Introduction to Big Data Analytics
A field to analyze and to extract information about the big data involved in the business or the data world so that proper conclusions can be made is called big data Analytics. These conclusions can be used to predict the future or to forecast the business. Also, this helps in creating a trend about the past. Skilled professionals in statistics and engineering with domain knowledge are needed in the analysis of big data as the data is huge, and analysis needs proper determination and skillset. This data is more complex that it cannot be dealt with with traditional methods of analysis.
We can define Big Data as three Vs
Volume: The amount of data that is being generated every second. Every day organizations like social media, e-commerce businesses, airlines collect a huge amount of data.
Velocity: The rate at which the data is generated. Social Media is being used by everybody, and there will be lots of data generated every second because people do a lot of things over social media; they post comments, like photos, share videos, etc.
Variety: Data could be of various forms structured data like numeric data, unstructured data like text, images, videos, financial transactions, etc., or semi-structured data like JSON or XML.
What are we doing with this Big Data?
We can use this big data to process and draw some meaningful insights out of it. There are various frameworks available to process big data. The below list provides the popular framework that is widely being used by big data developers and analysts.
Apache Hadoop: we can write map-reduce the program to process the data.
Spark: we can write a spark program to process the data; using spark, we can process a live stream of data as well.
Apache Flink: this framework is also used to process a stream of data.
And many more like Storm, Samza.
Big Data Analytics
Big Data analytics is the process of collecting, organizing, and analyzing a large amount of data to uncover hidden patterns, correlations, and other meaningful insights. It helps an organization to understand the information contained in their data and use it to provide new opportunities to improve their business which in turn leads to more efficient operations, higher profits, and happier customers.
To analyze such a large volume of data, Big Data analytics applications enables big data analysts, data scientists, predictive modelers, statisticians, and other analytical performers to analyze the growing volume of structured and unstructured data. It is performed using specialized software tools and applications. Using these tools, various data operations can be performed like data mining, text mining, predictive analysis, forecasting, etc.; all these processes are performed separately and are a part of high-performance analytics. Using Big Data analytic tools and software enables an organization to process a large amount of data and provide meaningful insights that provide better business decisions in the future.
Key Technologies behind Big Data Analytics
Analytics comprises various technologies that help you get the most valued information from the data.
The open-source framework is widely used to store a large amount of data and run various applications on a cluster of commodity hardware. It has become a key technology to be used in big data because of the constant increase in the variety and volume of data, and its distributed computing model provides faster access to data.
Once the data is stored in the data management system, you can use data mining techniques to discover the patterns which are used for further analysis and answer complex business questions. With data mining, all the repetitive and noisy data can be removed and point out only the relevant information that is used to accelerate the pace of making informed decisions.
With text mining, we can analyze the text data from the web like the comments, likes from social media, and other text-based sources like the email; we can identify if the mail is spam. Text Mining uses technologies like machine learning or natural language processing to analyze a large amount of data and discover the various patterns.
Predictive analytics uses data, statistical algorithms, and machine learning techniques to identify future outcomes based on historical data. It’s all about providing the best future outcomes so that organizations can feel confident in their current business decisions.
Benefits of Big Data Analytics
Big Data Analytics has been popular among various organizations. Organizations like the e-commerce industry, social media, healthcare, Banking, Entertainment industries, etc., are widely using analytics to understand various patterns, collecting and utilizing customer insights, fraud detection, monitor financial market activities, etc.
Let’s take an example of the e-commerce industry:
e-commerce industry like Amazon, Flipkart, Myntra, and many other online shopping sites make use of big data.
They collect customer data in several ways like
- Collect information about the items searched by the customer.
- Information regarding their preferences.
- Information about the popularity of the products and many other data.
Using these kinds of data, organizations derive some patterns and provide the best customer service, like
- displaying the popular products that are being sold.
- show the products that are related to the products that a customer bought.
- Provide secure money transitions and identify if there are any fraudulent transactions being made.
- Forecast the demand for the products and many more.
Big Data is a game-changer. Many organizations are using more analytics to drive strategic actions and offer a better customer experience. A slight change in the efficiency or smallest savings can lead to a huge profit, which is why most organizations are moving towards big data.
This has been a guide to Big data Analytics. Here we have discussed basic concepts like what is Big data Analytics is, its benefits, the key technology behind Big data Analytics, etc. You may also look at the following article to learn more –