What is Cloud Data Migration? Essential Insights for the Modern Data Leader

How much potential does your data really have? If it’s left locked away inside dozens of legacy silos or stale reports, odds are you’ll never know. 

But what if your data could finally deliver on its full potential to drive your business forward? That’s the promise of cloud data migration. When implemented correctly, it can free data from legacy systems and siloed point solutions so that it flows seamlessly across your organization, using cloud data platforms such as Snowflake and Databricks. 

These cloud platforms are springboards for an exciting array of new data use cases the old on-prem data warehouse could never support. Business users, analysts, and data specialists have a new place to find, understand, and trust the data they need to drive the business forward. 

So what’s stopping you? 

According to Gartner, 83% of data migration projects fail or exceed the timeline and budget. Echoing a similar sentiment, “Half of cloud-native transformations are abject failures,” claims Matt Barrington, emerging technologies leader at EY.

When project execution fails this frequently (and spectacularly!), we need to look closely at the strategies used. Many teams assume they can simply “lift and shift” their data into the cloud, and it will be business as usual. Cloud migration without transformation simply means your shiny, modern data stack inherits all the duplication, inefficiency, and technical debt you were trying to remove from the legacy platforms. 

Other teams fumble the project scoping, leading to higher costs and greater business risk. In the worst cases, cloud consumption costs are far higher than projected, forcing teams into difficult conversations with their finance leaders.  

But why migrate at all? How do you migrate on time and on budget? And what are the benefits?

In this blog, we’ll explore how cloud migration must ensure solid foundations before and after the migration itself. From understanding and planning the movement of key data assets and their dependencies to managing data safely and reliably through this process to migrating with confidence to drive efficiency and performance in our scaled cloud service, let’s take a deeper look at the concept of cloud migration in action.

What is Cloud Data Migration?

Cloud data migration is the process of transferring data, applications, and other business elements from an on-premises infrastructure or other cloud environments to a cloud computing platform. This involves moving data from servers located in physical data centers and databases to cloud-based servers accessible via the Internet. Migrated workloads are often deployed into new cloud data architectures such as data warehouses or data lakes using platforms like Snowflake or Databricks. 

Why is Migration to the Cloud Important?

Cloud data migration aims to take advantage of the power of cloud computing, which offers increased scalability, flexibility, cost-efficiency, and enhanced security. This process often includes re-evaluating and optimizing data and applications to fully utilize the cloud's capabilities, ensuring they operate more efficiently and effectively in the new environment. 

This leads to business teams implementing digital transformation programs, such as streamlining operations, adapting quicker to market shifts with agile project deliveries, and implementing advanced technologies like analytics and artificial intelligence (AI), which drive innovation, competitive advantage, and growth. 

What Platforms Are Teams Migrating From? 

Many organizations are reevaluating their legacy data management and analytics infrastructure. Platforms that once served as the backbone of enterprise data operations are now being reassessed in favor of more modern, scalable, and flexible cloud-based solutions. This shift is driven by the need to handle increasing data volumes, support advanced analytics, and reduce the overhead of maintaining on-premises systems. 

Let’s explore the main traditional platforms businesses are moving away from and examine the challenges associated with these environments. 

Teradata

Teradata has been a cornerstone in data warehousing for many enterprises, offering robust analytics capabilities over several decades. However, with the rise of cloud computing, CIOs are increasingly considering migrating away from Teradata due to the high costs associated with maintaining on-premises infrastructure and licensing fees, which can be prohibitive. Cloud data solutions offer a more cost-effective, pay-as-you-go model that scales with the business. 

Cloudera

Cloudera, known for its Hadoop-based data management solutions, was an early leader in the big data analytics market. However, the complexity and cost of managing Hadoop clusters on-premises are leading CIOs to consider cloud-based alternatives and modern data stack architectures that support a wider range of data workloads while reducing administrative overhead.

IBM DB2

DB2 remains a trusted database management system for many large enterprises. Despite its reliability, the challenges of high maintenance costs, lack of scalability, and the need for specialized skills and connectivity to manage and work with data stored in DB2 environments means that teams are looking at alternative cloud-based solutions for the longer term. 

IBM Netezza

IBM Netezza was a popular technology solution for high-performance data warehousing capabilities. However, the advent of scalable cloud-based data warehousing solutions has introduced more flexible and cost-effective options that can lead to significant performance improvements and cost savings. 

Oracle

Oracle's database solutions are widely used across various industries. Despite its impressive legacy, many technology leaders are now considering migrating to competitor cloud-based data platforms to leverage these modern solutions' flexibility, scalability, and cost-efficiency while reducing the risk of vendor lock-in.

Benefits of Migrating Data to the Cloud

Migrating to the cloud is a vital part of digital transformation, offering advantages that include:

Cost Efficiency

Cloud data migration eliminates the need for extensive physical infrastructure and associated costs. Traditionally, IT systems have required significant hardware, maintenance, and physical space investments. By moving data and business workloads to the cloud, businesses transition from capital expenditure (CapEx) to operational expenditure (OpEx) investments. This means predictable, subscription-based costs that can be adjusted and scaled according to usage, making IT spending more manageable and aligned with actual needs. Additionally, cloud providers handle hardware upgrades and maintenance, further reducing expenses and freeing up internal resources for other strategic initiatives. Gartner estimates that by 2025, 51% of IT spending in application software, infrastructure software, business process services, and system infrastructure will have shifted from traditional solutions to the public cloud. 

Scalability

Cloud data platforms enable businesses to scale their compute resources up or down, adapting to changing demands without disruptions or heavy investments. This flexibility is crucial to compete and maintain cost certainty, where demand can be unpredictable. With cloud services, companies can quickly expand their computing power during peak times or reduce it during slower periods, ensuring optimal performance without over-investing in infrastructure that might remain underutilized. This agility supports business growth and responsiveness, providing a competitive advantage. With the growth in cloud-native workloads, there is an increasing need for on-demand scalable infrastructure to support these workloads effectively, including the ability to handle large-scale data processing and storage requirements dynamically.  

Global Accessibility

Cloud technology ensures data and applications are accessible anywhere, promoting effective remote work and collaborative efficiency. In a globalized business environment where teams are often distributed across different locations, cloud data migration enables seamless access to critical data and applications, ensuring that employees can work effectively anywhere with an internet connection. This enhances productivity and supports business continuity and disaster recovery, as data is not tied to a single physical location.

Security and Compliance

Leading cloud service providers invest in robust security measures, enhancing data protection and simplifying compliance adherence. These providers deploy advanced security protocols, such as encryption, identity and access management, and continuous monitoring, to protect sensitive data. Moreover, they stay updated with the latest regulatory requirements, helping businesses meet compliance standards with less effort. This level of security and compliance is often difficult and expensive to achieve with on-premises solutions, providing peace of mind and reducing risk. Cloud data platforms like Databricks and Snowflake offer extensive multi-layer security and compliance features such as multifactor authentication, annual independent third-party audits, dynamic data masking, and role-based access controls. 

Innovation Catalyst

Cloud services offer access to advanced technologies without hosting, setting up, or maintaining local environments on-premises. By migrating to cloud data platforms, businesses can deploy scalable, cutting-edge capabilities such as artificial intelligence (AI), machine learning (ML), and analytics without significant upfront investment. This is a great example of how the democratization of technology enables companies of all sizes to innovate and implement new solutions quickly through the cloud, driving business transformation and keeping pace with industry advancements. Global spending on AI software is projected to surge from $124 billion in 2022 to $297 billion in 2027, as Gartner forecasts the market will grow at a 19.1% compound annual growth rate in the next six years, driven heavily by cloud workloads.

What are the Different Strategies for Cloud Data Migration?

As businesses migrate their data and workloads to the cloud, several strategic approaches must be considered. Each approach offers unique benefits, and understanding these can help you make informed decisions that align with your business goals. 

Here are several key strategies to explore for your cloud data migration projects:

Rehost (Lift and Shift)

Overview: Moving existing applications to the cloud with minimal changes.

Benefits:

• Speed: A lift-and-shift approach can quickly migrate applications, data, and workloads to a modern cloud data platform.

• Initial cost-effectiveness: Reduces initial cloud data migration costs as no significant modifications are needed.

• Minimal Disruption: Less disruption to current operations, making it a straightforward approach for quick cloud data migration and adoption.

Cautions: A simple lift-and-shift approach may not make the most out of cloud-native capabilities, potentially missing out on performance and cost optimizations available through more integrated cloud data migration options. Worse yet, lift and shift risks moving low-quality, unused data to the cloud resulting in expensive migration costs, ongoing storage costs, and delivering low-quality analytical insights. 

Ask your cloud data migration team: Are we missing opportunities for long-term efficiency and optimization gains by oversimplifying the migration and not considering the full range of cloud-native features?

Refactor

Overview: This strategy involves making data models, data architecture, or application-level changes to optimize for the cloud environment, taking advantage of cloud services and scalability.

Benefits:

• Optimization: Data workloads will perform more efficiently in the cloud.

• Scalability: Refactored data workloads leverage the ability to scale resources up or down based on demand.

• Cost Savings: Optimized data/analytics applications can reduce operational costs over time through better resource utilization.

Cautions: Refactoring workloads, applications, or models requires significant time and effort to modify the architecture, underlying data flows, or business logic which can delay the overall migration process and incur higher short-term costs.

Ask your cloud data migration team: Can we afford the upfront time and financial investment needed to refactor, and will the long-term benefits justify these costs?

Re-architect

Overview: This goes beyond refactoring and involves the complete redesign of applications to take full advantage of cloud-native features and capabilities. 

Benefits:

• Reach Full Cloud Potential: A re-architecture considers all the advanced features and capabilities of the cloud as part of the solution delivery.

• Enhanced Performance: Often achieves superior performance and reliability over lift-and-shift migrations.

• Future-Proofing: A re-architected data solution sets a solid foundation for future innovations and technological advancements.

Cautions: Re-architecting applications is non-trivial: it’s a complex and resource-intensive process, often requiring substantial investment in both time and skilled personnel, including potential downtime for the business during the launch of a re-architected solution.

Ask your cloud data migration team: Do we have the necessary resources and expertise to undertake such a significant transformation, and what are the risks if we fall short?

Rebuild

Overview: Applications are rebuilt from the ground up using cloud-native services. 

Benefits:

• Maximum Optimization: Rebuilding a data pipeline, analytic application, or model from the ground up offers a rare opportunity to optimize the solution for cloud performance and efficiency.

• Customized Solutions: A rebuild enables teams to deliver tailored applications that meet specific business needs.

• Innovation: An application rebuild encourages the development of modern, innovative solutions that can potentially leapfrog competitors and drive business growth.

Cautions: Rebuilding applications from the ground up is a huge undertaking that demands considerable development effort and resources. It could potentially divert focus from core business activities and cause significant project delays.

Ask your cloud data migration team: How will dedicating resources to rebuilding impact our current operations, and can we manage this shift without compromising our core business functions?

Replace

Overview: It may be more efficient to replace an existing data application or pipeline with a commercial cloud-based Software as a Service (SaaS) solution rather than migrate and upgrade the legacy components. 

Benefits:

• Efficiency: Replacement of a component generally avoids the complexities of migration by adopting ready-made solutions that often require lightweight configuration rather than heavy development.

• Focus on Core Business: Replacing legacy components frees resources to focus on core business activities rather than IT maintenance.

• Up-to-date: SaaS providers typically offer the latest features and updates, ensuring your software is always current and doesn’t demand heavy maintenance or upgrade cycles from consumers and their IT teams.

Cautions: Replacing existing data components or pipelines with SaaS solutions can lead to loss of customization and control over the software, which may not align with specific or long-term business requirements.

Ask your cloud data migration team: Will adopting a replacement SaaS solution meet our unique business needs, and how will we measure and manage the trade-off between customization and standardization?

Finally, let’s also talk about two strategies that will likely form part of your wider cloud data migration strategy: the decision to retire or decommission a data source or application and the option to keep legacy systems operational until a future phase in the migration program.  

Retire

Overview: Not all applications or components must be moved to the cloud. It’s a common goal for many to be decommissioned or retired as part of the wider migration and transformation program.

Benefits:

• Cost Reduction: Retiring legacy data silos and applications helps eliminate the costs associated with maintaining outdated data stack software.

• Simplified IT Landscape: Reducing legacy software helps reduce the overall complexity in your IT environment.

• Resource Reallocation: Having fewer resources focused on maintaining legacy data platforms frees up resources to focus on more critical applications and modern data stack initiatives.

Cautions: Retiring applications or data stack components demands careful evaluation before the “kill switch” is triggered. Valuable data, business logic or other functionality could be lost if care isn’t taken in the retirement process. 

Ask your cloud data migration team: What critical functionalities and data might we lose by retiring these applications, and how will this impact our operations?

Retain

Overview: Some legacy applications or components may be too complex or costly to migrate. In such cases, they are retained in the existing environment while new applications are built in the cloud.

Benefits:

• Cost-Effective: Avoids the high costs and risks associated with migrating complex or critical legacy applications.

• Provides Stability: Maintains the stability of applications that are essential to ongoing operations.

• Strategic Planning: Provides time to develop a long-term strategy for future migration or replacement.

Cautions: Retaining legacy applications can hinder overall IT modernization efforts, leading to a fragmented IT environment mired in so-called technical debt and potential integration challenges with new cloud-based applications.

Ask your cloud data migration team: How will retaining these legacy systems affect our overall IT strategy and budgets, and can we manage any future integration challenges?

While each cloud migration strategy offers distinct benefits, it is crucial to consider potential drawbacks and address key questions with a broad consensus of cross-functional teams and experts. Balancing the advantages with these counterarguments ensures a comprehensive cloud migration approach that aligns with immediate needs and long-term business objectives.

Data Intelligence Helps Craft Your Cloud Migration Strategy

Moving to the cloud isn't as simple as flipping a switch. Moving to the cloud requires a well-planned strategy, often taking months to assemble when companies don’t have the requisite visibility into either their business ambitions in the cloud or into their legacy data sources that need to be migrated. Many organizations are uncertain about the location of their data across legacy silos or even whether it is suitable for migration.

Data intelligence plays a crucial role in building a winning strategy for cloud data migration. IDC defines data intelligence as leveraging business, technical, relational, and operational metadata to provide transparency and understanding of data. Having clear intelligence about your data provides a critical foundation for an informed and well-planned migration to the cloud.

A data intelligence platform like Alation can streamline this process by providing comprehensive visibility into a data landscape across file systems, applications, databases, and business intelligence platforms. By understanding data profiles, classifications, quality, locations, and lineage, organizations can make informed decisions about what data to migrate to the cloud. This transparency helps prioritize critical data for migration and avoids moving obsolete or redundant data, reducing costs and complexity.

Before planning your cloud migration, use data intelligence to align key stakeholders on your business and data strategies. Here are some key best practices for your cloud migration journey supported by data intelligence with Alation.

How Data Intelligence Supports Cloud Migration

1. Data Strategy Drives Cloud Strategy

To determine which data to migrate to the cloud, ask: What data is most popular? Which assets are most trusted? Which are deprecated? This visibility is absolutely essential for a migration to succeed. 

Alation's data catalog can answer these questions by using metadata to label the most useful data, prioritizing it for cloud migration. Alation also provides deeper insights into cloud migration effects by offering visibility into how downstream processes consume data. Anyone can explore how data moves and impacts processes and analytical applications through interactive data lineage. This transparency for data lineage reduces risk and ensures that the migration can be completed successfully.

2. Plan for Governance and Data Quality

Effective data governance ensures that data is consistent, trustworthy, and doesn't get misused. It involves a set of practices and principles that help to ensure high data quality throughout its lifecycle, enabling businesses to meet regulatory requirements and drive effective decision-making. “Data quality is directly linked to the quality of decision making,” says Melody Chien, Senior Director Analyst, Gartner.

Ensuring data governance and quality during cloud data migration is crucial, especially since on-premise data governance policies and practices might not directly translate to the target cloud environment. A data intelligence platform like Alation helps maintain high data governance and quality standards throughout the migration process.

Alation’s data governance capabilities ensure that data policies are enforced consistently across data in both legacy silos and cloud data platforms. Its data stewardship and data health and profiling features help identify and address data quality issues before they become problematic in the migrated environment.

By maintaining robust governance and high data quality, you can ensure compliance with regulatory requirements and enhance the reliability of your data analytics in the cloud. Make sure that your migration strategy incorporates on-premises security policies and features. Make a plan to implement solutions that can provide similar security or compliance.

3. Don’t Underestimate Cost Management in the Cloud

The cost of cloud data migration depends on several factors, including data volumes and workload complexity. Post-migration costs, such as employee training and potential fees for moving data, should also be considered. According to a survey conducted by Foundry, 82% of organizations identified cloud cost management as their top priority in 2023. 

Data intelligence platforms such as Alation offer insights into data usage that can help identify and prioritize the most critical data for migration and rationalize and streamline assets once the migration has been completed.

4. Ensure a Continuous Service during Migration

Consider the cost of business downtime during data migration and plan ways to minimize disruption or significant outages to reduce overall cloud migration costs. One approach could be to break the migration down into smaller, manageable phases rather than a single large move. This can reduce risk and allow for testing and adjustments at each stage. 

Ensure that all critical data is backed up before starting the migration. Regularly test these backups to ensure they can be restored quickly in case of an issue. Plan the migration during periods of low business activity to minimize the impact on operations. Consider weekends or holiday periods when possible. Above all, keep all stakeholders informed about the migration plan, potential risks, and expected downtime. Clear communication helps to manage expectations and prepare teams for any disruptions.

Alation's real-time data lineage and impact analysis can help identify potential disruptions through dependency analysis and support planning for seamless transitions with collaboration between leaders, subject matter experts, and engineering teams coordinated through Alation directly or embedded within popular tools such as Slack or Teams.

5. User Adoption is a Valuable Measure of ROI

When measuring the return on investment of cloud data migration, one important measure is user adoption, which can significantly influence the success and perceived value of the migration. Effective training and user-friendly platforms are crucial for maximizing ROI since not everyone will understand the new cloud technology immediately. Transparency and proper training can help avoid roadblocks. 

Alation’s user-friendly interface and comprehensive training can facilitate this transition to the cloud, ensuring that all team members are onboarded effectively.

Cloud Data Migration Success Stories

Spark NZ and Texas Mutual Insurance Company successfully embarked on a transformative journey to accelerate their data migration, reporting, and machine learning capabilities.

Spark NZ: Challenges & Results

Challenges: Spark NZ, New Zealand's largest digital services provider, struggled with siloed and scattered data across its operations. Despite having a highly skilled team, analysts and engineers spent significant time searching for data rather than leveraging it for decision-making. Spark NZ aimed to become more data-driven and improve its data governance as it prepared for cloud data migration.

Results: With Alation's support, Spark NZ's data experts can quickly find and analyze Snowflake data, leading to faster reporting and strategic decision-making. The company expedited its cloud migration and enhanced data trust, resulting in a 30% reduction in delivery time for Power BI dashboards. Spark NZ's investment in machine learning also paid off, increasing mobile revenue by 9% in FY23.

You can dive deeper into the details by exploring our Spark NZ Customer Case Study.

Texas Mutual Insurance Company: Challenges & Results

Challenges: As the largest workers' compensation insurance company in Texas, Texas Mutual aimed to improve worker safety by leveraging data insights. However, their complex data landscape, siloed data warehouses, and lack of data governance hindered their efforts. The absence of trusted data caused decisions based on instinct rather than data, resulting in delays in report creation and data accuracy concerns.

Results: Alation empowered Texas Mutual to make data-driven decisions 80% faster, enabling senior executives to deploy capital more efficiently. By adopting Alation Cloud Service, the company reduced administration, maintenance, and resources needed for data governance. The transition to the cloud version of Alation was seamless and significantly improved the company's data management processes.

You can dive deeper into the details by exploring our Texas Mutual Insurance Company Case Study .

These success stories from Spark NZ and Texas Mutual illustrate the transformative power of effective data migration and governance. They result in faster decision-making, improved data trust, and enhanced business agility. Both companies are well-positioned to thrive in the data-driven digital era through their partnership with Alation.

Choosing a Service Provider for Cloud Data Migration: Snowflake, Databricks, and AWS 

Planning your cloud migration strategy is crucial for success. Equally important is selecting the right cloud data platform provider. Think of it as moving homes; you wouldn’t pick a moving company at random. You would look for movers with experience, the right equipment, and excellent reviews to ensure a smooth transition. The same principle applies to your cloud data migration partners.

Choosing a trusted cloud provider is vital because the migration process involves handling critical business data. A reliable provider will have the necessary experience, comprehensive plans for managing internal data infrastructure issues, and robust support for cloud data ecosystems. 

Known for its advanced data warehousing capabilities, Snowflake offers a highly scalable and flexible platform that separates storage and compute resources. This architecture is ideal for businesses looking to perform complex data analytics without worrying about performance issues. Indeed, Snowflake customers report faster query performance after data migration and deployment. 

Databricks excels in big data processing and machine learning. Its unified analytics platform allows seamless collaboration across data engineering, data science, and business analytics teams. This makes it an excellent choice for organizations aiming to leverage AI and machine learning for business insights against migrated data from legacy silos. Companies using Databricks see a 417% ROI within three years due to cloud data migration and unified analytics. 

As an established leader in the cloud market with a 31% market share, Amazon Web Services (AWS) offers a comprehensive suite of services, including powerful data storage, database compute, machine learning, and analytics tools. This makes it a reliable choice for businesses of all sizes looking to scale rapidly and innovate.

Vendor Flexibility

Ensure that the nature of the contract you establish with the cloud data platform provider is well-understood and offers acceptable levels of flexibility. In cloud contracts, this helps avoid being locked into a single provider and their tools or services and ensures the ability to switch providers if necessary. To achieve this, ensure your contract includes provisions for easy migration without excessive penalties and clearly outline your cloud objectives to your provider. Understanding service-level agreements (SLAs) is vital, particularly regarding uptime, data security, and migration support, to ensure they meet your business needs. This approach helps prevent being trapped in suboptimal situations and ensures alignment with your strategic goals.

Are you planning your cloud data migration?

Cloud data migration can be complex and time-consuming. Still, careful planning can yield exceptional returns and set up your entire organization for long-term success with a scalable, high-quality, governed data platform. As you plan your cloud data migration strategy, consider how the Alation data intelligence platform provides comprehensive visibility, governance, and self-service analytics across legacy data silos and modern data platforms, enabling everyone in the organization to find, understand, and trust the data throughout the migration journey.