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ABC of Big Data Analysis: The History and Importance

April 18, 2018

ABC of Big Data Analysis: The History and Importance


History of Big Data analysis

 

In the 1950s, when nobody knew the term “big data”, it was quite popular to use basic analytics, particularly using numbers in spreadsheet to examine manually. Big Data concept was around for many years, and only nowadays people understand that if it is possible to capture all data streams into businesses, then they can perform a big data analysis in order to get a significant value out of it. Currently big data analytics brings more benefits, like speed and efficiency. Few years back big data analysis allowed to gather information, run analytics and extract information that would be useful for future decisions. Now it is also possible to identify insights for immediate decisions.



The Importance of Big Data analysis

 

Big data analytics helps to handle data and eventually to use it for new opportunities. It helps to create a successful business with satisfied customers and higher profits. Tom Davenport is a professional researcher of more than 50 businesses, came to the conclusion that companies who were using Big data analysis succeed in cost reduction, could make much faster decisions and could create new products/services corresponding to customers’ needs.



The main areas using Big Data analysis

 

Businesses that need to make quick decisions to stay competitive are the ones that require big data analysis. There some main areas that relies on big data analysis in terms of technology development: 1. Travel and hospitality 2. Government 3. Health care and 4. Retail.




The key technologies of Big Data analysis

 

In Big Data analysis there are several types of technologies working together to help to get the most value from huge data and information. The key technologies that play a great role in Big data analysis are the following: 1. Data management 2. Data mining 3. Hadoop 4. In-memory analytics 5. Predictive analytics and 6. Text mining.

 

Data management. Data must be high quality in order to be sufficiently analyzed. Once data is reliable, then it is possible to arrange a data management program that shows the whole outcome on the same page.

 

Data mining. Data mining technology helps you examine large amounts of data to identify templates, it helps to answer complex business questions and perform further analysis.

 

Hadoop. This software can store large amounts of data and run applications on clusters of hardware. Its computing model processes big data much faster. This software is completely free of charge and it uses hardware to store large quantities of data.

 

In-memory analytics. In-memory analytics allows you obtain immediate insights from data and act on it quickly, when analysis is done from the system memory instead of hard disk drive. It helps to remove data preparation and analytical processing delays to test and create new models.

 

Predictive analytics. This technology uses data, statistical algorithms and machine-learning techniques to identify the probability of future outcomes based on historical data. It helps to identify what could happen in future and companies can feel more confident having the best possible business decision.

 

Text mining. With this technology, it is possible to analyze text data from the web, comment fields, books and other text-based sources. Text mining uses machine learning or natural language processing technology to run through documents, emails, blogs, Twitter feeds, surveys, competitive intelligence and helps to analyze large amounts of information in order to discover new topics.




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