How Big is Big Data? The term “Big Data” is used to identify or signify huge amount of data. It is often defined as structured, semi-structured, or unstructured data that could be used to extract information. Big Data can be used in Machine Learning projects and advanced analytics application.
The concept of Big Data is quite new, but people have stored data since the inventions of computer. Large Datasets came into existence in the 1960s and 70s when the first data centres were setup and development of Relational Databases started. In 2005, huge data was being generated because people started to be more active on Facebook, YouTube and other online services. Hadoop was developed the same year. NoSQL started gaining popularity.
The concept of Big Data can often be explained using the 4 V’s: volume, variety, velocity, and veracity. The primary characteristics of Big Data is the huge volume of data. It is not specific to any quantity of data but is often used to describe data in terabytes, petabytes, or exabytes.
Data might be in its raw form or pre-processed. The data might be processed using various data mining tools or data preparation software before analysis. IoT and Machine Learning have produced way more data than it was expected. Cloud computing has also expanded the big data possibilities. Cloud provides elastic scalability where developers simple spin up ad hoc clusters to test subsets of Data. Big Data contributes to a wide variety of datatypes, structured in SQL databases, unstructured and semi-structured. Unstructured data is kept in Hadoop cluster or NoSQL system.
Characteristics of Big Data
There are some specific characteristics that classify data as “Big Data”. They can be summarised as:
- Volume: It is the main characteristics of classifying some data as “Big Data”. It describes data as “big” in sheer volume.
- Variety: This was perhaps to signify the most interesting development in technology. Traditional datatypes are called structured datatypes. They include data from relational databases. Unstructured data are just random data kept together such as twitter feeds, audio files, web pages, web logs, MRI images etc. There are no specific rules to handle these types of data.
- Veracity: This term is used to define the trustworthiness of Data.
- Velocity: There is always a frequency of incoming data that needs to be processed. That is known as the velocity or speed in which data is coming into the system.
- Value: The data that is being collected or stored should generate some sort of value for the company that is doing all the analysis.
- Complexity: Since all the data comes from a variety of different sources, it is often difficult to create a link between those data to match, cleanse, and transform data across system. This makes it more difficult to connect and correlate relationships, hierarchies and data linkage in the data.
Importance of Big Data
Big data helps us analyse cost reductions, time reductions and product developments. The relationships and patterns received from the huge amount of data help us to make smarter decisions. Big Data can also be used to accomplish various business-related tasks such as:
- Big Data can help to determine root cause of failures, issues and defects in real time.
- Using Big Data, we might generate coupons on point of sale based on customer’s buying habits to increase product demand.
- Big Data might help us in recalculating entire risk portfolios in minutes.
- This also helps to detect fraudulent behaviour before any organization is affected.
Applications of Big Data
Big Data can be used in a variety of fields such as Banking, Education, Governmental, HealthCare, Manufacturing, and Retail. The analysis of the Big Data can help us take risky decisions at the peak hour. It can be used to predict some probable results from the analysis of the data that could be useful for business. Big data Analysis can be used to make or change some protocols or procedures.
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