Big Data is the most important topic across the industry sector for Business purposes. The topmost company focus on big data.
What Is Big Data Analytics
Big Data is the most “hyped” term in the market today. this term often used for related concepts such as Business Intelligence (BI) and Data Mining (DM). Data Mining Is important for keeping data in a safe place like “Data warehouse” by data analytics and data research. that is the data management process.
That is depended on three main characteristics like Volume, Velocity, and Variety. Big Data is not just only volume. which means That involves having a lot of data. That’s coming from a variety of source and also It may have complex data.
That data can be used in various industrial applications by collecting useful data ( That data is helpful for a particular application ).
That is often used relevant data from a variety of datasets to use in any application. It may help the application to make different and add more features.
Volume: This is a large amount of data from the dataset by data analytics and data research.
Velocity: That is the transaction data is coming at great speed on the basis of these data streams.
Variety: data comes from different data sources such as a random format like log data from various applications, structured data such as data table, XML data, unstructured data such as text, image, video stream, audio, and more.
” Big” data is the high variety, high velocity, and high volume related data. that demand cost-effective, innovative forms of information process. that enable for decision making, enhance insight, and process automation. “
What Data Are We Discussing About?
The Organization has different transactional data. apart from that, Now day Organizations are capturing additional data from operational at adding fast speed. for some examples here.
Web Data – The web data such as page views, searches, reading reviews, purchasing, etc. This data can help an organization for advertisement, user tracking, etc.
Text Data – The text data is one of the biggest widely applicable data. It is used in email, news, Facebook feeds, documents, etc. that help the organization to extract an input value.
Time And Location Data – The Mobile Phone, Wi-Fi connection and GPS can make time and location for Organization. most organizations used data for selling a product to track user location and time. The time and location are one of the most privacy-sensitive types for big data.
Smart Grid And Sensor Data – Nowadays, sensor data are used for many resources like Camera, Electric Car, any small or big machine, etc. they are collected high frequency or current.
The sensor data provides powerful information on the performance of the engine or machine. That may help the organization in different ways like networking, adding more features, security, etc.
Social Network Data – The social data is much helpful to the organization to track or analyze links in social media platforms such as Facebook, Instagram, Linkedin, Twitter, etc.
How Big Data Different From Traditional Data Source?
There are some important ways their data can be different from the traditional data source.
First, It is a new source of data. for example, most of us have online shopping. we can execute the different transactions for buying products. as the same organization may capture web transactions of us.
However, browser capturing behavior of new data such as customer execute a transaction create fundamentally new data.
Second, structured and unstructured data are coming. Most traditional data sources are structured. structured data can be like receipts from the store, salary slip, accounting information on the spreadsheet. The structured data means we have proof of those things.
Unstructured data means we have no control over its such as Text data, Video data, Audio Data. this data fall into this category.
The people working for data analytics called “Data Scientist”. The Thomas H. Davenport and D.J Patil toss the sentence of Data Scientist in a Harvard University in 2012.
The working title of Data Scientist is sometimes criticized. Because that lacks specification might be perceived as glorified for the data analyst.
Regardless, the position is having profit with large industries that are interested in deriving from data analysis, and a large amount of structured, unstructured data may produce large enterprises.
The primary distinction found in practice between data scientists and other analytics professional is that data scientist is likely to come from computer science platform to use Hadoop and programming language like Python.
That compared to traditional analytics who are likely to come from statistics, math or operations research background are likely to use relational analytics server information to code In SQL (Structured Query Language).
The Data Scientist is not so much different from a traditional analytics professional where the analytical mindset remains unaltered.
Big Data Analytical Techniques
Most of the wide analytics technique falls into these following categories.
Statistical methods, forecasting, regression analysis
Database querying – it useful for saving user data on the web by SQL (Structured Query Language)
Data warehouse – that important for keeping large data in one place or platform safely.
Machine Learning and Data mining – machine learning is a process of developing any resource. data mining is a method of building new data.
What Is Big Data Concept
That is tangible data for organizations. That enables enhanced relevance, decision making, and process automation.
The three characteristics of big data are there Volume, Velocity, and Variety. The “Big” in big data is not just volume while big data certainly involves having a lot of data.
The “big” data is a variety of different data sources. from where the data comes. that data can be combined with each other. that can be used in various industrial applications. that’s why big data is most useful for organizations or industries.
The “big” data has new data types management due to its high volume, high velocity, and high variety. this new data type management bears the trademark of high scalability, massive parallel, and cost-effective.