Status and future of Big Data in an industry. Part 2-The difference between Business Intelligence and Big Data. Methods for analyzing Big Data.

The difference between Business Intelligence and Big Data

Craig Bati, executive director of marketing and technology director for Fujitsu Australia, said that business analysis is a descriptive process for analyzing the results achieved by the business in a certain period of time, while the processing speed of big data makes it possible to make predictive analysis capable of offering advice to businesses future. Big data technologies also allow to analyze more types of data in comparison with business intelligence tools, which makes it possible to focus not only on structured repositories.

Mac Slocum of O'Reilly Radar believes that although big data and business intelligence have the same goal (seeking answers to a question), they differ from each other in three aspects:

• Big data is designed to handle more significant amounts of information than business intelligence.

• Big data is designed to handle more quickly received and changing information, which means deep research and interactivity.

• Big data is intended for processing unstructured data (emails, videos, audio files, web pages, and social media messages)

According to Oracle's white paper, “Oracle Information Architecture: An Architect's Guide to Big Data”, when dealing with big data, we approach information in a different way than doing business analysis. When working with big data, the result is obtained during the process of data cleaning by sequential modeling: firstly, a hypothesis is put forward; secondly, a statistical, visual or semantic model is constructed; next, on the basis of this, the correctness of the hypothesis is checked and the following is advanced. This process requires the researcher to either interpret visual values ​​or compose interactive queries based on knowledge, or develop adaptive algorithms of “machine learning” that can obtain the desired result. And the lifetime of such an algorithm can be quite short.

Methods for analyzing Big Data

There are many different methods of analyzing data sets, which are based on tools borrowed from statistics and informatics (machine learning). The following methods reflect the most popular approaches. Some of the techniques do not necessarily apply only to big data and can be used for smaller arrays (A/B testing, regression analysis).

·         A/B testing.

·         Association rule learning.

·         Classification.

·         Cluster analysis.

·         Crowdsourcing.

·         Data fusion and data integration.      

·         Data mining.

·         Ensemble learning.

·         Genetic algorithms.

·         Machine learning.

·         Natural language processing (NLP).

·         Network analysis.

·         Optimization.

·         Pattern recognition.

·         Predictive modeling.

·         Regression.

·         Sentiment analysis.

·         Statistics.

·         Simulation.

·         Time series analysis.

·         Unsupervised learning (close to Cluster Analysis).

For more articles about Business Analytics you can check this blog: 

http://haneenalansari.blogspot.com/2017/04/how-can-you-get-great-job-in-business.html