Migrating Data involves in and out to a new platform which requires migration of all the functional data from the legacy system. Most businesses Migration Agents Perth have shifted from traditional data centers to cloud with the promptly changing business needs and the advent of cloud technology. Like all other interactions data migration too involves few hurdles to overcome.So accurate planning is essential to overcome this hurdles.
- Common Problems Of Data Migration
- Data Migration Process
- Best Practices for Efficient Data Migration
Most Common Problems of Data Migration
Sometimes it’s very uncertain and you don’t even realize when you run into data migration problems. However, being aware of the common hurdles that could potentially derail your project will increase the likelihood of achieving a smooth transition of data to your new system. The most common data migration problems that are often not addressed and the factors that far too often lead to failure are as follows:
1. Poor Knowledge of Source Data
Not being aware of the problems that exist in your data is the reason for the poor knowledge of source data. It can be all too easy to get complacent and assume that your data can easily be configured into the parameters of a new system however the reality could mean critical failures when it comes to user acceptance. So to ensure success, you need a good understanding of the source data.
2. Underestimating Data Analysis
Constraints in a computer system can be an issue as information can be hidden in obscure places because often there are not specific fields to hold all elements of the data and sometimes the users may also be not aware of the purpose of the available fields. This result is outdated data getting transferred during migration which sometimes is discovered after the project is completed. Due to this, there is not enough time available to identify and correct this data. Performing a thorough data analysis at the earliest stage usually while designing and planning can uncover these hidden errors.
3. Lack of Integrated Processes
Data migration is Disparate technologies used by a set of people. Using spreadsheets to document data specifications which are not easy to translate while performing data transformations and which are prone to human errors is a classic example. This usually leads to failure in transferring data and problems in the development, testing and implementation stages. Organisations must look to utilise a platform that successfully links the critical inputs and outputs from each of the stages to help reduce error and save time and money.
4. Inability to Validate a Specification
Critical misses of the early stage of data can have repercussions later in the chain of activities may well have an understanding of your source data, but is not necessary that it would result in a strong specification for migrating and modifying data into a target system. Validating your data transformation specifications early on with actual data, rather than just documented aspirations can increase the confidence in executing the rest of the steps.5. Failure to Validate the Implementation
You can hit a brick wall because of lack of test cases even where your knowledge of source data is evident. To avoid the risk of developing problems you need to explore various scenarios before it is too late. Using full volume data from the real world helps to cover a wider range of possibilities while testing your migration.