ALL ABOUT THE BASICS OF BIG DATA: HISTORY, TYPES AND APPLICATIONS

3Mar - by aiuniverse - 0 - In Big Data

Source – https://www.analyticsinsight.net/

As big data comes with a handful of benefits, let us get to its bottom and learn all the basics of the technology

Today, organizations of all sizes hold vast amounts of data from all aspects of their operations. Companies use the big data accumulated in their systems to improve operations, provide better customer service, create personalized marketing campaigns based on specific customer preferences and, ultimately, increase profitability. Businesses that utilize big data at their best have the potential to outperform others. As big data comes with a handful of great benefits, let us get to its bottom and learn all the basics of the technology.

What is Big Data?

Big data represents the large, diverse sets of information that grows at an exponential rate. Unfortunately, big data is so large that none of the traditional data management tools can store it or process it efficiently. More than the volume of data, the way organizations utilize data matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves. Humans produce 2 quintillions of data every day. The New York Stock Exchange alone creates about one terabyte of new trade data per day. Social media platforms are also big contributors to the surmounting data. Besides, airlines also generate many petabytes of data. In the early 2000s, Doug Laney, an industry analyst listed three V’s that defines the characteristics of big data.

Volume: The amount of data inflow is exponentially high in business organizations. Data from various sources like business transactions, IoT devices, social media, industrial equipment, videos, etc, contribute to the cause. Since it can’t be stored in a physical space, the storage issue was a big deal earlier. However, thanks to emerging technologies like data lakes and Hadoop, the burden is far eased.

Velocity: Besides the exponential amount of data inflow, the data speed also matters. The datasets are put at a tough spot to be handled in a timely manner. RFID tags, sensors and smart meters are driving the need to deal with these torrents of data in real-time.

Variety: There is no assurance that the data we gather are bound to be the same or fall under a similar category. Data comes in all formats like numeric data, text documents, images, videos, emails, audios, financial transaction, etc.

History of big data 

The first trace of big data is seen way back in 1663 when John Graunt dealt with overwhelming amounts of information while he studied the bubonic plague, which was haunting Europe at the time. Graunt was the first-ever person to use statistical data analysis. Later, in the early 1800s, the field of statistics expanded to include collecting and analyzing data.

The world first saw the problem with the overwhelming of data in 1880. The US Census Bureau announced that they estimate it would take eight years to handle and process the data collected during the census program that year. In 1881, a man from the Bureau named Herman Hollerith invented Hollerith Tabulating Machine that reduced the calculation work.

Throughout the 20th century, data evolved at an unexpected speed. Big data became the core of evolution. Machines for storing information magnetically and scanning patterns in messages, and computers were also created at that time. In 1965, the US government built the first data centre, with the intention of storing millions of fingerprint sets and tax returns.

Types of big data

Data comes in different forms. The fact be said, here are the three main categories it falls into.

Structured data

Data that can be stored, accessed and processed in the form of fixed-format is termed as ‘structured data.’ Since this data comes in a similar format, businesses get the maximum out of it by performing analysis. Various advanced technologies are also invented to extract data-driven decisions from structured data. However, the world is going towards an extent where the creation of structured data is ballooning too much as it has already reached the zettabytes mark.

Unstructured data

Any data that comes in an unknown form or structure falls under unstructured data. Processing unstructured data and analyzing them to get data-driven answers is a challenging task as they are from different categories and outing them together will only make things worse. A heterogeneous data source containing a combination of simple text files, images, videos, etc. is an example of unstructured data.

Semi-structured data

Semi-structured data has both structured and unstructured data in it. We can see semi-structured data as structured in form, but it is actually not defined with table definition in relational DBMS. Web application data is an example of semi-structured data. It has unstructured data like log files, transaction history files, etc. OLTP systems are built to work with structured data wherein data is stored in relations.

Applications of big data

Business organisations are leveraging data to reach their maximum potential. Ever since technology took over big data analysis, business decisions are mostly based on predictive outcomes. Besides, big data is also contributing to personalized customer experiences at high-ends. Some of the important business applications of big data are listed below.

• Product development- Companies avail big data to anticipate customer demands. They build predictive models to see customer preference and provide relevant materials.

• Log analytics- Commercial and open-source log analytics provides the ability to collect, process and analyze massive log data without having to dump the data into relational databases and retrieving it through SQL queries.

• Security compliance- Big data helps you identify patterns in data that indicate fraud and aggregate large volumes of information to make regulatory reporting much faster.

• Recommendation engines- Big data, with its scalability and power to processes massive amounts of both unstructured and structured data enables companies to recommend the best option for customers based on their history.

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