When it comes to enterprise technology deployments, data migration is often the linchpin between a successful go-live and a painful, costly mess. A seamless transition doesn’t just happen—it requires strategic planning, the right tools, and a deep understanding of the data landscape. Without proper planning, data migration can derail even the most well-intentioned digital transformations, leading to data loss, compliance issues, and prolonged downtime.
In this blog, we’ll cover essential data migration best practices to help you avoid common pitfalls, preserve data integrity, and ensure your deployment stays on track. Whether you’re moving to a new ERP system, deploying Workday, or leveraging platforms like Databricks for advanced analytics, The Groove has the tools and expertise to help. Explore our enterprise technology services to learn more.
At its core, data migration is the process of transferring data from one system to another. But it’s far more than a copy-and-paste task. Data migration is the process of transferring data between storage types, formats, or computer systems, with as little change as possible. This migration occurs for a variety of reasons, including server or storage equipment replacements or upgrades, website consolidation, server maintenance, or data center relocation. It’s a strategic process that impacts business continuity, user adoption, compliance, and the integrity of your decision-making tools.
Migrating data across platforms—whether moving from legacy systems or transitioning to modern cloud solutions like Workday or Databricks—requires careful consideration of structure, quality, and compatibility. Without these, data can become fragmented, inaccessible, or even corrupted. Most deployments allow this to be done in a non-disruptive manner, such as concurrently while the host continues to perform its production tasks without disrupting the business.
Data migration is the bridge between where your organization is today and where you want to be. Done right, it powers accurate reporting, streamlined operations, and confident decision-making. Done wrong, it introduces risks that can ripple across every department.
For enterprise platforms like Databricks, which rely on clean, structured data for advanced analytics and machine learning models, any error in migration can jeopardize your insights. A seamless data migration strategy ensures your new system works as intended, improves user adoption, and unlocks business intelligence from day one.
The mapping granularity dictates how quickly the Metadata can be updated, how much extra capacity is required during the migration, and how quickly the previous location is marked as free. Smaller granularity means faster update, less space required, and quicker freeing up of old storage.
Automated and manual data remediation is commonly performed in migration to improve the quality of the data, eliminate redundant or obsolete information, and match the requirements of the new system.
A successful data migration strategy involves planning, assessment, execution, and verification, encompassing data audit, backup, and testing to ensure data integrity and minimize downtime.
Even the best technology can fail if the data behind it isn’t sound. These common pitfalls often surface during cloud data migration and can significantly delay or compromise your project.
Data needs to be moved when new applications are deployed or when applications are retired or merged. This process involves transformation of data to the format of the receiving new application or platform.
Data migration projects are frequently under-estimated or under-designed, because programmers are told to simply move the data. They do not engage in the analysis and design activities required for data integration.
When data is migrated without proper analysis, it often looks different from the data that came in through the normal processing or the migrated data may not work with the application.
Without a roadmap, your migration is flying blind. Every successful migration starts with a detailed data migration strategy that outlines:
A strong strategy helps teams anticipate challenges, manage expectations, and maintain control.
Garbage in, garbage out. Migrating bad or poorly structured data can lead to downstream issues that frustrate users and disrupt workflows. Data cleansing, deduplication, and mapping are critical steps that ensure consistency and prevent system errors.
Mapping legacy data to the structure of your new platform—like Databricks or Workday—requires careful alignment and sometimes transformation of formats or relationships.
If you skip testing, you’re inviting surprises. A comprehensive data migration testing approach includes:
Each phase plays a role in verifying that your migrated data behaves as expected.
Data security and compliance can’t be afterthoughts. Regulations like GDPR, HIPAA, and SOC 2 require strict safeguards during migration, including:
Neglecting these measures can result in legal repercussions and reputational damage.
To mitigate risks and achieve a seamless data migration, follow these tried-and-true best practices:
Before any data leaves its current system, you need to understand it. A full audit reveals:
Automated tools, including those integrated with platforms like Databricks, can assist with integrity checks and readiness scoring.
Choose between phased migration (gradual rollout) or big-bang migration (one-time switch), based on your risk appetite, budget, and system complexity. Consider:
A clear data migration checklist aligned with business goals ensures a smooth process.
Manual migration increases the risk of human error. Data migration tools—especially those with AI capabilities—can:
Databricks offers scalable, cloud-native pipelines that integrate with various data sources, making it a powerful tool for automation-heavy migrations.
Accurate data mapping ensures legacy fields correctly translate into the new system’s structure. If you’re moving to Workday or loading data into Databricks for ML models, ensure that your transformation logic maintains relational integrity and semantic consistency.
A Data Map will describe the data flows between input data sources and the applications that the data is used in. Also, if data is being stored in a data warehouse, the Data Map can be used to depict directional data from input source to database and any transformational process along the way. A Data Map can also be used to depict data moving from data warehouse to front-end applications, dashboards, or reports.
Build confidence in your migration with thorough testing. Testing should not be an afterthought, but the backbone of a successful data migration process. Without a structured testing strategy, you risk introducing corrupted records, losing critical data, or launching a system riddled with errors. According to Gartner, 83% of data migrations either fail or exceed their budgets and timelines, often due to inadequate testing.
Validate for:
Testing isn't a checkbox—it’s an ongoing process.
Even with airtight planning and testing, enterprise data migrations can go sideways. Prepare by:
At The Groove, we don’t just move data, we design migration strategies that align with your business goals, technical landscape, and user needs. We strive to make the migration process as seamless as possible while staying within plan and budget constraints.
Our consultants start with a deep analysis of your current data, infrastructure, and readiness. We will create a Data Flow map of the current process to make sure we have all the data identified. We will identify red flags and create tailored recommendations to streamline your path forward.
No two migrations are the same. We build custom playbooks that address complexity, minimize downtime, and account for unique use cases. Our team collaborates with stakeholders at every stage. We will create a tailored report and strategy document that will guide you through our best practice recommendations and our deployment plan for a successful data migration.
Once the platform is up and running, we will continue to support and guide you through any issues or opportunities that would arise. From post-migration tuning to user training, documentation, and data workflow optimization, we ensure long-term success—not just a smooth launch.
The top risks include data loss, corrupted records, system downtime, compliance violations, and user resistance.
Depending on the project size and complexity, migrations can take anywhere from several weeks to several months.
Conduct a data audit, remove duplicates, fill in missing values, and validate against source-of-truth systems. Creating a data flow diagram where all source systems and business critical systems use the data needed to drive the business, are identified.
Use encrypted channels, role-based access controls, and audit logs. Many platforms, like Databricks, have all the security measures in place along with data masking capabilities.
Automation reduces errors and saves time, but some manual oversight is always necessary—especially for edge cases and exception handling. This will also depend on where the data is coming from and the shape it is in.
Lack of planning, poor data quality, inadequate testing, underestimating effort, and not having a data flow diagram are common culprits. Requirement changes during the process is also an issue as it throws the entire planning process out from the original estimate which affects budgets. Business and IT not being on the same page.
Provide training, set expectations early, and include end users in testing to build familiarity. Having the correct knowledgeable individuals for each data source and platform that will be used during the migration process.
We combine strategy, technology, and experience to manage every stage of the migration process, from initial consultation to implementation and optimization—so you can focus on what comes next.
Whether you’re heading to Workday, leveraging Databricks, or tackling a multi-platform enterprise shift, The Groove has the experience to guide you every step of the way. Let’s get your deployment off to a smooth start. Contact us.