Unclogging the Mind: Data Science vs. Big Data vs. Data Analytics

Posted by techblog

We are living in a data-driven world, where most of our acts are either an action or a reaction to the information we receive. According to reports, we are creating about 2.5 quintillion bytes of data every day; most of which is unstructured.

With the humongous amount of data being created every day, it is crucial for businesses to learn how to leverage it for their benefits. This has opened up several avenues, from Big Data to Data Science to Data Analytics. Although people tend to use them interchangeably, there’s a significant difference between these concepts and the jobs they perform.

It is, therefore, imperative to understand the basics of these fields of study. In this blog post, we will understand the difference between Data Science, Big Data, and Data Analytics, based on what they all are and their applications.

Data Science

Data science is a combination of techniques used to extract insights and information from data. It deals with both unstructured and structured data. It comprises everything related to data; from data cleansing to preparation to analysis. Data science intelligently combines statistics, mathematics, and programming to not only capture data but also give a diverse perspective of things to solve problems. It, thus, comes in handy with:

Internet Searches: Search engines leverage data science algorithms to deliver the best results, in a fraction of seconds, for search queries.

Digital Advertisements: Data science algorithms rule the digital marketing spectrum. Whether its display banners or digital billboards, data science forms the core of digital advertisements and is also the reason for the higher CTR of digital ads than traditional advertisements.

Recommender Systems: Many companies use a recommender system to promote their products. The system uses data science to analyze the user’s search history to understand their demands and relevance of information and accordingly display suggestions. Recommender systems, thus not only simplify the process of finding relevant products among billions available but also augment the user experience.

Big Data

Big data refers to the collection of humongous volumes of data from distinct resources that cannot be processed effectively using traditional applications. It helps processes –

  • Unstructured data such as emails, blogs, tweets, mobile data, web pages, and so on.
  • Structured data such as transaction data, RDBMS, OLTP, etc.
  • Semi-Structured Data- text files, system log files, XML files, etc.

Big Data is highly useful in several industries, such as:

Financial Service Provider: From retail banks and credit card providers to insurance firms and private wealth management advisories leverage big data to extend their services to customers. Big data helps all these financial service providers to gather the massive volumes of multi-structured data stored in multiple disparate systems for the customer, compliance, operational, and fraud analytics.

Communications: Whether it is gaining new subscribers or retaining customers, telecommunication service providers leave no stone unturned to expand their subscriber base. This is only possible by combining and analyzing the volumes of the customer- and machine-generated data being created every day.

Retail: Retail is a challenging domain and the key to remaining competitive and challenging is to understand the customer. Whether brick and mortar or an online e-tailer, big data gives all these retailers the capability to analyze all the disparate data sources, including social media, order history, customer transaction data, and payment history to make informed, customer-centric decisions.

Data Analytics

Data Analytics is known as the science of inspecting raw data to draw inferences about that information. It involves applying algorithmic or mechanical processes over the raw data to derive insights. For instance, examining numerous data sets to draw a conclusion and ensure the attributes are correlated.

Data analytics is widely used across industries to help organizations make informed business decisions.

Healthcare: Hospitals across the globe are striving to ensure efficient treatment while improving the quality of care. The instruments and machines used in treatment generated a huge volume of data. Data analytics helps in analyzing this data to optimize the patient flow and the treatment given to them. It is estimated that a 1% efficiency gain could save more than $63 billion globally.

Travel: Data analytics in the travel industry can help optimize the customer experience throughout the communication channels. Travel sights gain hordes of customer information through signup forms, inquiries, and social channels. Analyzing this data can help them sell customized packages and offers by correlating it with the customer’s desires and preferences. They can even deliver personalized travel recommendations based on their social network data.

Energy Management: Data analytics also helps in energy management, from energy distribution and optimization to smart-grid management and building automation. It integrates millions of data points in the network performance, enabling the engineers to use analytics to control and monitor network devices, dispatch crews, and manage service outages, ensuring optimum use of energy.

Closing Lines

Data is the backbone for almost every activity performed nowadays. Whether it is education, technology, healthcare, retail or any other industry, all have moved from being centered on their products to being data-focused. Even the smallest piece of information today holds value as it can help them derive some useful information.

Even though all three – data science, big data, and data analytics – have data at their core; it is evil to refer to them interchangeably. We hope this blog post gave you an idea of how all the three areas of specialization are different from each other.

That is it from us.

Until next time!

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