Fri. Dec 4th, 2020
BIG-DATA-AND-DATA-ANALYTICS
BIG-DATA-AND-DATA-ANALYTICS
BIG DATA AND DATA ANALYTICS

BIG DATA AND DATA ANALYTICS- Data, a four-letter word is the forerunner of a digital economy. Structured and unstructured data through emails, financial transaction numbers, audio files, web pages, business documents, and social media messages are mined to harness intelligent insights. Data forms the core of AI-powered Machine learning models. You will be surprised to know that humans generate more than 2.5 quintillion bytes of data every day, broken to 1.7 megabytes in just a second. Research estimates that over 6 billion smartphone users are looking at some form of data, and over 50 billion smart devices are interconnected to collect, analyze, and share data in 2020 alone.

Data Science, Big Data, and Data Analytics let enterprises navigate the complex world of data and technology to target its customers and offer bespoke services and products leveraging endless possibilities. Data opens endless possibilities, for businesses in big ways and processing of this data usually begins with data aggregation accumulated from multiple sources. Data Science, Big Data, and Data Analytics answer the unsolved problems related to handling and managing data that we have in our dispersal.

Data Science

Data Science is a banquet of data cleaning, analysis, and preparation, an umbrella term for several scientific methodologies to discover hidden patterns from raw data. Data science also involves solving business problems in multidimensional processes deploying prototypes, algorithms, predictive models, and custom analysis. Data scientists who work distinctively with data apply predictive analysis, machine learning, and sentiment analysis to extract valuable insights. Data Science finds its application in BFSI, Healthcare, Manufacturing, and Transportation to name a few.

Banking- Fraud Detection, Customer data management, Risk modeling, Customer segmentation, and real-time predictive analytics.

Finance- Customer lifetime value prediction, stock market assessment, algorithmic trading, customer relationship management, and fraud detection.

Manufacturing- Production optimization, cost reduction, equipment monitoring, production hours optimization, preventive maintenance, and quality improvement.

Transport– Making safer driving environments, vehicle performance optimization, self-driving cars, logistical route mapping, and surge pricing models.

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Article Credit: Analytics Insight

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