We live in an era when Information Technology has taken over many redundant and mundane tasks that humans used to perform during their day-to-day work manually. The computers have digitized the manual registers and ledger system of record keeping. I am sure that in all this, you would have noticed one thing. IT has made gathering, processing, and analyzing data easier, faster, and cheaper than ever before. It has also helped us increase the bulk of data that we can store and process. So let’s start by discussing data.
In terms of IT, Data is a set of information that can be Unstructured, Structured, or Semi-structured. Although data and information are similar, there is a generic difference between both of them. Data is generally in raw form. This means that data is collected randomly from various sources. To convert this raw data into information, we have to organize the data and make it understandable.
Data generally is collected through observations. Nowadays, with the incidence of SmartPhones, PCs, etc., the data is easily accessible. For example, Whenever we download an application from the Play Store, we give it permissions to access the data generated on your phone which is relevant to that application.
In the beginning, Data Science was a term used alternatively for Statistics. This in itself gives us a flavor of the term Data Science. As we discussed earlier, data is relevant only if it is understandable, readable, informative, and can be analyzed to draw actionable decisions. Data Science uses methods, algorithms, and processes that are scientific in nature to derive information out of data. Data Science deals in 3 major avenues. These are Data Mining, Machine Learning, and Big Data. Many methods are applied in Data Science that include statistics, mathematics, information science, computer science, domain knowledge, etc which you can learn easily through data science courses or through a business analytics course.
One such example is Google. Google powers our android phones, and we know that all our activities are tracked by it. It records the data of our internet surfings, our online purchases, locations we have visited, etc. So, Google is a modern-day information powerhouse. Google uses this data to customize the notifications and advertisements that are shown to us on the go. Based on our searches and visits, google suggests options to explore. This has made android the most user-friendly and the most used OS in smartphones. There is no doubt that efficiently applying Data Science techniques and utilizing them to add value to the user’s experience has been the key to the rise of Google as the world leader in IT. Though some people have concerns about privacy, still, Data science is growing, and it is going to drive our future.
With the growing ecosystem of computers and automation by the IT industry, the affordability and reach of new technologies have spread to the masses. As per the latest available data, India is only second to China in the number of internet users. There are almost 756 million active internet users in India alone. Isn’t this figure indicative of the magnitude of data that the IT deals with on a daily basis? The data in consideration is really big. Here comes the concept of Big Data. Big Data deals with analyzing and systematically extracting information from large sets of data that are complex enough to be understandable. The use of Big Data starts when data is so big that our traditional data processing software cannot handle it. The Big Data process includes capturing, storing, analyzing, searching, querying the data from the source.
Big Data generally comprises 3 V’s. These are:
Data is collected from various sources, including smart devices, industrial equipment,
social media, search engines, etc. Earlier, the volume of data was less, but storage
wasn’t as efficient as now. Hence the volume of data is increasing.
With the introduction of the Internet of Things, not only has the bulk of data has increased, but the requirements of handling it in real-time have also evolved. A simple example can be the number of growing online transactions. The bulk of transactions is increasing. The feeding, matching, and processing of data is all done in real-time, and the transaction is reflected immediately across all stakeholder accounts. All these steps are done on every step of a transaction.
Data online is available in all types and formats, which cannot be handled by traditional software. These include structured numeric as well as unstructured text data. In addition to these 3 V’s, there are two additional V’s that are being considered nowadays. They are Variability and Veracity.
There are many areas where Big Data is currently being used. These include,
The basic difference between Data Science and Big Data is that Data Science is a broader term, and Big Data is a part of Data Science as a whole. Let’s look at the major differences between these two:
|S. No.||Data Science||Big Data|
|1.||Data Science is the complete practice of recording, organizing, analyzing, and utilizing data to make actionable decisions.||Big data is a technique used as a part of Data Science to collect and maintain the bulk of information.|
|2.||Uses traditional Scientific methods to analyze the data and create useful information.||Works on the principle of 3 V’s, i.e., higher volume, higher velocity, and a greater variety of data beyond the handling capacity of traditional software.
|3.||Tools like SAS, R & python are used for Data Science.||Advanced tools such as Hadoop, Spark, Flink, etc., are used.|
|4.||Its main use is to analyze the data scientifically.||This technique is used by businesses to increase customer satisfaction.|
The scientific process of converting random data into useful information using different methods and techniques like statistics, mathematics, algorithms, etc., is called Data Science. Then, utilizing these huge chunks of gathered data to enhance user experience and make information readily available (also in real-time) is referred to as Big Data.