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Big data terminology: A layperson’s guide

Published: July 13, 2022

Picture this: A business user wants to review the last six months of sales results for a specific region. She enters a query into the sales system, which then accesses the requested information from a data warehouse.

In order to get an answer…

  1. The requested information must be in the warehouse.
  2. The user must ask a question that the sales system recognizes.

Suppose this same business user wants a different piece of information that is not currently stored in the warehouse? Or asks a question that the system does not recognize?

For example, what if the user wants to drill down and find the sales results for a particular store in the last six months? If that information is not in the data warehouse, the user would not receive an answer.

Before Big Data, making the changes to get this store-level sales information was not a simple undertaking. Implementing a change request like this could impact 50+ enterprise systems, requiring an intensive time and resource investment.

To remain competitive, organizations need fluid, nimble ways to access and analyze data. Companies cannot afford to wait months or more for answers to pressing questions.