Inspiring Facts: 5 Famous Companies and brands use Data Science to improve their performances

This is a little effort to show the power behind Data Science. Let’s take a closer look at some companies and brands using such platforms to improve performance and efficiency and deliver better customer experiences.

 

#01 Walmart

• Walmart uses data mining to discover patterns in point of sales data. Data mining helps Walmart find patterns that can be used to provide product recommendations to users based on which products were bought together or which products were bought before the purchase of a particular product.
• A familiar example of effective data mining through association rule learning technique at Walmart is – finding that Strawberry pop-tarts sales increased by 7 times before a Hurricane. After Walmart identified this association between Hurricane and Strawberry pop-tarts through data mining, it places all the Strawberry pop-tarts at the checkouts before a hurricane. 
• Another noted example is during Halloween, sales analysts at Walmart could look at the data in real-time and found that thought a specific cookie was popular across all Walmart stores, there were 2 stores where it was not selling at all. The situation was immediately investigated, and it was found that simple stocking oversight caused the cookies not being put on the shelves for sales. This issue was rectified immediately which prevented further loss of sales.

 #02 McDonald's

    McDonalds is another famous company that use data science to increase their performances. Their updated mobile app allows customers to order and pay almost entirely via their mobile devices. To make the experience that much more enjoyable, they gain access to exclusive deals, too. In return for the convenience, McDonald’s collects essential information about their audience. They can see what foods and services customers order, how often or even whether they visit the drive-thru or go inside. All this data allows for more targeted promotions and offers. In fact, Japanese customers using the company’s mobile app spend an average of 35 percent more because of spot-on recommendations just before they are ready to order food.

#03 Spotify

Spotify is another brand name which uses Big data for a better user experience. it uses AI and big data to deliver better playlists and streaming content recommendations to its users. The Discover Weekly feature is an excellent example of this in action. Each week, Spotify offers every user a personalized playlist with music recommendations based on their listening and browsing history. It’s kind of like a curated mixtape from the platform, offering new tracks and artists, showing you new genres you might enjoy or even updating you on your favorite music.

This feature is possible thanks to a vast trove of information and data they collect from their user base. When you have millions of people listening to music every day, you gain some pretty deep insights into user habits and preferences.

The company has also launched a “Spotify for Artists” app that lets bands and music artists see analytics related to their content.

 

#04 Amazon

The online retail giant has access to a massive amount of data on its customers; names, addresses, payments and search histories are all filed away in its data bank.

While this information is obviously put to use in advertising algorithms, Amazon also uses the information to improve customer relations, an area that many big data users overlook.

The next time you contact the Amazon help desk with a query, don't be surprised when the employee on the other end already has most of the pertinent information about you on hand. This allows for a faster, more efficient customer service experience that doesn't include having to spell out your name three times.

#05 CocaCola

The company collects data on its customers to boost current consumption and upsell new products, which has led to a more efficient operation that cuts costs and boosts profits. As consumers share their opinions of the product through social media, phone or email, it allows the company to adjust its approach and better align with consumer interests and demands. The data the company collects is aimed at improving the brand experience and developing greater customer loyalty.

Why Data Science? & Why it’s so important?

Now we have a clear idea about what data science is and about the history of the data science. Will now explore why we need something like data science and what’s the importance of the data science to the world. 

Before that will look at why data matters this much in the current world.

Data is the electricity in the current world, Fuel to run the world. As it was mentioned in the 1^st article, we are living in the age of the 4^th industrial revolution. Which is the era od Artificial Intelligence and Big Data.  There is a massive data explosion that has resulted in the culmination of new technologies and smarter products. Around 2.5 exabytes of Data is created each day. The need for data has risen tremendously in the last decade.

Just think of this amount of data produced in every millisecond throughout the world! Then assume the world without data science! All the data will be just another raw material simply a rubbish which will be gathered and disposed without any usage.

Before data scientist there were statisticians who use data. These statisticians experienced in qualitative analysis of data and companies employed them to analyze their overall performance and sales. With the advent of a computing process, cloud storage, and analytical tools, the field of computer science merged with statistics.

 This gave birth to Data Science!

Data is a magic while data scientists are wizards who know how to use data in a insightful way. Data Scientist will know how to dig out meaningful information with whatever data he comes across. He helps the company in the right direction.

Summarizing Data science or data-driven science enables better decision making, predictive analysis, and pattern discovery. It lets you.

Before

·         Find the leading cause of a problem by asking the right questions.

·         Perform exploratory study on the data.

·         Model the data using various algorithms. 

·         Communicate and visualize the results via graphs, dashboards, etc.

In practice, data science is already helping the airline industry predict disruptions in travel to alleviate the pain for both airlines and passengers. With the help of data science, airlines can optimize operations in many ways, including:

·         Plan routes and decide whether to schedule direct or connecting flights.

·         Build predictive analytics models to forecast flight delays.

·         Offer personalized promotional offers based on customers booking patterns. 

Decide which class of planes to purchase for better overall performance.

Trip into the history of data science.

Data Science has revolutionized several different aspects of our world. Let's take a look then at when and where data science comes from.

·         In 1962, John W. Tukey wrote in “The Future of Data Analysis” - The first milestone in the history of data science is globally recognized for the bright American mathematician John Tukey. The influence of John Tukey in statistical terms is enormous, but the most famous coinage attributed to him is related to computer science. In fact, it should be mentioned that he was the first to introduce the term "bit" as a contraction of "binary digit."

·         In 1974, Peter Naur published the Concise Survey of Computer Methods, which surveyed data processing methods across a wide variety of applications. The term “data science” becomes clearer, as he puts his own definition on it: “The science of dealing with data, once they have been established, while the relation of the data to what they represent is delegated to other fields and sciences.”

·         In 1977, the International Association for Statistical Computing (IASC) was founded.

·         In 1989, Gregory Piatetsky-Shapiro organized and chaired the first Knowledge Discovery in Databases (KDD) workshop.

·         In 1994, BusinessWeek published a cover story on “Database Marketing.”

·         In 1996, on the occasion of the conference of the International Federation of Classification Societies (IFCS), for the first time, the term “data science” is included in the title of the conference (“Data science, classification, and related methods”). In the same year, Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth publish “From Data Mining to Knowledge Discovery in Databases.”

·         In 1997, during his inaugural lecture as the H. C. Carver Chair in Statistics at the University of Michigan, Jeff Wu called for statistics to be renamed “data science” and statisticians to be renamed “data scientists.”

 

 

Data Science: Most Blooming field in the 21st century.

    Our digital world creates a massive amount of data each and every second. Thanks to the communication devices, sensors and computations we use they capture information of great value to business and government across the globe. There is an explosion of data around us, and Data Science and Big Data are transforming our world and touching our daily lives like never before. It is believed that over 2.5 quintillion bytes (2.5 e+9 GB) Data is created every day and the number is increasing in order. 

Companies like Walmart, Adidas Entertainers like Spotify, Netflix Search engine companies such as Google, Yahoo! Leagues like NBA (National Basketball Association is a professional basketball league in North America.) USE has created an entirely new business model by capturing the information freely available on the web and providing it to people in useful ways. They collect trillions of bytes of data every day and continually add new services to their businesses to improve them.

Data science enables businesses to process huge amounts of structured and unstructured big data to detect patterns. This in turn allows companies to increase efficiencies, manage costs, identify new market opportunities, and boost their market advantage.

Asking a personal assistant like Alexa or Siri for a recommendation demands data science. So does operate a self-driving car, using a search engine that provides useful results, or talking to a chatbot for customer service. These are all real-life applications for data science.

• Will see some of the definitions from some famous websites.

1. Definition from IBM

Data science is a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations. Data science encompasses preparing data for analysis and processing, performing advanced data analysis and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.

 2. Definition from Edureka

Data Science is a blend of various tools, algorithms, and machine learning principles to discover hidden patterns from the raw data.

3. Definition from Wikipedia

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from many structural and unstructured data. 

4. Definition from DataRobot

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users can translate into tangible business value.

 5. Definition from ComputingForAll

Data science is a field of study that focuses on techniques and algorithms to extract knowledge from data. The area combines data mining and machine learning with data-specific domains.

6. Definition from Omni. sci

Data science enables businesses to process huge amounts of structured and unstructured big data to detect patterns. This, in turn, allows companies to increase efficiencies, manage costs, identify new market opportunities, and boost their market advantage.

• This is a simple video that will clearly describe data science.