Data migration is the process of transferring data from one system or platform to another, and if not done correctly, it can result in data loss, downtime, and even financial losses. However, with the right strategy and tools, data migration can be a smooth and successful process that contributes to data quality.
Are you planning a data migration for your business?
Whether you're moving to a new system, consolidating multiple systems, or upgrading to a new platform, Master Data Management has got you covered.
Read on for our essential steps for seamless migration of data.
Data migrations are complex, risky and expensive — and often disrupted by unexpected challenges. To avoid surprises, deliver on time and ensure quality, data and analytics leaders can improve planning, proactively address data quality and rely on structured, technology-enabled execution.
Make Data Migration Boring: 10 Steps to Ensure On-Time, High-Quality Delivery
Ted Friedman, Gartner Research
Pave the Way for Successful Data Migrations
When embarking on a data migration journey, it's crucial to lay down a solid foundation to ensure success.
Just like moving house, meticulous planning and preparation can make all the difference. Pave the way for successful data migrations by understanding the intricacies involved and addressing potential challenges proactively. From assessing the current state of data to establishing clear objectives, every step counts towards a smooth transition.
Why Neglecting Data Quality Can Jeopardize New Systems
Neglecting data quality in the context of implementing new systems is akin to building a house on shaky ground.
No matter how advanced the technology or sophisticated the architecture, compromised data quality can jeopardize the integrity and effectiveness of the entire system. It's imperative to recognize the significance of pristine data as the backbone of any operation. Learn why neglecting data quality can jeopardize new systems and how prioritizing data integrity can lead to sustainable growth and efficiency.
Why Overlooking Data Integration Leads to Data Migration Failures
Data integration plays a pivotal role in the success of data migrations.
Failure to address this crucial aspect often leads to significant setbacks and failures in migration projects. Just as a chain is only as strong as its weakest link, data migration efforts are only as effective as the integration strategy employed. Discover why overlooking data integration leads to data migration failures and how integrating disparate data sources can streamline processes, enhance accuracy, and mitigate risks.
Automated data profiling emerges as a powerful ally in the realm of data migration, offering insights and efficiencies beyond traditional methods.
By leveraging automated tools and techniques, organizations can gain a comprehensive understanding of their data landscape, identifying patterns, anomalies, and potential issues with precision. Explore how to improve data migration with automated data profiling and harness the transformative power of data-driven insights to optimize migration processes and ensure successful outcomes.
Why migrate data?
Common events that may trigger a requirement to migrate data include:
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The shift to cloud
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Digital transformation
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Application modernisation and re-platforming
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Data centre consolidation or migration
Challenges of Data Migrations
Data migrations can present several challenges for businesses, including:
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Data loss: If not done correctly, data migration can result in data loss, which can be costly and damaging to businesses.
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Downtime: Data migration can result in system downtime, which can disrupt business operations and result in lost revenue.
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Compatibility issues: Migrating data to a new platform can result in compatibility issues with existing systems and applications.
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Security risks: Data migration can expose businesses to security risks if not done correctly.
Steps for a Successful Data Migration
To ensure a successful migration of data, businesses should follow these essential steps:
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Plan: Develop a comprehensive migration plan that includes timelines, budget, and resources needed for the migration.
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Analyze: Analyze the existing data and systems to identify any potential issues or compatibility problems.
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Test: Conduct testing to identify any issues that may arise during the migration process.
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Migrate: Perform the migration, taking care to ensure data integrity and minimize downtime.
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Verify: Verify that the data has been migrated correctly and that all systems are functioning as expected.
The first step in planning for data migration is to define the project scope.
This involves identifying the data that needs to be migrated, the systems involved, and the expected outcomes of the project.
Mapping and Data Assessment
Data mapping and analysis: Once the project scope has been defined, the next step is to map the data and analyse it. This involves identifying the source data, the target data, and any data transformations or mappings that need to be performed.
A common mistake is to assume that existing data is well understood and also that it will be compatible with the data structures of the intended target environment. Before migrating data we recommend verifying any assumptions to ensure that you have a clear understanding of what you will be migrating, potential risks that must be managed before they become issues, and what functional changes may need to be planned for to ensure that data will fit into the target system.
Assessments should look at three key areas:
Data Structure: Confirm actual table and column structures (of source and target systems) to confirm data mappings and identify functional disconnects. Tools such as Data360 and Safyr can add tremendous value at this stage
Data Flows: Understand the business logic that may be built into the data layer of your source system, to ensure that this can be planned for in the target. MANTA's unified data lineage platform is a good choice to get this understanding.
Data Content: Profiling the data can provide clarity on issues such as missing, duplicated or misfielded data, identify disconnects between source and target data types, and give an indication of where manual data remediation tasks may be required.
Select your Data Migration Approach
After the data has been mapped and analysed, the next step is to select the appropriate migration strategy and methods.
Choosing the Right Data Migration Strategy
There are several data migration strategies to choose from, depending on the size and complexity of the data being migrated, as well as the goals and objectives of the migration.
Big Bang Migration
In a big bang migration, all data is migrated at once, with the new system going live immediately after the migration. This approach can be quicker and simpler, but also carries a higher risk of errors and disruption to business operations.
Phased Migration
A phased migration involves migrating data in stages, with different parts of the system going live incrementally. This approach can help minimize disruption and allow for more thorough testing, but can also be more time-consuming.
Parallel Migration
In a parallel migration, both the old and new systems are run in parallel for a period of time, with data being migrated gradually over time. This approach can provide a safety net in case of errors, but can also be more complex and costly.
Hybrid Migration
A hybrid migration involves a combination of different migration strategies, depending on the specific needs and requirements of the migration.
In addition to deciding on a strategy, one must select one or more migration methods
Migration Methods
This may include manual data entry, automated data migration tools, or a combination of both.
Develop a Migration plan
Once the migration methods have been selected, the next step is to develop a detailed migration plan. This plan should include timelines, milestones, and contingency plans in case of unexpected issues
Execute the Data Migration
Once the planning and preparation stages are complete, the data migration process can begin. This typically involves three main stages: data extraction, data transformation, and data loading.
Data Extraction
Data extraction involves extracting data from the old system or application and preparing it for migration to the new system. This may involve cleaning and formatting the data, as well as verifying its accuracy and completeness.
Data Transformation
Data transformation involves converting the data into a format that is compatible with the new system or application. This may involve mapping data fields, reformatting data, and performing data validations and transformations.
The data transformation stage will typically include data cleansing and validation. For example, a migration may create an opportunity to purge redundant or stale data, remove or consolidate duplicate records, and enrich existing data. At the very least a migration will require data to be transformed and standardised to fit the data structures and meet the needs of the target system.
Data Loading
Data loading involves loading the transformed data into the new system or application. This may involve performing data integrity checks and verifying that the data has been loaded correctly.
Our recommended approach
In our experience, a parallel migration strategy leveraging automation is the most effective
Using the inputs from the assessment we:
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Define and develop a staging environment for the source data
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Design and define a landing environment that will be compatible with the target.
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Develop data integration and data quality processes to move the source data to the landing environment, addressing data risks and issues along the way. Data360 allows us to build agile data pipelines to quickly connect, transform and move data, and repeat the process to keep source data synchronised with our landing area.
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Generate exception reports where manual data remediation is required in the source e.g. for missing data.
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Review and repeat: Following the initial development phase, we repeat the process focusing our attention on the new landing area. This means that we are identifying data risks that were not addressed during earlier phases. We can repeat this process as often as required until approval is given to migrate the clean data into the target.
This approach is designed to manage data risks that may otherwise derail your entire project, and to ensure that the data migrated is, if possible, of higher quality than the original source. It also minimises downtime by allowing the new system to run in parallel with the old, while keeping the data synchronised from old to new.
Our parallel approach to data migration
Testing and validation
Once the data has been migrated, the next step is to test and validate the migrated data. This involves comparing the source data with the migrated data to ensure that it has been migrated accurately and completely.
Post-migration activities
After the migration process is complete, there are several post-migration activities that need to be performed. These may include data reconciliation, user training, and system maintenance.
Reuse your Investment
The last thing you need is to invest millions in new systems only to find that the data quality issues that plagued you previously have resurfaced within months of your go-live date.
Our approach allows new systems to reuse business processes and validations built to migrate data, ensuring you get the business returns you expect from your system.
Migrating to Cloud
Cloud migrations are becoming commonplace, yet, without appropriate planning, even a theoretically simply lift-and-shift migration can end in disaster. In most cases, phased approaches are best as outlined in our post on expediting cloud migrations
Read our post on 5 ways we can help your successful migration to the Snowflake Data Cloud and watch the MANTA recording
MIgrating to Databricks?
Read Precisely's post on Cloud Migration and Modernization with Databricks, Microsoft, and Precisely
Don’t let data be a stumbling block in your SAP S/4HANA upgrade! Read our post for tips.
What is data migration?
Data migration is the process of transferring data from one system or format to another. It may involve moving data from an older system or environment to a newer one, or from one database to another.
Why do I need to perform a data migration?
There are several reasons why you might need to perform data migration, such as upgrading to a new system or platform, consolidating data from multiple sources, modernising a database structure, or moving to the Cloud.
What are the risks associated with data migrations?
Data migrations can be risky if not executed properly. Common risks include data loss or corruption, extended downtime, and compatibility issues with existing systems.
How can I minimize the risks associated with data migrations?
To minimize risks associated with data migrations, it's important to plan thoroughly, test extensively, and involve key stakeholders in the process. You should also consider working with a reputable data migration expert or consultant, like Master Data Management.
What are some common tools used for data migrations?
There are many tools available for data migrations, ranging from commercial software to open-source solutions. Popular tools include data lineage tools, data discovery and modelling tools, data profiling and quality tools, and tools that support data integration such as ETL, CDC or data preparation tools.
How long does a typical data migration take?
The length of a data migration depends on several factors, including the amount of data being transferred, the complexity of the data, and the systems involved. Some data migrations can be completed in a matter of hours, while others may take several weeks or months.
For complex migrations, such as an application migration, we recommend running your data migration phase in parallel with your application design and implementation. In other words, data migration for complex requirements can take as long as your implementation, and planning should begin at an early stage of your project.
What are some best practices for data migrations?
Best practices for data migrations include defining clear objectives, creating a detailed project plan, testing thoroughly, communicating regularly with stakeholders, and documenting the entire process. You should also establish a contingency plan in case of any issues or delays. Following these best practices helps to ensure a successful data migration and unlock the value of your enterprise information.
Are you truggling with data quality issues? Take the data migration opportunity to improve your name and address data quality.
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