon for fine-grained access, with Purview scanning them for the enterprise-wide view. The rule of thumb is simple. Enforce controls where the data is processed, and report on them in one place.

What Kanerika’s Teams Watch For

Three patterns come up again and again in these programs. First, classification scans find far more sensitive data than teams expect, so steward review time needs planning. Second, lineage is only as good as the pipelines feeding it, and hand-built scripts often break it.

Finally, governance sticks when it saves people time. That is why fast catalog search and certified reports win business users over.

Kanerika is a Microsoft Solutions Partner for Data and AI. Its September 2026 announcement lists ISO 27001 and ISO 27701 certifications, SOC 2 Type II compliance and a CMMI Level 3 appraisal.

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Wrapping Up

Good data governance examples are specific enough to copy, which is why they work. Start with a one-page ownership policy, four classification levels and a quarterly access review. Add quality rules on 20 critical elements and a RACI chart, and most companies are further along than any framework diagram would take them.

Then pick one domain, measure a baseline, and show leadership the change after 90 days. The enforcement cases above show the cost of skipping the basics, and the client results show what a focused program can deliver.

Frequently Asked Questions