Data migration can be a common chore for enterprise IT teams these days, but that doesn’t mean it’s an easy task. According an estimate offered by IDC, more than 80 percent of data migration projects run late or goes over budget, which can put enterprise application deployment at risk and negatively impact business operations and the business objective.
So, what may seem like a simple task of moving data from legacy systems to latest innovation, actually involves much more strategic planning and thought to successfully deliver trusted, accurate info that your business can run and rely on.
With this in mind, below are six tips to make a data migration project successful-
Planning- A proper planning is important before putting the first step towards migration. Decision makers have to think on what is achievable in terms of what the data sources will support and what is reasonable. After refining the scope, a timeline, resource plan, and budget can be furnished in place. Technically speaking, migration is complex task and key aim is to migrate the smallest amount of data required to run the target system on an effective note. Seldom will all source data be required, so scoping needs to be approached firmly to filter out any surplus data.
Due Diligence- High level analysis of the source and the target system should be conducted in close consultation with the business users who will be directly affected by the data migration process and who have in-depth knowledge of the business issues-past, present and future.
Access the budget and timeline- After gaining knowledge about the resources, planning is needed on how the project will be resourced and to secure agreement from the business. Estimates should include all time and material costs of auditing and profiling data developing mapping specifications, writing migration code, building data transformation and cleansing rules, and loading and testing data. A typical project is managed over six months to two years. Realistic deadlines must be set in line with external dependencies and include appropriate levels of contingency—-bit complex isn’t it?
Start understanding the data- Data identification, profiling and auditing helps organizations gain a clear visibility on the data which is on-premises, and helps in investigating anomalies to any required depth. This process helps in identifying unknown data issues and helps in creating a single repository for all analysis, regardless of source system.
Designing and building- The results of the data audit are applied to the agreed scope to develop a series of rules for transferring all designated source data and ensuring that it is accurately manipulated to fit the target. Before any actual code is written, the mapping specifications and supporting documentation must be clearly understood and signed off by the business
Executing- Then comes the main task of executing the whole process of extracting data from the source system, transforming it, cleansing it, and loading it on the target system, using migration tools.
Testing- Unit, system, volume, online application, and batch application tests need to be carried out before the conversion can be signed off by the business. A key objective should be to get to the full-volume upload and online application test stage as early as possible for each unit of work—in many cases, several units of work may need to be completed to achieve this before an online test can be done. This strategy helps avoid storing up issues until too late in the development cycle, when they are more expensive to rectify. Another major risk is that data is changing in the source systems while the migration is in development. Having created a profile and audit of the sources, it is possible to re-run the audit at any point to assess any changes and take appropriate action. This should be done before all major project milestones, to facilitate continue/stop and revise decisions.
Follow up and maintenance- Once the migration process is completed, data audit can be implemented at any time, on any set of data and at any point in the data migration cycle to assess whether the project is on success track and still within its scope.
Finally, whatever is the reason for data migration, its ultimate aim should be to improve corporate performance and deliver competitive advantage.
Want to upgrade your data center environment with new hardware and need professional help in doing data migration?
Approach DNF Corporation for your enterprise data migration needs. DNF understands that each migration is unique and requires a comprehensive migration plan. The fundamentals to consider include available migration strategies, migration issues, migration methodology and the critical data involved. DNF RADIM methodology uses the best industry practices for risk mitigation and data integrity-before during and after the migration.
Just give them a call to initiate the process.