The big deal about big data migration

This blog is intended to shed some light on BI, data warehousing and some key elements to include in your QA decision-making when undergoing a big data migration.

  • Carla Hartman
  • 14 May 2019

As the world becomes increasingly digital and organizations adopt new software and processes, it is likely that migrating large amounts of data will be necessary. Additionally, the 3 Vs of Big Data continue to increase: volume, velocity and variety.

This blog is intended to shed some light on BI, data warehousing and some key elements to include in your QA decision-making when undergoing a big data migration.

First let’s discuss the differences between business intelligence (BI) and data warehousing.

Data warehousing is the process of extracting and consolidating data from multiple sources into a central database. A data warehouse is the central database/repository where the data is consolidated.

BI is the meaningful and actionable data that is extracted from all the raw data an organization has collected. Either tracked in real-time on dashboards or presented historically, BI provides decision-makers with a degree of foresight and confidence when making critical business choices.

The QA process is essential to include in data warehousing and BI initiatives. It is important to trust the operations you have in place, but it is just as important to verify.

By considering the testing of your BI and data warehouse systems early in their development, you can be confident that migrated data maintains its integrity. Catching data inconsistencies during a data migration early ensures that the business can adhere to timelines and get back to making critical decisions sooner.

When choosing a system and process, some key areas to focus on for a successful operation include:

  • Ability to compare sources and targets
  • Ability to choose sources and files types
  • Ability to sort
  • Easy UI that enables business analysts and QA professionals to compare the data
  • Responsive dashboards
  • Ability to input data from diverse sources
  • Ability to create automated regression tests
  • Robust and easy to use report generation

A successful QA process that ensures data integrity following a data migration can benefit your organization by efficiently notifying developers of architectural and functional issues, ensuring forecasts and reports are trustworthy, and providing insight to architects to further enhance their data warehousing process.