Data Science, Big Data, and Data Analytics – Are they related?

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Key Highlights 

• The future is turning digital and the wave of digitalization is depending on data.
• There are some basic differences between three Data terms – Data Science, Big Data, and Data Analytics.
• Data Science helps the search engines to deliver the best results for search queries, whereas the Data Analytics optimizes the buying experience through social media data analysis.

We are now living in a data-driven world – the amount of digital data is growing faster than ever before. You will be surprised to know that the amount of digital data is doubling every two years, and gradually changing the way we live. By 2021 beginning, about 1.7 megabytes of new information will be created every second, which makes it important for us to know the basics of the data field at least. After all, our future is turning digital and the wave of digitalization is depending on data. This blog is, however, discussing the basic differences between three basic terms of Data – Data Science, Big Data, and Data Analytics.

Data Science

Data Science is related to cleansing, preparation, and analysis of structured and unstructured data. Extracts insights and information from structured and unstructured data, this science actually combines statistics, programming, mathematics, problem-solving, capturing data in ingenious ways. The applications of Data Science involve a lot of things. Earn Data Science training from Multisoft Systems if you are looking forward to start a career as Analytics Engineer, Analytics Managers, Data Scientists, Hadoop Professionals, or Information Architects.

1. As we all know, digital ads getting higher CTR than traditional advertisements. Data Analytics algorithms are the reason behind it as it helps the digital marketing world to display banners and digital billboards.

2. It helps the search engines to deliver the best results for search queries in a fraction of seconds.

3. It helps the companies in managing Recommender Systems that help them to promote products and suggestions as per exact user demand. These systems make it easy to find relevant products from billions of products available and add a lot to user-experience.

Big Data

Data that is not aggregated and almost impossible to store in a single computer is called Big Data. Introduced to submerge a business on a day-to-day basis and help in taking better decisions, Big Data is a buzzword which is used to describe huge volumes of data, including structured and unstructured data. This humongous volume of data is not subjected to be processed with existing traditional applications.

1. Retail banks, Credit card companies, private wealth management advisories, insurance firms, venture funds, and institutional investment banks use big data to manage their financial services with accuracy.

2. Big Data is used in a wide range of fields, including fraud analytics, customer analytics, compliance analytics, operational analytics, and big data in communications.

3. It is ideal for the companies in getting new subscribers, retaining old customers, and expanding sales within current subscriber bases.

4. The organizations use Big Data that require the ability to analyze data sources to deal with weblogs, social media, customer transaction data, store-branded credit card data, and loyalty program data.

In adherence to the definition given by Gartner, “Big data is high-volume, and high-velocity or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.” 

Data Analytics

In order to extract information and derive insights, organizations apply algorithmic or mechanical process and go through several data sets. Hence, they look for meaningful correlations between each other for better decision making. This process is called data analytics. The focus of Data Analytics lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows.

1. It optimizes the buying experience through mobile or weblog and social media data analysis. Personalized travel recommendations that are based on social media data can also be delivered by data analytics.

2. Data Analytics helps in collecting data for optimizing and spending across games. So, it is very popular among the online and offline game-based companies.

3. It is helpful in energy management. Many utility companies use this technology for smart-grid management, energy optimization, energy distribution, and building automation.

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About the Author: Rajib Kar is a technical content developer at Multisoft Systems. This experienced IT professional loves to write about the recent developments and future trends of corporate training.