In today’s data-driven landscape, businesses rely on accurate and accessible data to drive decision-making and strategic growth. A well-executed data migration is crucial to ensuring that critical information remains intact, secure, and usable across systems.
This article outlines five best practices for a smooth and efficient data migration process. From planning and data assessment to validation and post-migration optimization, these best practices help mitigate risks and enhance data integrity. We’ll also explore common types of data migration and key challenges organizations may face along the way.
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What is data migration?
Data migration refers to transferring existing data from one storage system or computing environment to another. Organizations usually implement data migrations to upgrade systems, consolidate databases, transfer on-premise systems to cloud services, or during mergers and acquisitions.
The migration process typically involves data preparation, extraction, transformation, and loading into the new computing system. Whether you’re moving your data from an on-premise location to a public cloud, private cloud, hybrid cloud, or multi-cloud environment, you need to find a cost-effective, secure, and efficient way of migrating it to its new location.
Data migration best practices
Planning a successful data migration project requires effective planning. Here are the best practices when planning a data migration project for your organization:
- Define the objectives and scope of the project: Outline the purpose of your data migration project and define the scope by identifying the data to be migrated, its source systems, and the processes that will be included.
- Assess and analyze data: Conduct a comprehensive assessment of your current data source and target systems. Identify potential risks or challenges, including inconsistencies, duplicates, and compatibility issues.
- Create a data mapping plan: Data migration requires a detailed mapping plan for how data will be transferred and transformed. Consider how your organization will use the plan in the long term and include data management policies for data ingestion, transformation, and analysis.
- Conduct data cleansing: Analyze your source data thoroughly before transferring it to another system. The process involves preparing and validating data to detect and correct inaccurate information as needed. It’s essential to classify the cleansing rules during the cleansing process, which should be performed manually or by automation. In doing so, domain experts can focus on manual data cleansing while migration experts can develop automation solutions.
- Build a backup strategy: Before conducting data migration, ensure all data is backed up to prevent loss in case of errors. You should also create a system to regularly back up your raw data before cleansing and store copies in multiple locations. You should also test these backups to make sure that you can recover your data.
6 Rs of data migration
The “6 Rs” of data migration, often referred to in the context of cloud migration, are the six key strategies organizations can use when moving their data.
- Rehost: Also known as “lift and shift,” this strategy involves moving applications with minimal changes. It is usually used for quick migrations or legacy systems that don’t need extensive customization.
- Replatform: This data migration strategy refers to moving an application while slightly optimizing the underlying codebase without a full redesign.
- Repurchase: This strategy replaces an existing system or application with new solutions, usually to improve scalability and performance. For instance, organizations replace existing on-premise or legacy software systems with new cloud-based solutions.
- Refactor or re-architect: Organizations use this strategy to redesign applications to be cloud-native and take advantage of modern cloud capabilities.
- Retire: This involves removing obsolete or unnecessary applications that no longer provide value to the business or the new cloud environment.
- Retain: This means keeping specific applications as they are if data migration isn’t cost-effective or is hindered by technical factors, regulatory requirements, or business constraints.
Data migration challenges
Data migration is a complex process that poses several risks and challenges for organizations, including the following:
Data quality issues
Poor-quality data can lead to significant problems during data migration. Common data quality issues include incomplete or missing data, inconsistent data format, inaccurate information, duplicates, or potential mapping errors. These issues arise due to several factors, including different data structures, migration tools, integration of data sources, lack of proper testing, and human error.
Hence, it’s essential to clean and validate data before moving it and ensure that information is accurate and complete through the pre-migration process to avoid critical data quality issues.
Stakeholder management
Keeping all stakeholders informed and managing their expectations throughout the process is critical for a successful data migration project. Lack of proper stakeholder management can lead to misaligned goals, unmet expectations, and project delays. Engaging stakeholders early in the data migration process is essential to set clear expectations and ensure that everyone’s needs and concerns are discussed.
System compatibility
Your organization’s existing data formats or database structure may not work with your new system, which can cause data loss or corrupted files. However, ensuring the new system can accommodate the migrated data without issues can be complex. Addressing system compatibility issues in data migration requires comprehensive data mapping, a transformation process, and custom integration solutions.
Security concerns
Data migration involves transferring data from one location to another, introducing potential risks and vulnerabilities. Without proper security measures during data migration, sensitive information can be compromised, leading to breaches, loss of customer trust, and failure to comply with regulatory standards.
To deal with security issues for data in transit, organizations should implement robust access controls, use data encryption, and ensure strict compliance with the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA).
Choosing a data migration tool
Selecting the right data migration tool ensures a smooth and efficient migration process. Here are key factors to consider when choosing a data migration tool for your organization:
- Compatibility: The data migration tool must be compatible with the source and target systems for smooth data transfer without extensive customization. It should also support different data formats, protocols, and platforms, including legacy systems, cloud solutions, and modern databases.
- Scalability: A data migration tool should be able to handle current data volume and scale operations to manage large datasets over time.
- Cost: Before choosing a data migration tool, assess the total cost of ownership, including licensing fees, implementation costs, and maintenance. Budget constraints may influence tool selection, so it’s important to consider if the tool offers flexible pricing models to align with your budget.
- Usability: A user-friendly data migration tool reduces the learning curve and allows both technical and non-technical users to implement migrations efficiently.
- Core Features: When choosing a data migration tool, look for core functionalities for data extraction, transformation, and loading (ETL or ELT) and error handling.
- Advanced Features: In addition to core features, you should consider advanced tools such as AI-powered workflow automation, real-time data synchronization, pre-built connectors, and more.
When choosing the right data migration tool, consider compatibility, scalability, pricing, ease of use, and feature set. A well-chosen solution will simplify complex migration processes, ensure smooth system integration, and support scalability. Aligning the tool’s capabilities with your organization’s objectives and resources can ensure a successful data migration process.
Frequently Asked Questions (FAQs)
While both processes involve transferring data, data migration refers to moving data between storage systems or formats for a particular purpose. In contrast, data transfer generally refers to any data movement without necessarily altering its format or structure. For example, data transfer can happen over a network, such as sending files between devices (WiFi or ethernet), or using physical media, like copying data to a USB drive or an external hard drive.
The best approach depends on the organization’s needs, including downtime tolerance, environment complexity, and existing resources. Generally, a “trickle migration” approach is considered the best option as it minimizes data migration in phases, enabling the old and new systems to run simultaneously. However, a “big bang” migration is suitable for smaller data sets, single-time transfers, and scenarios when downtime is not a concern.
Ultimately, whichever approach organizations deem suitable, the 6 Rs of data migration should always be considered.
ETL (Extract, Transform, Load) is a specific type of data integration process that often overlaps with data migration. However, ETL is a more specific process that involves transforming and loading data into a destination system rather than just moving it.
While some data migration processes change formats, especially when moving between different database types, storage formats, or applications that require restructuring, it’s not always the case. Meanwhile, ETL always emphasizes data transformation to make it suitable for analytics or business intelligence purposes.
Are you interested in upgrading your sales strategy by migrating from spreadsheets to a CRM solution? Read our CRM data migration guide to know more.
