Today, many businesses have constantly believed that change is inevitable, especially when it comes to the technology sector. Introduction of new systems such as CRM, ERP, HR, mergers, acquisitions, partnerships, diversification, warehousing projects, inventory management, and staying updated with regulatory issues all require a strong existence of data migration. With this proven approach to the activities associated with data migration, the probability of development turns out higher.
According to Gartner, 83% of data migration projects either fail or exceed their budget and schedules. Now the question is: What is the cause of such higher project failures? The reasons could vary such as unclear assumptions, lack of data governance, vague methodologies, misconceptions, wrong tools for supporting the data migration process, uncovered risks, uncertain risks, level of complexities, etc. But here is the complete guidance of a checklist to plan before moving forward for a successful migration that minimizes all such reasons significantly.
How to integrate a successful data migration? Here is a clear checklist plan:
1. Stakeholder Participation at every phase – For quicker accessibility:
Data migration is not limited to only IT departments, its significance lies in every department and every function. An ongoing business requires the involvement of every data and its analysis for great strategic formulation that exceeds expectations. The higher the amount of involvement from partners, customers, employees, and other stakeholders the greater the chances of addressing issues facing an organization. Deep involvement increases the chances of high-quality data and provides clear visibility of the requirements.
2. Data Analysis & its Interpretation – A continuous process to execute a purpose:
Data migration is not a one-time process that involves data transfer from one type to another or from one folder to another. Instead, it is a continuous process where end-to-end processes have been set up in a desired workflow structure to meet the purpose. In response to a relatable methodology, the best approach is discovered for eradicating risk at any point. And this process includes:
- The current stage of data verification
- Root cause analysis focused on business goals.
- A clear understanding of the difference between how the process was documented and how it is executed.
- An in-depth discussion on what lies ahead of current data analytics for making it more goal-oriented.
- Profound estimations about actual time and efforts required in each stage throughout the process.
3. Data Evaluation & its value attributes – Escalate the business effectiveness for Future Demands:
Create a well-defined data administrative system concerning people and its process of migration defining the technologies that have been used, steps taken, feedback implemented, and more. The quality of the data when it will go-live with no duplicates, list of its attributes, issues resolved, dealing with a current scenario along with future needs, targeted needs have been fulfilled or not, and more such are the major value attributes that should be clearly defined for proper management.
4. Data Integration and its Acceptance – Be ready for the clear visibility of business processes:
Whenever the data migration steps take place make sure the process is smooth with fewer risk factors and clear final outputs. The more definite the outputs will be, the clearer the visibility will be, and introducing new strategies will be pre-defined. Replicate the process as much time as required because a perfect data migration process will reflect constant business effectiveness in specific terms.
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Enable your organization and your collaborative teams for quicker and easier identification of underlying issues and resolve them through data migration strategies such as discovering new opportunities, improving operational excellence, smoothie complaints with industrial, environmental, social, and government reforms, and more.
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