Until a decade ago Data Scientists weren't in trend as today because of relatively smaller data which could be gathered, analysed and interpreted easily either manually or by simple tools and algorithms. But modern-day scenario requires companies, grappling with information which comes in several varieties and volumes that has never been witnessed before. The fourth industrial revolution includes Data science as a significant component for industries and as such has become the fuel for successful industries today.
So, who is a data scientist?
Data scientists in a layman's term are a new breed of super heroes who have the technical skills to collect, analyse and interpret large amount of unstructured data using complicated analytical tools and sophisticated algorithms. It is the sexiest job of the 21st century. The primary role of a Data scientist is to develop statistical models that gather data, derive and extract meaningful insights from the data and interpret it for decision making and problem-solving, in order to improve the company's performance. They play a vital role in helping companies reduce costs, detect patterns and trends, predict consumer behaviour, find new markets and make better decisions by using different disciplinary tools such as mining, statistics, machine learning, analytics and programming.
Data Science Use cases.
Data science applications are used in almost all the industries we can think of, be it the Banking sector, Manufacturing sector, Education sector, Finance industry, Transport sector, E-commerce, Media and Entertainment, healthcare, Agriculture, logistics, Marketing etc.
Banks and other financial institutions-Data science plays a crucial role in the banking and in recent times has become an indispensable part of the online banking sector.
- Detect frauds – The rapidly expanding digital world offers us ease but makes us vulnerable to frauds and cyber-attacks. Banks hire data scientists who apply Data analysis solutions and machine learning algorithms like association, clustering, forecasting and classification to detect frauds relating to payments, insurances, credit cards and customer accounting. This is done by identifying unusual activity of the user, unfamiliar spending patterns etc.
- Risk Management – Risk Management helps the banks to formulate new strategies for assessing their performance. With the help of and Data Science and big data banking industries are able to analyse and classify defaulters before sanctioning loan in a high-risk scenario. This model can also be used to access to the overall functioning of the bank.
- Customer's lifetime value (CLV) – Businesses like banking sectors are required to predict their customer lifetime value as customers are the most essential part of the banking industry. Banks use various data analytical tools such as Classification and Regression Trees (CART), Generalized Linear Models (GLM), etc.in monitoring their customers which in return contributes to the profitability of the company.
- Personalised Marketing – Data analytics enables banks to create personalized marketing that offers the right product to the right person at the right time on the right device by scrutinizing the behavioural, demographic, and historical purchase data suiting clients' needs and preferences.
Big data analysis helps e-commerce industries in making better strategic decisions, improves control of operations, better understanding of customers and reduces cost.
Case study- Amazon
Amazon is world's largest online market place, which has established itself through technological innovation and mass scale. Amazon relies heavily on big data and uses nearly 21 data science systems to operate its business.
Amazon uses predictive analytics and predicts products likely to be purchased by customers by tracking activities of the customers on the website, competitors pricing, product availability, item preferences, order history, expected profit margin.
Big data analytics also helps amazon in making recommendations based on what the customer ordered before, what he has saved in his cart using collaborative filtering. This enables amazon to streamline the process of persuading the customer to purchase a particular product.
Amazon has its own algorithms to detect fraud sellers, purchasers, fake reviews and also fraud detections of credit card transactions.
Data analysis plays a vital role in amazon's supply chain optimisation.
- Selection of warehouses in relation to proximity to vendors and consumers so as to minimize distributions costs.
- Selecting inventory of the products that should be kept at any time in the warehouse to reduce product decay and optimise profit.
- Amazon reduces costs of transportation of delivery trucks by nearly 40% by optimising delivery speed, gas usage etc.
- Amazon has also optimised its packaging of products through data collected from workers and customers.
Google the best-known search engine and even other search engines such as Yahoo, Bing, Ask, AOL, etc utilize data science algorithms to provide the best outcome for our searched query in a few seconds. Because Google processes more than 20 petabytes of information daily. If there has been no data science, Google wouldn't have been the one which we know today.
Travel and Transport sector-
Transport and travel industries employ big data to create a 360-degree view of each customer. This enhances their customer services by identifying the most valuable customer, fixing fares, making their travel safe and thereby improving efficiency.
Uber is one of the most popular cab services which can be booked with the help of our smartphones. Uber serves approximately 8 million users in its platform. Though booking a cab using the uber app seems so simple, it is very complicated at the back end.
Uber's entire business model is based on the very Big Data principle of crowd sourcing. It maintains a massive database of its customers and drivers and uses algorithms to match a customer's request with the most suitable driver.
Uber uses machine learning algorithms to analyse its multiple data and forecast where the highest demand will be, so that it can re-direct its drivers there. This insight will monitor demand and supply to ensure that it doesn't implement surge pricing. Machine learning tools help Uber to predict the exact time the driver will be at your door, or the estimated fares.
Uber works on the dynamic pricing model or surge pricing which adjusts rates based on a number of constituents such as time and distance of your route, traffic and the current rider-to-driver demand. So, when there is scarcity of drivers as compared to riders, the fares go up however when the demand is less, we are charged less fares. This calculation of fares by Uber using the statistical data analysis can give Uber's customers a positive user-experience.
Social Networking and Advertising-
Good advertising has always been crux of successful companies. Data Science has become critical for marketers and advertisers, who need to analyse endless signals in real time and deliver ads at the right moments to the right people. Machine learning plays an essential role in measuring consumer targeted advertising based on several traits of customer such as age, status, education, income, lifestyle, interests, personality etc.
Google Ads use a combination of AdWords, Google Analytics and DoubleClick Search to make it easy to bring together data from various marketing channels.
With millions of users around the world and the highest number 250 million only in India, Facebook has become the social media leader of the world today. Data scientists at Facebook conduct extensive research to gather deep insight into how people interact with each other and the world around them. Facebook has a clear do-it-yourself approach; it designs its own servers and networking and even builds its own data centres. Its staff writes most of its own applications and creates virtually all of its own middleware. Facebook stores enormous amounts of user data and analyses data making use of advanced technology called" deep learning" which in turn uses facial recognition & text Analysis. Facial recognition involves using" Deep face" to classify and recognise people in photos. Since Facebook uses a lot of text too, so It makes use of its own tool for understanding that it developed itself called "Deep Text" to extract meaning from words we post and also helps in understanding interest of the people by aligning photographs with texts. Facebook nearly makes around $4 billion in revenue from advertising and is therefore more than social media platform. Facebook uses deep neural networks and deep learning to decide what kinds of ads should be shown to which users. It gathers information from the users based on their preferences to provide them with appealing advertisements.
Media and Entertainment-
Advanced scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, can help companies to better understand consumer interests and have proved to be a boon in media and entertainment industry.
Netflix uses Data Science to cater relevant and interesting recommendation. The recommendation system uses different machine learning algorithms based on past viewed content. It uses Ranking Algorithms to provide a ranked list of movies and TV Shows content that appealed the most to its users.
Health care sector
Medicine and healthcare are a revolutionary and promising industry for implementing the data science solutions. Data analytics is moving the medical science to a whole new level, from computerizing medical records to drug discovery and genetic disease exploration and this is just facilitating the industry in reducing its expenses with the help of large amounts of data. Machine learning enables the health care sector to find the perfect balance between doctors and computers. The key is to automate simple routines, like using data science predictive analytics methods to process the patient data, make sense of clinical notes, find the correlations, associations of symptoms, familiar antecedents, habits, diseases, and then make predictions. The impacts of certain biomedical factors such as genome data management area, machine learning allows the creation of comprehensive registers of medical data, where all the paperwork will be transferred to a much more promising digital form. The whole medical history of a person will be stored in one system.
Coronavirus or covid-19 has scared the world and put a halt on the economy. Data science plays a very important role in learning the various aspects of covid-19 in these uncertain times. Several organizations, including Johns Hopkins University, IBM and Tableau, have released interactive databases that offer real-time views of what's happening with the virus. Data Science provides Accurate figures of Coronavirus Outcomes, giving detailed figures of confirmed cases, fatalities and also the recovery cases. Contact tracing plays significant role in slowing COVID-19. Data scientists devise a speeder way to handle contact tracing. Contact tracing is time-consuming process so data scientists in integration with the medical teams using mathematical models and mobile applications to make it easier. Almost all the nations are involved in the process of developing an antibody for the disease but the typical process of antibody discovery in a lab takes years. Two graduates of the data science program at Columbia University have turned to machine learning to help. The typical process of antibody discovery in a lab This approach, however, takes a week to screen for therapeutic antibodies with a high likelihood of success. The more we know about coronavirus, there is more possibility of saving lives. These are only some of the fascinating ways that data scientists are using their skills to help and contribute to this knowledge.
To sum up there are thousands of use-cases from car design, to insurance ,to pizza delivery where businesses use data science to make better products, deliver better services and optimize their operations to meet customers' expectations .An estimated 84% of the enterprises believe data science analytics are critical and essential in gaining an edge in this competitive market.
Case study-JP Morgan
JP Morgan makes extensive use of the popular open-source platform – Hadoop It is an ideal platform for accommodating both mobile platforms and internet services. It has a system of active tracking of phone calls and emails, for monitoring unusual and irregular transactions. JP Morgan makes optimised use of its internet and digitization platform to analyse and process its customers queries and provides them with cash forecasting, incrementing their turnover to gain an edge over competitors. It makes use of predictive analytics to effectively manage client's cash and detect loopholes. Datawatch platform at this bank provides intuitive information to customers with real-time analytics. This leading bank in the United States has developed a smart contract system called Contract Intelligence (COiN). The algorithm based on data and machine learning helps quickly find necessary documents and important information contained in them.