5 Differences Between Big Data And Business Intelligence

Although Big Data and Business Intelligence are two technologies used to analyze data to help companies in the decision-making process, there are differences between both of them. They differ in the way they work as much as in the type of data they analyze.

Traditional BI methodology is based on the principle of grouping all business data into a central server. Typically, this data is analyzed in offline mode, after storing the information in an environment called Data Warehouse. The data is structured in a conventional relational database with an additional set of indexes and forms of access to the tables (multidimensional cubes).

Difference Between Big Data and Business Intelligence

A Big Data solution differs in many aspects to BI to use. These are the main differences between Big Data and Business Intelligence:

  1. In a Big Data environment, information is stored on a distributed file system, rather than on a central server. It is a much safer and more flexible space.
  2. Big Data solutions carry the processing functions to the data, rather than the data to the functions. As the analysis is centered around the information, it´s easier to handle larger amounts of information in a more agile way.
  3. Big Data can analyze data in different formats, both structured and unstructured. The volume of unstructured data (those not stored in a traditional database) is growing at levels much higher than the structured data. Nevertheless, its analysis carries different challenges. Big Data solutions solve them by allowing a global analysis of various sources of information.
  4. Data processed by Big Data solutions can be historical or come from real-time sources. Thus, companies can make decisions that affect their business in an agile and efficient way.
  5. Big Data technology uses parallel mass processing (MPP) concepts, which improves the speed of analysis. With MPP many instructions are executed simultaneously, and since the various jobs are divided into several parallel execution parts, at the end the overall results are reunited and presented. This allows you to analyze large volumes of information quickly.

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