13 Amazing Uses of Data Science Today
Some of the best uses of data science in the modern world.
Take a look at our data and analytics roles
When we speak of search, we think ‘Google’, but there are many other search engines like Yahoo, Bing, AOL, Duckduckgo etc. All of these search engines use data science algorithms to deliver the best results for our search query in a fraction of seconds. Considering that Google processes more than 20 petabytes of data every day. Had there been no data science, Google would not have been the ‘Google’ we know today.
Digital Advertisements (Targeted Advertising and re-targeting)
From display banners on websites to the digital billboards at airports- almost all of them are decided by using data science algorithms. This is why digital ads have been able to get a lot higher CTR than traditional advertisements. They can be targeted based on the user’s past behaviour.
The suggestions about similar products on Amazon are not only to help find relevant products from billions of products available with them but also adds a lot to the user experience.
A lot of companies have fervidly used this engine/system to promote their products/ suggestions in accordance with user’s interest and relevance of information. Internet giants like Amazon, Twitter, Google Play, Netflix, LinkedIn, IMDB, and more use this system to improve their user experience. The recommendations are made based on previous search results from a user.
You upload your image with friends on Facebook and you start getting suggestions to tag your friends. This automatic tag suggestion feature uses a face recognition algorithm. Similarly, while using WhatsApp web, you can scan a barcode in your web browser using your mobile phone. In addition, Google provides you with the option to search for images by uploading them. It uses image recognition and provides related search results. To know more about image recognition, check out this amazing (1:21) mins video:
Some of the best examples of speech recognition products are Google Voice, Siri, Cortana etc. Using speech recognition feature, even if you aren’t in a position to type a message, your life wouldn’t stop. Simply speak out the message and it will be converted to text. However, at times, you wouldn’t realize, speech recognition doesn’t perform accurately.
EA Sports, Zynga, Sony, Nintendo, Activision-Blizzard have led the gaming experience to the next level using data science. Games are now designed using machine learning algorithms that improve/upgrade themselves as the player, moves up to a higher level. In motion gaming also, your opponent (computer) analyses your previous moves and accordingly shapes up its game.
Price Comparison Websites
At a basic level, these websites are being driven by lots and lots of data which is fetched using APIs and RSS Feeds. If you have ever used these websites, you would know, the convenience of comparing the price of a product from multiple vendors in one place. PriceGrabber, PriceRunner, Junglee, Shopzilla, DealTime are some examples of price comparison websites. Nowadays, price comparison websites can be found in almost every domain such as technology, hospitality, automobiles, durables, apparels etc.
Airline Route Planning
Airline Industry across the world is known to bear heavy losses. Except for a few airline service providers, companies are struggling to maintain their occupancy ratio and operating profits. The high rise in air-fuel prices and the need to offer heavy discounts to customers have further made the situation worse. It wasn’t long before airline companies started using data science to identify the strategic areas for improvement. Now using data science, airline companies can:
- Predict flight delay
- Decide which class of aeroplanes to buy
- Whether to directly land at the destination or take a halt in between (For example A flight can have a direct route from New Delhi to New York. Alternatively, it can also choose to halt in any country.)
- Effectively drive customer loyalty programs
Southwest Airlines, and Alaska Airlines are among the top companies who’ve embraced data science to bring changes in their way of working.
Fraud and Risk Detection
One of the first applications of data science originated from the Finance discipline. Companies were fed up with bad debts and losses every year. However, they had a lot of data that use to get collected during the initial paperwork while sanctioning loans. They decided to bring in data science practices in order to rescue them from losses. Over the years, banking companies learned to divide and conquer data via customer profiling, past expenditures and other essential variables to analyze the probabilities of risk and default. Moreover, it also helped them push their banking products based on customers’ purchasing power.
Who says data science has limited applications? Logistic companies like DHL, FedEx, UPS, Kune+Nagel have used data science to improve their operational efficiency. Using data science, these companies have discovered the best routes to ship, the best suited time to deliver, the best mode of transport to choose thus leading to cost efficiency, and many more to mention. Furthermore, the data that these companies generate using the GPS installed, provides them with a lot of possibilities to explore using data science.
Apart from the applications mentioned above, data science is also used in Marketing, Finance, Human Resources, Health Care, Government Policies and every possible industry where data gets generated. Using data science, the marketing department of companies decide which products are best for Upselling and cross-selling, based on the behavioural data from customers. In addition, predicting the wallet share of a customer, which customer is likely to churn, which customer should be pitched for a high-value product and many other questions can be easily answered by data science. Finance (credit risk, fraud), Human Resources (which employees are most likely to leave, employees performance, decide employees bonus) and many other tasks are easily accomplished using data science in these disciplines.
Content by Analyticsvidhya
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