Data Migration Plan for Non-volatile Subject-Oriented Records into a Read-Only OLAP Environment

Data migration refers to the process of preparing, extracting, transforming and selecting data and permanently moving it from one storage system of a computer to another. Therefore, data migration is a multifaceted process necessitating a robust technique to accomplish. However, the planning process of migrating information will support and minimize the risks that naturally transpire during migration course (Chenoweth et al. 94). Business intelligence (BI) implies to a technology-driven operation for presenting as well as analyzing vital information to empower executives, managers, and entrepreneurs to make informed decisions regarding the business (Shankar 29). Consequently, the article will focus on creating a data migration plan for non-volatile subject-oriented records into a read-only OLAP environment. In the process, migrate historical data from an operational source into a business intelligence data mart to become a historical record for making informed decision making.

Using BI has effectively transformed the business environment in the modern society. Some of the benefits of BI include;

i. Getting insight to make timely and accurate decisions regarding the business

ii. Identification of new opportunities for income.

iii. Tracking KPIs successfully by getting notifications and alerts every time there is a data change.  

One of the factors that has made data migration successful is data warehouse. A data warehouse (DW) refers to a design that allows business intelligence operations (Devlin 13; Hinchcliff et al. 107). A DW exists to enable the users to understand and increase the performance of their organization. Data warehouse is designed for analysis and query rather than for the processes of transactions and it usually has historical data derived from data generated from transactions (Pipe 254). The planning for non-volatile subject-oriented records into a read-only OLAP environment will entail six steps to help to support the migration process of information.

i. Configuration

Before the migration process, it is vital to carry out structural configuration to make sure that all the facets of the relocation of data are correctly functioning. The process entails ensuring the expert defines all the communication routines, availability of storage and versioning of artefacts, and making sure the hardware is accessible and accessible.

ii. Migration design

The phase covers the cleansing and extraction of historical data from operational sources as well as verifying and transforming the data. The process should pursue a stable and effective methodology to ensure a smooth flow of data during and after migration is achievable.

iii. Testing design

The testing design stage will define an overall plan of testing for all phases of migration. Additionally, it encompasses how the testing process of each stage will take place at a unit level followed by the entire procedure of relocation.

iv. Migration development

The data expert should use an agile methodology to develop the migration stages. The procedure has been successful where there are multiple stakeholders involved. An agile procedure ensures that mitigation of risks at an early phase.

v. Testing development

The analyst undertakes testing within a test structure provided or established for the objective of the migration. The framework permits regular running of unit tests to immediately highlight any concerns that may arise.

iv. Execution

The data specialist defines a several dry runs to test the go-live strategy. Therefore allowing the confirmation of the go-live plan and refining as necessary. The early dry runs may or may not use the sampled historical data. When the initial dry-run ends, a test migration occurs.   

In conclusion data migration is one of the vital steps in the management of information for informed decision making. Through the use of appropriate methodology, specialist can mitigate risks associated with data loss during the migration process at an early phase.   

Works Cited

Chenoweth, Tim, et al. “A Method for Developing Dimensional Data Marts.” Communications of the ACM, vol. 46, no. 12, Dec. 2003, pp. 93-98. EBSCOhost, doi:10.1145/953460.953465.

Devlin, Barry. “Thirty Years of Data Warehousing.” Business Intelligence Journal, vol. 23, no. 1, Mar. 2018, pp. 12-24.

Hinchcliff, Monique, et al. “Text Data Extraction for a Prospective, Research-Focused Data Mart: Implementation and Validation.” BMC Medical Informatics & Decision Making, vol. 12, no. 1, Jan. 2012, pp. 106-112. EBSCOhost, doi:10.1186/1472-6947-12-106.

Pipe, Pamela. “The Data Mart: A New Approach to Data Warehousing.” International Review of Law, Computers & Technology, vol. 11, no. 2, Oct. 1997, pp. 251-262.

Shankar, Ravi. “Enabling Self-Service BI with a Logical Data Warehouse.” Business Intelligence Journal, vol. 22, no. 3, Sept. 2017, pp. 29-35.