Google processes more than 99,000 search queries per second, and Instagram uploads a thousand photos or even more. So the volume of data on the Internet is growing at a cosmic rate. Data is everywhere, and it is a resource for business and marketing. It processes and analyzes vast amounts of information, answering business questions in retail, industry, banking, and other areas. In this article, you’ll learn what data science is and precisely what applications it finds in business due to examples from well-known companies.
What Data Science Studies
Humanity generates about 2.5 quintillion bytes of data every day. They are created every time people click and scroll through a page, not to mention watching videos and photos on online services and social networks. Data science has been around long before its volume exceeded every conceivable prediction. The countdown began in 1966 when CODATA (Committee on Data for Science and Technology) appeared worldwide.
It was created as part of the International Science Council to collect, evaluate, store, and retrieve the most important data for scientific and technical purposes. The term data science came into use in the mid-1970s at the suggestion of the Danish computer scientist Peter Naur. According to his definition, this discipline studies the life cycle of digital data from emergence to use in other fields of knowledge.
Over time, however, the definition has become broader and more flexible. Today, it is an interdisciplinary field at the intersection of statistics, mathematics, systems analysis, and machine learning. It covers all data manipulation stages and focuses on practical results. Widespread use of the mobile Internet, the popularity of social networks, the general digitization of services and processes, and other essential factors have prompted the development of data science.
Also, people often associate this term with big data and data analysis technologies. Although these fields sometimes overlap, it’s better not to confuse them. Of course, they all involve understanding large amounts of information. But while data analytics answers questions about the past (changes in customer behavior of an online service over the past few years), data science looks to the future.
Moreover, DS specialists can create models based on big data that predict what will happen tomorrow. It includes predicting the demand for certain goods and services. And if you want more information on this subject, you can use Trust My Paper, where you can find a specialist who will provide you with all the information you need.
Why Businesses Need Data Science
Companies use data science regardless of the size of their business. Kaggle statistics demonstrate such results. Also, many modern companies confirm that the amount of information they analyze and use has increased significantly in the last few years. So businesses realize that unstructured information contains knowledge that is very important for companies and can affect business results. And it applies to a wide variety of industries. Here are just a few examples of fields that use data science to solve their problems:
|Online commerce and entertainment services||Recommendation systems for users|
|Healthcare||Disease prediction and recommendations to maintain health|
|Logistics||Planning and optimization of delivery routes|
|Digital advertising||Automated content placement and targeting|
|Finance||Scoring, fraud detection, and prevention|
|Industry||Predictive analytics for repair and production planning|
|Real estate||Search and offer the most suitable objects for the buyer|
|Public administration||Forecasting of employment and economic situation, fighting crime|
|Sports||Selecting promising players and developing game strategies|
Use Cases for Businesses
Data science technologies make collecting and analyzing information more manageable for the company’s benefit. It makes it easier for businesses to find the right solutions to achieve their goals. Machine learning algorithms are rapidly evolving, forecasts based on them are becoming more accurate, and many other applications exist. So let’s look more attentively at seven typical ones.
# 1 Anomaly Detection
Working with a small amount of information isn’t very difficult to distribute it into clusters or groups and then detect anomalies. And many small organizations do it successfully. But what about large financial companies analyzing petabytes or exabytes of data daily? Well, that’s a highly sophisticated task, made even more difficult by the prevalence of fraud in transaction data. So what’s the solution to this situation?
The best option here is to use data science to quickly detect anomalies and respond to events and changes, as American Express has done. It also prevents cyberattacks, monitors the performance of IT systems, and eliminates exclusionary values in data sets to improve analysis accuracy.
# 2 Pattern Recognition
It enables retail and e-commerce businesses to detect trends in customer buying behavior. And this is where the relevance of offers and chain reliability become most important to companies. But it’s no wonder because if customers are happy with your products and services, they won’t buy similar goods from your competitors.
That’s why market giants like Amazon and Walmart have decided to use data science to detect buying trends. For example, Walmart concluded that most customers tend to buy strawberry Pop-Tarts in anticipation of a hurricane or tropical storm. Thus, such correlations can help develop more effective purchasing, inventory management, and marketing strategies.
# 3 Predictive Modeling
Data science can dramatically improve decision-making capabilities with machine learning and various algorithmic approaches to large amounts of information. It creates models that can predict market trends, customer behavior, financial risks, and other factors necessary to any business.
For example, predictive maintenance systems help manufacturers reduce equipment breakdowns and increase production uptime. Big companies like Boeing and Airbus use predictive maintenance to increase fleet availability. The situation is similar at Chevron and BP, which, through modeling, improve equipment reliability in environments where maintenance is quite costly.
# 4 Classification and Categorization
Processing and analyzing unstructured data (images, video, audio, emails) is challenging. However, with the advent of deep learning, the work of organizations has been dramatically simplified. Artificial neural networks make it possible to analyze vast amounts of data. Thus, companies are now much more effective at object, image, and audio recognition tasks and classifying data based on the document type.
For example, NASA uses image recognition to get more detailed and complete information about objects in space. Another example is the U.S. Bureau of Labor Statistics, which automates the classification of workplace injuries based on the analysis of accident reports. If you want to get even more examples regarding this area of data science application, you can use Best Essays Education, where you can find an expert to help you with this issue.
# 5 Sentiment and Behavioral Analysis
Sentiment and behavioral analysis applications allow companies to multiply the effectiveness of identifying buying and usage patterns. Also, data science opens the veil on how customers feel about a particular product or service and shows the level of their satisfaction with certain goods.
Classifying people’s moods and behaviors and how these factors change over time is possible with these applications. For example, Airbnb analyzes information from users: the options they looked at and what they ended up choosing. So data science can predict the likelihood of bookings in some city regions.
# 6 Conversational Systems
The earliest application of machine learning was the development of a chatbot that could have conversations without human intervention. The Turing test, which saw the light of day in 1950 thanks to computing pioneer Alan Turing, uses a conversational format to determine whether a system can mimic human intelligence. Consequently, many companies are now actively using various chatbots and other conversational systems to augment existing workflows and take over some of the tasks previously performed by employees.
# 7 Autonomous Systems
Data science is one of the driving forces behind the development of autonomous cars and robots driven by artificial intelligence. But, of course, the list of intelligent machines doesn’t end with these examples, and there’s more to come. As the use of data science tools and techniques in enterprises expands, so will the types of applications they enable.
However, there’s still much work to bring autonomous systems to life. For example, image recognition tools in a car must be trained to recognize all relevant elements: other vehicles, roads, traffic control devices, pedestrians, and other factors that make for successful driving. But we are confident that this is not far off.
Big data is pervasive in our lives, and it’s only a matter of time before data science is introduced into all business sectors. Large corporations are already processing data and creating algorithms. All business areas need to be able to predict events and assess risks, not to mention the most common use of machine learning algorithms, making automated recommendations and increasing customer engagement.