Data Conversion

Data conversion can be defined as a process of converting data from one structural form to another to suit the requirements of the system to which it is migrated. Data conversion becomes a necessity when a firm decides to move to a different software application to maintain its business critical data.

A decision is required to be taken in this phase as to whether there is a necessity of data cleansing. Data quality of legacy systems is generally poor, rendering it unsuitable for migration considering the more stringent data requirements of the target model. For instance, consider a particular field in the target system for an entity, which is mandatory, but the same field in the legacy system can have absolutely no data at all. This renders all other related data in the legacy system to be rejected. A trade-off is thus required to be made in such cases as to whether go for data cleansing or default the field with some predefined value so that it is not rejected. In the event of defaulting it to a predefined value, we are compromising the data quality, which is usually undesirable. The decision primarily depends on the time scale of the data migration project. It should be appreciated that data cleansing is an iterative process and should be generous on time scales for any improvement in data quality as it is done by the business. Consequently, a data cleansing initiative should be addressed in the very beginning of any data conversion project and is critical in the success of any data conversion project.

Testing the converted data is an important activity of data conversion. The converted data, a result of the conversion process, based on mapping specifications should be tested for the following:

• Is the entire set of mapping rules defined in mapping specifications document incorporated?
• Does the data converted follow the data structure model of the target system?
• Are all the fields mapped from the legacy system converted in the appropriate way in the target system?
• Are all the constraints of the target system fulfilled or is there is any risk of data load rejections?
• Did the correct number of records get created in the target system?
• Was the cleansing data incorporated correctly in the conversion process?
• Does the legacy and target system records reconcile?

IDN offers data conversion planning, management and supplemental resources to assist your organization.

Our proven Data Conversion methodology ensures that:

• Identify potential business risk and issues
• Identify resources needed to carry out conversion task
• Determine how the new database will be constructed.
• Determine specific data requirements. Define conversion needs for historical data by determining the required age of the data and the required level of detail.
• Identify tasks to be performed before and after data conversion process. These tasks will be described and documented in detail before conversion runs.
• Determine other tasks related to updating, maintaining, or loading the data.
• Determine which tasks will be automated, which ones will have to manual, and determine the interaction between the automated and non-automated tasks.
• Refine the timetable dependencies for the data conversion.
• Confirm that all data elements required in the new application have been identified.
• Confirm that the sources for all data elements have been identified.
• Document the location of all data conversion processing, including associated physical resources.
• Test plan and test scenario shall be prepared
• Both CLIENT and IDN have to agree on conversion acceptance criteria for sign-off
• Identify contingency plan

Data Conversion Timetable will be setup which shall include a reasonable allowance for re-running failed jobs.

• These include descriptions of:The estimating basis and assumptions used in constructing the conversion timings.
• The monitoring mechanisms used to ensure that particularly long, critical path conversion runs are proceeding according to expectations.