Data is a critical component that is needed for the smooth functioning of businesses. Data also impacts our personal lives considering the fact that each of us, in our daily lives, generate huge amounts of data, especially through social media. Every industry in this world needs data. With the advancements in computational capabilities, companies are now able to analyze large scale data to gain useful insights from the massive horde of information and help industries, in particular, make data-driven decisions.
In this article, we will discuss the top 10 diverse data science use cases and applications. But before that, we will understand the term ‘Data Science.’
What is Data Science?
Data science is a blend of various algorithms, tools, and machine learning principles that operate with the goal of discovering hidden patterns from raw data. It is used to make decisions and predictions by using prescriptive analysis, predictive causal analysis, and machine learning. It is used to scope out the right questions from the dataset. It is a multidisciplinary field that works at the raw level of data (structured, unstructured, or both) to make predictions, identify patterns and trends, build data models, and create more efficient machine learning algorithms. Data science experts work in the realm of the unknown. Some of the data science techniques are regression analysis, classification analysis, clustering analysis, association analysis, and anomaly detection.
1. Airline Route Planning
The airline industry is one that is known to bear heavy losses. Apart from a few airline service providers, many companies are struggling to main their occupancy ratio and operating profits. The rise in air-fuel prices and the need to offer heavy discounts to customers have made this situation even worse. Due to these issues, airline companies have now started using data science to identify strategic improvement areas. With data science, airline companies can now:
- Decide the class of airplanes to purchase.
- Predict flight delays.
- Effectively drive customer loyalty programs.
- Choosing to directly land at a destination or take a halt in between destinations.
Nowadays, games are now designed using machine learning algorithms that have the ability to improve or upgrade themselves as the player moves up to higher levels. In motion gaming also, data science aids the opponent (computer) in analyzing your previous moves and shaping up the game accordingly. Examples of some companies that have led the gaming experience to the next level using data science are Zynga, Sony, Nintendo, EA Sports, etc.
3. Fraud and Risk Detection
One of the earliest applications of data science was in the field of finance. Companies were getting fed up with incurring bad debts and losses every year. But they had a lot of data that got collected sue to the initial paperwork that was involved while sanctioning loans. This is when the companies decided to bring in data scientists to bring them out of their losses. But over the years, banking institutions have learned to divide and conquer data through customer profiling, past expenditures, and other essential variables for analyzing the probabilities of risk and default. It also helped them to push their banking products based on the purchasing power of the customers.
4. Targeted Advertising
If you were of the opinion that search would have been the largest of all data science use cases or applications, let me give you a challenger- the whole digital marketing spectrum. Starting from the display banners to the digital billboards, all of them are decided using data science algorithms. This is the main reason for digital-ads to have more Call-Through rate (CTR) in comparison with traditional advertisements. These can be targeted based on the past behavior of a user. This is the reason why we see ads about data science training programs and an advertisement for apparels in the same place at the same time.
5. Augmented Reality and Virtual Reality
Data science is also used in augmented reality, and virtual reality as the two have a strong relationship. Augmented reality deals with altering the perception of the real-world environment. Virtual reality refers to using computer technology to create a simulated environment.The headsets used in a virtual reality setting contain algorithms, data and computing knowledge to provide users with the best viewing experience. The high trending game Pokemon Go is a perfect example of this. This game uses data science to provide the ability to walk around things and look at Pokemon on streets, things, and walls that are not really present.
Till now, we have analyzed the general use cases. Let us now understand the use cases of data science specific to companies, products, or services.
6. Bank of America- Leveraging Customer Experience With Data
Bank of America makes use of data science and predictive analytics. Data science helps banking industries detect frauds in areas such as payments and customer information. It helps prevent frauds relating to credit cards, insurances, and accounting. To minimize losses, banks need to detect frauds faster. For this purpose, banks employ data scientists who will apply their quantitative knowledge to algorithms like clustering, association, forecasting, and classification.
Risk modelling is also another area in which banks use data science to regulate financial activities. Banks will be able to minimize risk modelling by using machine learning. Using analytical solutions, banks can make data-driven decisions that are based on transparency and risk analysis. Bank of America was able to detect high-risk accounts using the technology of big data. Several banks like Bank of America are now understanding their customers using the intelligent customer segmentation approach. A data scientist makes use of various techniques such as decision trees, clustering, and logistic regression to help banks understand the Customer Lifetime Value (CLV) and group them into appropriate segments.
7. Airbnb- Leveraging Data to Make Stays More Comfortable
Airbnb is an international hospitality company that lets you host accommodations and find them through mobile apps and websites. This is a data-centric industry. It contains a large amount of customer and host information, website traffic, and homestays and lodge records. Data science plays a pivotal role in Airbnb. It makes use of data to provide better search results to customers. Airbnb uses knowledge graphs where the preferences of the users are matched with various parameters for providing ideal lodgings and localities. It optimizes its search engine to provide better results to customers and help find compatible hosts.
8. Uber- Data to Make Rides Better
Uber, as most of us are aware, is a smartphone application that lets you book a cab. Uber uses big data extensively, as it has to maintain a large database of customers, drivers, and other records. It uses big data to derive insights and provide the best services to users. Uber uses the big data principle with crowdsourcing, which means that registered drivers in the area can assist anyone who wants to go somewhere. Uber uses data science to calculate surge pricing. When there is a scarcity of drivers in a particular area, the price of the ride goes up. When the demand for Uber rides is less, Uber then charges a lower rate. Such dynamic pricing is rooted in big data. It uses data science to calculate the fares based on the parameters.
9. Facebook- Revolutionizing Social Networking and Advertising
Facebook is, undoubtedly, the social media leader of the world today. Facebook uses data science to gain insights into the social interactions of people. Facebook is a hub of innovation that uses advanced data science techniques to gain insights to improve products and study user behavior. Facebook uses deep learning for text analysis and facial recognition. In facial recognition, it uses powerful neural networks for classifying the faces in photographs. It uses DeepText, its own text understanding engine, to understand user sentences, understanding people’s interests, and aligning photos with texts. Facebook makes use of deep learning for targeted advertising. This way, it decides the kind of advertisements users must view. It uses the insights obtained from the data to group users based on preferences and also provides them with advertisements that appeal to them.
Amazon relies on predictive analytics to increase customer satisfaction. It does this through a personalized recommendation system. This system is a hybrid type that involves collaborative filtering. Amazon analyzes the purchases of the user for recommending more products. It uses an anticipatory shipping model to analyze the pattern of purchases. It also sends products to the nearest warehouse. Amazon optimizes the prices on its websites by analyzing various parameters such as prices, user activity, order history, product availability, etc. Another area that all e-commerce platforms address is fraud detection. Amazon uses algorithms to detect fraudulent purchases and fraud sellers.
I hope you have now gained a fair idea of the top data science use cases of data science. To become a data science expert and know more about data science certifications, check out Global Tech Council.