The Rise of Data: Data Science, Big Data and Data Analytics for Seamless Business Operations

by June 11, 2020

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.

Healthcare- Conversational virtual assistants, health bots, predictive modeling for diagnosis, drug discovery, genomic sequences to understand genetic structures, and medical image analysis.

E-Commerce- Customer base identification, customer targeting, inventory forecasting, sales trend identification, optimizing pricing strategies, fraud detection, and prevention.

Data Scientists need the following skills to master their profession-

•  Statistical & Analytical Skills.

•  Understanding of Data Mining, Co-relation, Machine Learning, and Deep Learning principles.

•  In-depth knowledge of SQL/Database/ SAS/R coding.

According to a leading recruitment platform, the average annual salary estimates for Data Scientists range from US$100,000- US$125,000.

 

Big Data

A term used to describe large volumes of both structured and unstructured data inundated in daily Business processes. What is done with this data is important, effective mining can lead to better decisions and strategic business moves. Big data is characterized by 3V’s –

Volume- These include the multiple data sources from where data is collected, like smart (IoT) devices, industrial equipment, social media, etc. This voluminous data is stored in dynamic storage platforms like data lakes and Hadoop.

Velocity- Prompt handling of data generated at unprecedented speeds. For instance, RFID tags, sensors, and smart meters generating torrents of data in near-real-time.

Variety- Different formats of data like structured data, numeric data in traditional databases, and unstructured text documents. Examples include financial transactions and stock ticker data.

Variability and Veracity are other two dimensions often considered into account when it comes to Big Data.

 

Why is Big data important?

Big data revolves around what businesses do with the data they have at their disposal. This data is harnessed for cost and time reductions, new product planning, smart decision making, and optimized offerings. The use cases in Big data accomplish business-related tasks like-

•  Analyzing the root causes of failures and defects in near-real time

•  Customized offerings based on a customer’s buying habits

•  Recalculating portfolio risks

•  Detecting frauds

 

Big Data experts need the following skills to master their profession-

•  Data visualization and Creativity skills.

•  Understanding of technologies like Hadoop, Spark, Hive, MATLAB, etc.

•  Working with unstructured data

•  SQL/Database coding.

According to a leading recruitment platform, the average annual salary estimates for Big Data professionals range from US$95,000- US$110,000.

 

Data Analytics

Data analytics (DA) is the fundamental level of Data Science. Data Analytics is the process of analyzing large data through an algorithmic or mechanical process to understand the correlations between each other and draw conclusions about the information they contain. Data Analytics begins with descriptive analytics, describing historical data trends and extends to advanced analytics to extract data, make predictions, and discover trends. Advanced analytics encapsulates neural networks, natural language processing, sentiment analysis to answer “what if?” questions.

Data Analytics’ is a robust field, prescriptive, diagnostic, predictive, and prescriptive analytics are the four DA pillars deployed according to business objectives to provide intelligent insights quintessential for a business to make effective and efficient decisions.

•  Data Analysts need the following skills to master their profession-

•  Programming, scripting & statistical skills.

•  Hadoop, Adobe & Google Analytics.

•  Reporting-with data visualization software.

•  SQL/Database coding.

•  Advanced excel expertise.

According to a leading recruitment platform, the average annual salary estimates for Data Analyst professionals range from US$50,000- US$75,000.