Big Data Analysis

Big data analytics is the often complex process of examining big data to uncover information — such as hidden patterns, correlations, market trends and customer preferences — that can help organizations make informed business decisions.



Why is big data analytics important?


How does big data analytics work?


Here is an overview of the four steps of the data preparation process:

  1. Data professionals collect data from a variety of different sources. Often, it is a mix of semi-structured and unstructured data. While each organization will use different data streams, some common sources include:
  • internet clickstream data;
  • web server logs;
  • cloud applications;
  • mobile applications;
  • social media content;
  • text from customer emails and survey responses;
  • mobile phone records; and
  1. Data is processed. After data is collected and stored in a data warehouse or data lake, data professionals must organize, configure and partition the data properly for analytical queries. Thorough data processing makes for higher performance from analytical queries.
  2. Data is cleansed for quality. Data professionals scrub the data using scripting tools or enterprise software. They look for any errors or inconsistencies, such as duplications or formatting mistakes, and organize and tidy up the data.