A governance framework for
strategic success

This isn’t theoretical. It’s drawn from organizations that have successfully navigated the complexity of modern cloud transformation while laying the foundation for AI innovation.

Define the operating model: Begin with clarity — establish your governance framework and policy strategy. This sets the foundation for how data moves through your organization, ensuring every stakeholder understands their role in creating value. This isn’t about restricting access — it’s about creating the conditions for scale
Discover and curate data: Transform scattered data assets into a coherent, intelligent network. By bringing your metadata into a central repository, you illuminate not just what data exists, but how it flows and creates value. This is where fragmented governance gives way to unified visibility and control
Assess data asset readiness: Move beyond technical metrics to evaluate strategic fit and governance maturity. This comprehensive assessment examines data lineage, platform alignment and business value — ensuring every migration decision advances your strategic objectives
Determine data quality health: Quality isn’t a checkpoint — it’s a continuous journey that starts at the source. By embedding quality monitoring and enrichment into your data lifecycle, you transform data quality from a technical requirement into a strategic advantage

This framework isn’t about adding layers of control — it’s about creating the foundation for Data Confidence, where your people can trust, comply and consume data across every system and use case.

What is unified governance?

Unlike fragmented approaches that tether control to specific systems, unified governance automates visibility and policy enforcement across all data sources while bringing both technical and business users into the fold. It creates the foundation to accelerate data initiatives and AI innovation, safely.

Exploring data
ownership in
cloud migration

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Step 1
Define the operating
model and policy strategy

Your operating model structures how data flows through your organization, while your policy strategy creates the guardrails for safe innovation. This foundation determines how effectively you can scale data initiatives.

Start by documenting clear ownership of key
roles in your business processes. Then engage stakeholders to establish your governance pillars and policies—for instance, requiring end-to-end lineage documentation before migration.

Here’s what this looks like

Data policy creation and management
Persona-based user experiences
Automated workflows to facilitate governance
Data retention rules
Data access management

Step 2
Discover and/or curate data across disparate systems

With your operating model in place, it's time to illuminate your data landscape. Unified governance brings all your metadata into a central repository, revealing not just what data exists, but how it's used and transformed. With comprehensive visibility, now your team can accelerate the migration timeline by quickly identifying and prioritizing high-value, business-critical data assets.

Plus, enhanced discovery capabilities streamline access to trusted information. So it’s easier than ever to create an inventory of certified data products, including reports, AI use cases, models, applications and more. And you can easily identify sensitive data and apply data policies to ensure proper data handling.

Here’s what this looks like

Intuitive data discovery across disparate systems
Built-in business glossary
Native integrations for metadata ingestion
Automated extraction and maintenance of
data lineage
Automated data classification

Here’s what this looks like

Intuitive data discovery across
disparate systems
Built-in business glossary
Native integrations for metadata ingestion
Automated extraction and maintenance
of data lineage
Automated data classification
Certified reports, reference and master
data, and data sources
Certified data products
Faceted and semantic search
Filters, matchers, and recommenders

More than 9 out of 10 have either a cloud-first or a hybrid cloud policy

Sources: TechTarget, ‘Is a cloud-first strategy right for you?’

Step 3
Assess data asset readiness and feasibility

Step 3 is all about smart data prioritization. With unified governance in place, you can use data lineage to spot the data that’s powering your critical data products, and assess its readiness and feasibility. Then you’re ready to make smart decisions about data migration prioritization, you can be sure you’re leveraging the right cloud technologies for each data workload. The result: Faster time to value and adoption.You’ll want to cover three essential areas:

Understanding data lineage: Getting an end-to-end view of the data lifecycle — from source to destination — offers a clear view of data flow and dependencies
Navigating multi- and hybrid-cloud environments: Ensuring your use cases are a good match for the cloud platform you’re considering is critical. Is your use case better suited for another platform in your stack
Leveraging analytics: Running analytics on your data can help you determine if it needs to be migrated. Ask yourself these three questions:

Leveraging analytics: Running analytics on your data can help you determine if it needs to be migrated. Ask yourself these three questions:

Do we still utilize this data
Is the migration economically
and strategically worthwhile
Is there any chance we’ll soon
phase out this data?

Here’s what this looks like

Navigable visualizations of
technical and business lineage
Context enrichment with business,
security and other not-technical details
Monitoring and transparency throughout the migration journey — for example, comparing
the transformations that occur on-prem with
the transformation happening in the cloud

Step 4
Determine data quality health

The final and most important step is determining the quality of your data. And a pivotal part of planning this step is deciding when to do it. Should it be done before, during or after migration?

In almost all cases, you’ll want to implement data quality and observability checks as soon as possible. Why? Getting an accurate view of your existing data landscape allows you to address any issues upfront and avoid wasteful migration of certain data, and unnecessary costs. It helps prioritization for higher-quality sources that are both trustworthy and useful in the first place, accelerating the process and aiding in adoption and trust with stakeholders.

On the other hand, some scenarios might lend themselves to conducting a data quality assessment after migration. This approach may

make sense if your team wants to utilize the enhanced processing speed and scale of your cloud platform for initial checks, or already has a good idea of its data quality. With pushdown, some solutions also utilize cloud compute remotely for data quality and observability. This is most often utilized for continual checks to ensure data quality after migration.

Here’s what this looks like

Repository of industry-specific and custom quality rules
Proactive, automated approach to
data quality routines and deployment
Continuous data pipeline monitoring
Automated issue remediation flagging