Data Governance in Financial Services: Architecture Requirements for BCBS 239, Basel III, DORA and Regulatory Compliance

TL;DR: Data governance in financial services determines whether a firm can meet strict regulatory expectations for accuracy and data tracking across every stage of its reporting chain. Institutions that build governance into their architecture avoid the audit failures, rebuild cycles, and operational risks that come from fragmented data systems, and they gain structures that support compliance with rules like GDPR, Basel III, Dodd-Frank, and MiFID II. DORA: the EU’s Digital Operational Resilience Act, which becomes applicable on 17 January 2025, setting binding requirements for ICT risk management, incident reporting, testing, and third-party oversight.

Data governance in financial services is the line between a data architecture that survives regulatory examination and one that gets flagged for remediation. When examiners trace a regulatory report back through your transformations and can’t verify lineage to source systems, you’re looking at consent orders that freeze new initiatives while you rebuild infrastructure under independent oversight. 

In Europe, the Digital Operational Resilience Act (DORA) turns operational resilience into explicit system requirements, covering ICT risk management, major-incident reporting, threat-led testing, information sharing, and direct oversight of critical ICT providers.

A process like this runs between $5 million and $50 million and takes anywhere from 18 to 36 months. But here’s what most vendors won’t tell you: the institutions that fail these audits almost always share the same architectural mistake. They treated governance as documentation they could add after building their data warehouse, not as design constraints that shape how the warehouse gets built in the first place.

We will look at why banks need governance built into their data systems, which rules shape data warehouse design, and how to track data in ways that consistently pass audits.

Why Data Governance Architecture Requirements Changed After 2008: BCBS 239 and Basel III Impact

Data governance in financial services changed from paperwork tasks to system design requirements after the 2008 financial crisis showed how banks couldn’t pull together risk data from different departments during emergencies. Lehman Brothers had 2,600 different software systems, most of which couldn’t ‘talk’ to each other. When regulators asked how much exposure the bank had to mortgage-backed securities, it took weeks to get an answer because “mortgage exposure” meant different things in different systems. Banks couldn’t answer basic questions about how much money they’d lent out because different systems used different definitions for simple terms like “customer.”

The Basel Committee on Banking Supervision’s BCBS 239 rules set clear technical requirements: 

Banks must produce complete risk data within hours during crises, with documentation detailed enough that regulators can verify every calculation independently. The core problem is that you can’t add data tracking to systems after you build them. The tracking must be built in from the start and stay updated through every system change.

Banks that built data warehouses before BCBS 239 spent years adding tracking to systems that never captured how data changed as it moved through the warehouse. Many found they couldn’t trace data back to its source because the code existed only in undocumented scripts written by developers who’d left years ago.

The General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) create data location rules that conflict with global data warehouse designs. You can’t copy European customer data to US data centers for reporting anymore, without ensuring that it aligns with frameworks such as the EU – U.S. Data Privacy Framework (DPF). Your system must enforce geographic boundaries through code, and not through policies.

Financial institutions also face enforcement risks that exceed other industries. A bank that can’t prove adequate anti-money laundering controls can lose the ability to do international banking, effectively shutting down global operations.

Alongside prudential rules like BCBS 239 and Basel III, DORA closes a long-standing gap by harmonizing ICT-risk expectations across EU financial entities. From 17 January 2025, firms must demonstrate resilient architectures that can prevent, detect, contain, recover from, and report ICT incidents within prescribed processes and timelines.

Four Technical Requirements Regulators Check in Financial Services Data Governance Systems

Financial services systems must satisfy four technical capabilities that regulators check: automated data quality checks, detailed access control with audit trails, complete tracking from source to report, and policy rules built into system controls instead of written procedures. Each requirement limits your design choices.

Requirement	What Regulators Expect	Where Legacy Systems Break	What Modern Architecture Must Support
Data Quality Validation	Checks are built into processing steps so errors surface before filing deadlines. Each check must show the rule applied, the data used, and the outcome.	Quality rules run after loads finish, catching issues too late. No record of which rule version was used or who approved exceptions.	Validation rules are embedded in the transformation logic with evidence stored for audit queries.
Access Control With Audit Evidence	Each access event must match a defined rule and include justification. Logs must explain why access was granted or denied.	Role-based models are too broad. Logs show table access but not the reason. Policies conflict and aren’t evaluated together.	Policy-driven access that evaluates all conditions and records the rule path behind each decision.
Lineage From Source to Report	Regulators must trace every reported value to the originating system with a full history of calculation logic.	Lineage lives in diagrams updated manually. Scripts lack documentation. No versioning for formulas.	Automated lineage is generated from metadata, so tracking updates occurs with each change.
Policy Enforcement Through System Logic	Policies must be executed through code, not procedures. Systems must enforce boundaries such as information barriers and regional restrictions.	Staff rely on written rules. Enforcement depends on manual approvals. Cross-policy conflicts create gaps.	Rules encoded directly in governance engines that apply restrictions consistently across all data paths.

What DORA adds: architectures must also support consistent ICT incident classification and reporting, evidence-rich logging, threat-led penetration testing, and governed ICT third-party arrangements. Practically, that means: incident logs and classifications that feed regulatory templates, testable recovery playbooks, and contract clauses for audit rights, data location, exit strategies, and sub-outsourcing controls for cloud and other providers.

Data quality validation: an architectural requirement

Data quality in financial services means building checks into your data change logic instead of running checks after data loads finish. Quality systems must detect fraud signs, find reporting gaps before filing deadlines, and flag problems that trigger regulatory questions.

Wire transfers need checks against sanctions lists, ownership registries, and risk ratings, with records showing which list versions applied at transaction time and who approved exceptions. Missing any element creates audit findings. Your extract, transform, load (ETL) system must capture this information during processing, not rebuild it afterward.

Quality rules encode regulation definitions in transformation logic. When Dodd-Frank defines “swaps,” your validation system implements that definition exactly where data enters the warehouse.

DORA expects these controls to be embedded in normal operations so that incidents are detectable, classifiable, and reportable without ad-hoc reconstruction.

Access controls that satisfy multiple regulations in financial services

Access control in financial institutions must satisfy competing requirements at the same time. The Sarbanes–Oxley Act (SOX) requires separation of duties. GDPR requires justification for every access. Industry regulations restrict nonpublic customer information. Chinese walls prevent information flow between departments.

Traditional role-based access control cannot provide the level of granularity that regulators expect in financial services. An investment banker needs client deal information but must be blocked from the same client’s trading data to prevent insider trading.

Attribute-based access control provides this flexibility but requires systems that keep attributes accurate. When you classify data as material nonpublic information, that classification must spread through derived datasets automatically. Manual classification doesn’t work for petabytes where importance depends on transaction context.

Every access denial needs logging with enough context to prove controls worked correctly during examinations. Regulators test permissions by reviewing access logs. Your system must create audit trails that explain access decisions in regulatory terms, not just technical identifiers.

Access decisions and administrative actions should be logged at a level that supports DORA’s incident analysis and reporting obligations.

Lineage tracking architecture

Financial services lineage requirements extend to calculation logic. When regulatory reports show capital ratios, examiners trace those numbers to specific loan records and confirm source data came from authorized systems.

You need to track which calculation formula version was active when each report was generated, keep history after source systems are shut down, and handle lineage through manual changes that traders make to model outputs.

WhereScape RED captures lineage automatically because code generation from metadata naturally documents the relationship between source fields and calculated results. The metadata-driven system keeps lineage current as transformations change, avoiding documentation debt that builds up when teams manually update diagrams months after deploying code changes.

Impact analysis answers the question examiners actually ask: if source data proves incorrect, which reports are affected? Financial institutions need this analysis to be complete within hours when quality issues might affect filed reports. Manual lineage documentation can’t support this timeframe because tracing dependencies through code and scripts takes days.

For DORA, lineage and change evidence help reconstruct the technical root cause of ICT incidents and demonstrate control effectiveness during supervisory reviews.

Policy enforcement through architecture

Data governance policies in financial services must run through system controls instead of procedural compliance. Manual approval for routine data access creates bottlenecks that prevent meeting reporting deadlines. But automatic approval without controls creates audit findings when access doesn’t match documented policies.

Failure mode appears when policies interact. An analyst might have legitimate access to customer transaction data through their territory assignment, but that same access violates information barriers if the customer is also a trading counterparty in a deal the analyst’s division is advising on. Most access control systems evaluate policies sequentially and stop at the first match, which creates gaps where later policies never get checked. Financial institutions need policy engines that evaluate all applicable rules and apply the most restrictive outcome.

The system solution encodes policy decisions in system logic. If the policy states analysts can access transaction data for assigned customers, the permission system evaluates that rule automatically:

• Vague policies like “Analysts should generally avoid accessing data outside their territory” don’t translate to enforceable logic
  
• Specific policies like “Analysts can access transaction history for customers assigned to their territory ID in the CRM system” can be automated

Exception workflows need systems that create audit trails. When analysts need cross-territory access for legitimate reasons, the system needs to do the following:

• Document justification for the exception
  
• Limit access duration
  
• Log all data viewed during exceptions.

These exception logs become audit evidence that controls operated effectively even when exceptions occurred.

BCBS 239, Basel III, Dodd-Frank, and MiFID II Architecture Requirements for Financial Services

Financial institutions deal with overlapping regulatory regimes that create specific technical requirements. Understanding how regulations translate into system limits prevents finding gaps during examinations.

Basel III and BCBS 239 architecture requirements

​​Basel III

Basel III creates capital requirements that force banks to calculate and report risk exposures accurately. Banks must prove they have enough capital to cover potential losses, which means your system needs to aggregate risk data from every loan, investment, and trading position across the entire organization. These calculations must be accurate because regulators use them to determine if your bank can stay open during financial stress.

But Basel III capital calculations depend on data quality. If your warehouse can’t accurately sum credit exposure across all business units, you can’t prove capital adequacy. 

When a loan becomes past due or defaults, the risk weight typically increases to 150%, which directly impacts your capital adequacy ratio. If your warehouse can’t trace that status change back through your loan servicing system with timestamps showing when the classification changed and who approved it, you can’t prove your capital calculations were accurate at report time. When risk weights change due to regulation updates, your system must recalculate capital ratios across your entire loan portfolio and show the regulatory basis for those changes.

BBCS 239

BCBS 239 principles apply primarily to large international banks but increasingly act as de facto standards for smaller institutions. The core requirement is to produce complete, accurate risk data from all business units under normal and stress conditions.

Principle 3 demands accuracy and integrity with specific technical needs. This typically requires database change auditing, checks in transformation processes, and permanent archives of source data as proof for regulatory filings.

Principle 4 requires risk reports to cover all material exposures without gaps. If your bank starts offering a new derivative product, your system must capture that exposure in risk systems before it reaches materiality limits.

Principle 6 calls for speed sufficiency for risk management during stress. Institutions stopped monthly batch processing in favor of more frequent updates.

Dodd-Frank architecture requirements

Dodd-Frank swap reporting creates technical complexity requiring careful system design. Counterparties must report swap transactions to registered repositories within regulatory timeframes, but both sides rarely have identical data.

Your system must put in matching logic that fixes these differences and makes sure changes to previously reported trades follow repository-specific formats.

The regulation requires legal entity identifiers on all counterparties, which sounds simple until you hit subsidiaries, affiliates, and complex ownership structures.

MiFID II architecture requirements 

The MiFID II regulation requires instrument identification codes, venue identifiers, and trader IDs in specific formats varying by asset class. Internal representations are transformed into regulatory formats while keeping relationships between reported trades and internal transaction records.

But when regulators question a reported trade, you trace that submission back through format conversions to the original order management system record. Standard lineage tools track table-to-table relationships, but MiFID II lineage requires field-level tracking through complex transformations that combine multiple source fields into single regulatory fields.

DORA architecture requirements (EU)

ICT risk management: end-to-end controls for prevention, detection, response, and recovery, backed by governance and testing.

Incident reporting: classify ICT incidents and report significant ones to competent authorities using harmonized taxonomies and templates.

Digital operational resilience testing: a program that scales with size and risk, including threat-led penetration testing for selected firms.

ICT third-party risk: governed contracts, ongoing monitoring, and special oversight when providers are designated “critical.”

Information sharing: enable trusted cyber-threat and vulnerability information exchange.

Note: In 2025, the ESAs and national authorities began operationalizing DORA’s oversight of critical ICT providers and the related reporting infrastructure. Find out more from ESMA.

GDPR architecture requirements

GDPR limits where financial institutions can process European customer data. You can’t copy all data to centralized warehouses in other jurisdictions for consolidated reporting. 

Right of access requests require different system capabilities than most warehouses provide. When EU customers request all data an institution holds about them, you need to search across every system and compile complete responses within one month. Metadata management requires sophisticated enough to track which tables contain personal data and what identifiers link records to individuals.

Anti-money laundering architecture requirements

AML programs create massive system overhead because regulations require verifying customer identity and filing reports when patterns appear. Each step creates system requirements.

Customer identification records need retention spanning decades and the ability to produce them during examinations. Your system must classify this data correctly, apply retention schedules automatically, and keep access logs showing appropriate controls.

Financial Services Data Governance: Legacy System Integration and Data Silos Implementation

Financial institutions hit specific obstacles in putting in governance capabilities due to regulatory complexity. The legacy system limits and organizational structures that grew before modern system patterns caused these implementation challenges to compound. 

Every bank architect eventually learns this lesson: you can’t fix governance problems by hiring more compliance officers. The institutions that succeed treat governance as an engineering problem, not a policy problem. The ones that fail keep writing new procedures for people to follow, while their systems make following those procedures impossible.

Legacy system integration without disruption

Banks run core systems from the 1970s and 80s on mainframes that process trillions in daily transactions. These systems can’t be replaced because they encode decades of business logic that isn’t fully documented.

WhereScape customer, Xerox Financial Services, needed governance across 16 countries, each running different software versions with varying business rules and compliance regulations. Reports were rigid and only available monthly. Multiple IT attempts failed because they couldn’t pull together data across different systems while maintaining data quality and consistency.

The solution builds abstraction layers, providing modern governance without changing core systems. You create metadata repositories documenting legacy fields in business terms, build quality checks in ETL processes, and keep lineage through transformation layers.

Xerox Financial Services built a 2.5TB warehouse with 95% of the work done by a single person. Within six months, they had a consolidated view of their leasing portfolio that was never achievable before. Auto-accept and auto-denial rates jumped from the low teens to 47.8% because the system could now access and query data consistently.

Someone needs to keep mappings between COBOL fields like “ACCT-STAT-CD” and business concepts like “account status.” Organizations that don’t treat this metadata as production code find governance documentation falling apart within months. Xerox avoided this because it used automation to keep documentation current as transformations changed.

The metadata management overhead creates a critical architectural decision: do you maintain mappings in a centralized repository that becomes a single point of failure, or distribute them across transformation layers where they’re harder to audit but more resilient? Most institutions choose centralized metadata repositories because regulators expect to see a single source of truth for data definitions, but this means your metadata database becomes as critical as your core banking systems. If that repository goes down or gets corrupted, your entire governance framework breaks.

Data silos from acquisition history

Banks grow through acquisitions, bringing completely different technology stacks. A bank that acquired a dozen regional banks might run that many different platforms, each with its own customer database and accounting rules.

Another WhereScape customer, Toyota Financial Services, needed to consolidate financial reporting data from offices in nine European countries. Each country kept its own data model and sent data in different formats. The group BI team was using Pentaho ETL with hand-coding methods that couldn’t keep pace.

Your system should create unified views across these silos without requiring system consolidation that might take years and cost hundreds of millions.

Toyota standardized the data model across all countries, requiring each to deliver data in a unified XML format daily. They moved 95% of infrastructure to Snowflake and switched to Data Vault. Within seven months, changes that would have taken weeks now took a single day.

The challenge is keeping consistency without forcing early harmonization. If two acquired banks define “deposits” differently for different market segments, forcing identical definitions might remove important business distinctions. Toyota’s approach standardized the delivery format while respecting country-specific business rules.

Showing architecture value beyond compliance

Business units resist governance capabilities they see as obstacles. A trader needing specific data doesn’t appreciate three-day approval cycles through governance committees.

The solution requires showing system value in business terms. When your system finds data quality issues before they reach regulatory reports, it prevents enforcement actions. When lineage tracking helps analysts find data quickly, that improves productivity.

Xerox Financial Services proved this when its system delivered measurable business value beyond compliance. The Volume Activation Report provided daily sales performance snapshots. Risk was reduced by gaining visibility into all financial agreements across regions. Checking a company’s total exposure required contacting each region individually.

When Xerox showed its system automated credit approval decisions and improved auto-accept rates from the low teens to nearly 48%, business units understood the value. 

Balancing security with accessibility

Security teams want to limit access to prevent breaches, while business analysts need broad access to identify trends. You’ll need to find solutions to these tensions through risk-based controls: strong authentication, network segmentation limiting where sensitive data can flow, data masking for non-production environments, and detailed logging to detect unusual access patterns.

Organizations that default to denying access create shadow systems where business units pull data to desktop databases or spreadsheets to avoid governance controls. Those shadow systems become the actual security risk because they lack the logging and quality checks that official systems provide.

Data Governance in Financial Services: Deployment Architecture, Audit Trails, and Metadata Components

Component selection should take into account the regulatory requirements that general-purpose platforms don’t expect.

Deployment architecture and data residency 

Cloud-based SaaS platforms create problems for financial institutions with data residency requirements or policies against hosting sensitive data outside their security perimeter. A platform like WhereScape offers self-hosting that can be deployed on-premise or in your virtual private cloud. Your data never leaves your secure environment or demilitarized zone (DMZ). You maintain full control over the network, access levels, encryption, and change management to meet standards such as GDPR and regional data residency requirements.

On-premise deployment removes questions about where processing happens and whether data crossed regulatory boundaries. Under DORA, firms must evidence robust governance of ICT providers, including contractual audit and access rights, termination and exit strategies, data location transparency, and controls over sub-outsourcing. These requirements influence cloud and SaaS patterns as much as on-premise deployments. Hybrid systems let institutions keep sensitive data on-premise while using cloud services for less-sensitive functions like metadata management.

Audit trail capabilities for data governance in financial services

System components for financial services should also create audit trails detailed enough to rebuild past decisions and show that controls worked correctly. Generic audit logs capturing “user X accessed table Y at timestamp Z” don’t provide enough context for regulatory examinations.

Required audit detail includes:

• What policy authorized the access
  
• What business justification supported any exceptions
  
• Which specific records were viewed during the session
  
• What actions were taken with the accessed data

Unchangeable audit logs prevent tampering that might hide unauthorized access or data changes. Retention requirements for audit data often exceed those for the underlying business data.

You might only need to keep customer transaction records for seven years, but audit logs showing who accessed those records might need keeping on file for ten years.

DORA’s incident-reporting framework relies on audit-ready evidence, so capture of classifications, timelines, actions taken, and communication trails should be systematic rather than ad-hoc.

Metadata management depth

Financial services systems require metadata tracking to capture business logic and regulatory interpretations. When a calculated field in a regulatory report uses a specific formula from regulatory guidance, the metadata should always document:

• Which guidance section defines that calculation
  
• What assumptions apply
  
• How the code handles edge cases

Details let institutions respond to regulatory questions without depending on knowledge from developers who might have left. 

Integration with existing systems

System components must connect with source systems ranging from modern cloud platforms to decades-old mainframes. The component needs strong integration capabilities that work with whatever systems currently hold real data.

API quality matters more than API quantity:

• Pre-built connectors for popular databases help
  
• Financial institutions always need custom integration with proprietary systems
  
• The platform needs well-documented APIs and example code

Change data capture (CDC) capabilities determine whether your system can keep up with source system updates. Real-time CDC lets systems track data changes as they happen instead of finding changes during nightly batch loads.

Calculating implementation costs

License costs are only part of the expenses. Implementation services, ongoing support, training, and internal resources all add to the total cost of ownership.

Hidden costs often appear after deployment in specialized skills your team doesn’t have, requiring expensive consultants or vendor help for customization, meaning you’re paying professional services fees for the long term.

Financial Services Data Governance Maturity: Regulatory Compliance Indicators and Operational Metrics

Financial institutions need clear measures of system effectiveness to guide improvement efforts and show progress to regulators and senior management.

Regulatory compliance indicators for data governance in financial services

The most basic measure: making sure your system passes regulatory examinations. Institutions track examination findings related to data capabilities. Zero findings represent ideal maturity instead of realistic expectations, but reducing severity and frequency shows improvement.

Tracking restatement frequency shows whether your quality controls detect issues before they reach regulators. Turnaround time for regulatory data requests shows operational maturity.

Operational efficiency metrics 

How quickly can you find and fix data quality issues? Tracking fix time from detection through validation shows whether your system handles problems efficiently or creates bottlenecks that delay fixes.

Speed matters for data access, too. If analysts wait days for routine data access, your process overhead needs fixing. Mature systems automate routine requests while keeping appropriate controls for sensitive data.

You also want to watch for shadow systems where business units bypass official systems to keep Excel files or desktop databases. If this happens, your system isn’t working regardless of the documentation. Tracking and reducing shadow systems shows that systems add value instead of being obstacles.

Track DORA-specific KPIs such as time to classify an ICT incident, time to assemble required data for authority notifications, and completion of annual testing plans.

Technical capability maturity: three questions to ask

For measuring technical capability maturity, you want to ask yourself three questions to assess how well your system scales and responds to problems:

• What percentage of your data estate has documented lineage? Higher automated lineage coverage shows better sustainability because manual lineage documentation always falls out of sync with actual system behavior.
  
• How many of your governance policies run through system controls versus depending on manual compliance? Policies encoded in access controls and data quality checks enforce themselves reliably, while policies that need people to remember and follow procedures fail eventually.
  
• When you find data quality issues in production systems, how quickly can you identify affected reports, determine root causes, and put in corrections? Mature systems keep documentation and tools that support rapid incident response instead of requiring case-by-case analysis during every incident.

Building Financial Services Data Governance Architecture That Passes Regulatory Audits

Successful implementations depend on matching organizational structure to technical capabilities. You can’t build governance architecture that works if roles lack authority to enforce standards, and you can’t scale manual governance processes across the data volumes financial institutions handle daily. 

The implementations that survive regulatory examination and support business operations combine clear ownership with automation that enforces policies through system controls rather than procedures.

Define ownership with enforcement authority

Someone needs clear authority to reject projects that violate system standards. Without enforcement authority, governance becomes advisory and gets ignored under deadline pressure.

• Chief data officers carry accountability for system success but rarely have direct authority over all systems and processes. The role requires influence across technology, operations, risk management, and compliance functions. Successful chief data officers establish councils with executive representation from each business area.
  
• Data stewards operate at the domain level, translating policy into practice within specific business areas. A steward for customer data owns defining what constitutes a “customer” across channels, resolving conflicts when systems use different identifiers, and ensuring customer records meet quality standards.
  
• Data architects implement decisions through system design, but they need authority to reject designs that violate principles. An architect who can’t push back when developers propose schema changes that break lineage tracking becomes decorative rather than functional.

Start with regulatory requirements

Pick a single regulatory report that causes frequent problems, build a complete architecture for just that report’s data chain, and show value before expanding the scope. Proving that systems prevent regulatory issues builds organizational support better than theoretical discussions about data management principles.

Build automation that scales with your team

Manual processes can’t scale to the data volumes that financial institutions process daily. They also can’t keep up as systems change continuously. Data Vault automation through WhereScape provides a foundation for scalable architecture by creating consistent structures, capturing lineage automatically, and keeping documentation as systems change.

Automation lets small, focused teams accomplish what used to require large governance departments. The metadata-driven approach means architects and stewards can maintain governance without manual overhead that creates bottlenecks.

Establish policies that systems enforce automatically..

The only data policies that actually get followed at scale are those encoded in system logic rather than left as written procedures. If the policy states analysts can access transaction data for assigned customers, the permission system evaluates that rule automatically. Vague policies like “analysts should generally avoid accessing data outside their territory” don’t translate to enforceable logic. Specific policies like “analysts can access transaction history for customers assigned to their territory ID in the CRM system” can be automated.

Focus on outcomes instead of activities.

Systems that measure success by policy documents published, training sessions run, or committee meetings held miss the point. Measure whether data quality improved, whether regulatory findings went down, and whether business users can find and access the data they need. If those outcomes aren’t improving, process activity doesn’t matter.

Turn Data Governance in Financial Services From a Burden into an Advantage

Data governance in financial services demands system capabilities that general-purpose data platforms don’t provide. You need detailed lineage tracing calculations through complex transformations, audit trails satisfying regulatory examination requirements, and deployment options meeting data residency limits.

WhereScape delivers these capabilities through metadata-driven automation that creates governance documentation by building data warehouses instead of requiring separate documentation efforts. With WhereScape, the technical record of how data moves and changes stays aligned with every update you make. Quality checks follow the same patterns wherever they’re deployed, and the metadata produced during development feeds the documentation your governance teams rely on.

WhereScape also runs entirely inside your own infrastructure. You can deploy it on-premises or in a private cloud you control, which keeps all sensitive information inside your secured network perimeter. Your team manages the access model, the encryption standards, and the change procedures.

Request a demo to see how automation can transform your data systems from a compliance burden into a process that stays accurate as your environment changes.

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