Ever wondered why Data Migration Projects are notorious for budget and schedule overruns?
“Andy’s major client required the availability of a large number of inventory at key points in the production process. Initially everything seemed to be pretty much in place until inaccurate data from the legacy system resulted in inventory accounting issues and an unexpected discovery that a number of data transformation rules were left untouched due to lack of business ownership.”
On the surface, data migration appears to be easier. It’s perceived simplicity often leads many project managers to view it as a part of a larger underlying project and thus scheduling data migration near the end of the project rather than the beginning. Therefore, leaving the migration analysis to last and not being fully able to understand its implications, often leads to fatal failures.
Approx. 70% to 90% of data migration projects fail to meet expectations due to budget or time over-runs
While the statistic represents a bleak picture of wasted efforts, industry analysts estimate that at any given time, two-thirds of Fortune 1000 companies are engaged in some form of data migration.
Whether you’re pursuing one-off ‘Big Bang Migration’ or ‘Trickle Migration’ by way of replacing your CMS, or transitioning to a new website or even overhauling an existing information architecture, the following best practices listed below enable you to give data migration the attention they deserve and deliver a competitive advantage.
Let's explore the 9 Best Practices For Successful Data Migration Projects
The complex cumbersome process of data migration requires great effort, significant planning and resources to ensure a smooth and seamless transition. Successful data migration demands that you make sure you’re validating and testing throughout. So how do you make it happen? Following these 9 best practices across the data migration phases (design, extraction, cleansing, load, verification) will enable you to complete your data migration project on-time, within scope and on-budget.
1. Analyze Data Complexity and Its Scope:
How complex is the data? Data migration project will always have unforeseen issues. The difference between success and failure lies in how effectively your chosen approach can respond to those unknown issues.
Examine and evaluate the various forms of organizational data, examine what data you are migrating, where it resides, where and how are they stored and the form it will need to take when it arrives at the destination. These together determine the direction and consecutive migration and integration process.
2. Set up Data Standards
Once you’ve evaluated the complexity of your data, it is advisable to have a comprehensive set of data standards in place. Establishing data standards allows you to identify problem areas early on, thus making sure that you’re not faced with multiple unexpected issues at the final stage. Moreover, since data is an entity that is continuously changing with time, establishing a set of rules and standards is helpful to develop, define and recommend definitions across the organization to support data consolidation, thus ensuring more successful use of data in the future.
3. Clearly Define Future and Current Business Rules
Defining business rules that will apply to your migration ensure compliance and compatibility with present data migration project and future policy requirement and regulations. Not only must they be compatible with different validation and business rules, they enable you to move data in a systematic way by enforcing data migration policies.
4. Establish Data Governance Roles
Defining who manages the information and who is responsible for supporting data quality, usage and access, thus ensures that entire data migration process is supported with appropriate accountability. The end-goal of data governance is to improve the quality and usability of the data. You may establish data governance roles by setting up a governing body or council where project manager identifies all possible roles, assign responsibilities to all project staff members.
5. Assess Data Quality
Data migration is much more than moving data from point A to point B. Therefore, before any data is transferred from one system to another you must do a thorough data quality check to ensure a high-level quality once the new data base takes shape. Conduct a data quality assessment to identify the quality of legacy data and build firewalls to filter, cleanse and protect good data while eliminating de-duplication.
6. Gather Risk Migration Requirements
Once data complexity and quality have been established, rules and data standards defined, information governance structure put in place, gathering migration requirements is fairly straightforward. It is critical to analyze how and where the organization’s data will be used and who will use it and how it might change the future.
7. Assess and Identify the Proper Tool
Tools are only good as the infrastructure they support and it is critical to assess and identify whether you have the proper tool for your new data environment. How flexible are the data migration tools? Is there a fit with the skills of the people working on the project? Identifying and assessing a proper tool to carry out the migration process. An ideal tool must be flexible, highly scalable, require minimal technical expertise, and be intuitive so that business and technical staff can work collaboratively.
8. Have a Risk Management System in Place
Data loss is unavoidable during data migration. According to a survey by Kroll Ontrack, nearly 600 IT administrators, nearly half (57%) of the respondents had a backup solution in place when data was lost during migration process, 75% were not able to restore all of their lost data, while 23% unable to recover data at all.
Before starting migration process, it is advisable to build a test environment and test the migration process to provide an opportunity to prepare for corrective measures, should any failures or loss occur. Don’t rush into deploying a new OS immediately because it brings with it missing or not yet optimized drivers as also some bugs. Finally use ad-hoc deployment tools that can backup data and settings, while also managing the entire migration process in a centralized console.
9. Migration Test and Validate
This step is essential to validate the integrity of migrated data. Whether it is accurate, in the expected format and well-executed. Post data migration, ensure that you document each stage of the data migration process to maintain a clear audit trail to meet the regulatory compliance.
Following these best practices will reduce the pain of managing a complex data migration and give your project the best chance of success. Do you have any additional best practices to make data migration projects successful? We’d be glad to know them.