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Data is a term which is being talked about by everyone and why not since it is a crucial factor for the smallest ventures to the largest enterprises in the world.

Everyone relies on data to gain a better understanding of a particular topic or a task. In the earlier days, data was recorded in books & registries which was a cumbersome process.

But with advancements in technology & the advent of computing systems, smartphones & connected devices, data is being generated on a huge scale. This data is very precious for many small & large companies. Why?

Applying various data analysis techniques to such huge troves of data can give organizations insight into various important details. This crucial data can then be leveraged to make changes in certain areas of business activities to enhance growth.

For example:

1. Enterprises can analyze data to maximize organizational efficiency.
2. Ecommerce businesses can know customer behavior patterns.
3. Logistics companies can streamline their operations.

So, let’s quickly learn about some important data analysis technologies that have a huge potential in 2020 & beyond.

What is Data Science, Big Data & Data Analytics?

Data Science – Umbrella of Techniques

Data Science is a combination of:

1. Mathematical & Statistical techniques
2. Programming 
3. Problem-Solving methods
4. Ingenious methods to capture data
5. Activities like Data Cleansing, Preparing & Aligning the data

So Data Science is an amazing blend of various techniques & methods that are used to deal with structured as well as unstructured data. The main goal of this process is to gain useful insights from data.

Big Data – Analysis of Huge Volumes of Data

The name itself suggests that it deals with humongous troves of data which a business/enterprise receives daily.

An important aspect of Big Data is that it primarily deals with raw data i.e. data that hasn’t been aggregated yet. 

This huge amount of data cannot be stored on a single computer; hence big data techniques are used to analyze such data – be it structured or unstructured.

This enables companies in extracting enhanced insights which help in making better decisions.

Data Analytics – Raw Data Examining

Data Analytics comprises:

1. Applying Mechanical or Algorithmic Processes
2. To derive insights into data.

Data Science is also called the science of examining raw data. This helps in concluding information about the raw data.

It is highly preferred in various industries as it helps companies to:

• Make better decisions
• Verify/Disprove Theories or Models which are currently being used 

One interesting part about Data Analytics is that it is based on inference i.e. making conclusions based on the data available to the researcher.

Use-cases of Data Analytics, Data Science & Big Data

1. Data Science

Recommender Systems – Personalized Recommendations

You might’ve used some mobile eCommerce apps like Amazon, Flipkart among others. 

One thing that might’ve crossed your mind was the easy visibility of a collection of products that you might like. This is made possible by innovative solutions that are provided by data science consulting services & data science-based techniques.

Digital Advertisements – Relevant Ads

This is similar to personalized recommendations but the only difference is instead of products you receive ads that are relevant to your interests & preferences. This is usually done by businesses & enterprises who want to attract customers with beguiling ads.

2. Big Data

Financial Services – Innovative Analytics

Financial institutions like retail banks, insurance firms, credit card companies, investment banks & others use Big data techniques for their financial services.

These institutions generate huge volumes of data which is multi-structured. But, with big data analytics solutions, all of this data can be readily analyzed.

Communications – To Drive Business Growth

Some important priorities for telecommunication companies are:

• Getting new subscribers
• Retaining new & existing customers
• Expanding the customer base.

With Big Data analytics, understanding huge volumes of customer-generated data can help telecom companies to achieve the aforementioned goals with ease.

3. Data Analytics

Healthcare Industry – High Efficiency

Healthcare industry is currently experiencing heavy workloads due to the ongoing pandemic along with regular caseload.

With data analytics, authorities in healthcare institutions can seamlessly understand & enhance patient flow, treatment & equipment are being used in the hospitals.

Travel Industry – Recommendations & Upselling

With Data Analytics, the travel industry can easily understand customer preferences for a particular place, accommodation habits, food preferences & much more.

Based on the obtained data, travel companies can curate personalized packages, upsell products/services & recommend the best places to travel.

These are the major differences between Data Science, Big Data & Data Analytics.ARSR is a cutting-edge technology solutions provider that offers data science consulting services, big data analytics solutions & Data Analytics services to enterprises & startups for enhancing productivity & operational efficiencies to drive business growth.