Data Science in Business

What is Data Science? According to 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. Data science is related to data mining, machine learning, and big data.” In simple terms, it is a discipline to predict the future, based on past data using computer algorithms where statistical methods are used in the hidden side.

Data Science is rapidly becoming a prominent and powerful marketing tool in business, offering a wide range of benefits for organizations looking to take advantage of market opportunities and increase their profits. Data Scientists provide strategic and tactical advice based on past data while integrating multiple disciplines to identify new opportunities and improve business performance. They use a combination of data mining, predictive data analytics, and machine learning algorithms to provide insight to businessmen about how human habits, processes, and technologies affect business performance. Their actionable outputs allow companies to harness new information, process it in an organized manner, and ultimately improve business performance.

So, in which areas are data science used in the business? To find an answer to this question, we will briefly mention the uses of data science in the e-commerce, natural language processing, image processing, and finance sectors.

E-Commerce Use Cases in Business

E-commerce (electronic commerce) is the activity of electronically buying or selling products on online services or over the Internet. Data science has important uses in the e-commerce sector as well as many other fields. Some uses of data science in the field of e-commerce are given below.

Personalized Product Recommendation:

Information collected from customers provides significant clues about their purchasing habits. The more data can be collected from people in the world, the more advertising expenditures can be made to the right people. For example, the great effort the WhatsApp company has recently made to transfer its data to the Facebook company is purely about providing the right advertisements to the right people. In other words, it is to get the maximum benefit from advertisements by making personalized product recommendations.

Forecasting Extra Purchases:

Thanks to data science, it can easily be predicted which other products a shopper can buy. This method enables e-commerce platforms to offer the most likely products to the customer. For example, a customer who buys a mobile phone can be immediately recommended mobile phone case models.

Identifying purchasing trends and customer behaviors:

The use of data science in e-commerce allows for the discovery of purchasing trends and customer behaviors. Which age group is doing what kind of shopping? What kind of advertisements affect which customer groups? What kind of products are needed in the market or which products are out of date? These questions can be answered by data science.

Customer Segmentation/Customer Lifetime Value Prediction:

Customers using the e-commerce platforms have different behaviors and requirements. If e-commerce platforms do segmentation and adjust their implementations according to customers’ needs and behaviors, that will have a positive effect on sales. Based on customers’ past data, the RFM (Recency-Frequency-Monetary) method can be used to divide customers into Low-Value, Medium-Value, and High-Value groups. Thus, different approaches can be created for different groups.

Predicting Future Sales:

Predicting future sales has very important benefits for e-commerce companies in terms of stocks and personnel planning. In this respect, time-series prediction is one of the major fields of Data Science. Thanks to LSTM (Long Short Term Memory) which is one of the popular deep learning methods used for time-series predictions, future sales can be predicted very accurately.

Use Cases of Natural Language Processing in Business

NLP or Natural Language Processing is a subfield of Artificial Intelligence that gives machines the ability to understand and extract meaning from human languages. NLP is an area that focuses on the interaction between data science and human language. It allows data scientists to derive meaningful results in areas like media, healthcare, finance, and human resources, etc. Nowadays NLP is booming thanks to the huge improvements in acquiring the data and the sharp increase in computational power. NLP can help businesses with many tasks. Some cases are given below.


Diagnosing means the prediction of diseases based on the patient’s speech and electronic health records. This method allows the patient to be diagnosed with great accuracy before seeing the doctor.

Sentiment Analysis:

Determining what customers are feeling about a product or service by extracting information from sources like social media is called sentiment analysis. Today, the major e-commerce companies can extract emotions from millions of comments with this method.


In the last 5-10 years, a great improvement has been achieved in translation quality with the transition of online translation services from classical methods to NLP-based translation method. Online translators like Google Translator have never been so successful before until NLP was used in that field.


Communicating with the customers like an actual employee is achieved by chat-bots. The messaging boxes opened in the lower right corner of the

web site you visit are chat-bot examples. In this way, customers are assisted at low cost at all hours of the day.


Classifying emails:

This service means classifying emails as spam or ham and stop spam before they even enter the inbox. It is very crucial to automate this action if the company receives millions of emails.

Detecting the Fake News:

To determine if a source is politically biased or accurate, detecting if a news source can be trusted or not is very important because there can be lots of fake news in the virtual world.

Voice-Driven Interfaces:

Intelligent voice-driven interfaces that use NLP to respond to humans like Apple’s Siri or Android’s Iris is very popular in recent years.

Trading Algorithms:

Tracking news, reports, comments about financing to sell or buy the stocks automatically enables customers to take rapid actions.

Recruiting Assistant:

Today, companies have long meetings to find the right employee, and this causes a serious waste of time. Assisting both in the search and selection phases of new employees and identifying the skills of potential hires can help companies with that problem.

Litigation Tasks:

Most of the cases in the courts are similar to each other and robots can make a decision to automate routine litigation tasks and help courts save time.

Use Cases of Image Processing in Business

Image processing is the technique of manipulating an image to either improve the quality or extract key information from it. Artificial intelligence image processing services combine high-order artificial intelligence with powerful computer vision and neural networks to rapidly and accurately process terabytes of images from all over the world with high accuracy. The most popular uses of this technology are for visual Search Engine Optimization (SEO) services, content discovery, facial recognition, image retouching, image stabilization, real-time facial analysis, rapid prototyping, and many more.  It can be used for training programs for websites, facial image analysis, product catalogs, e-commerce websites, marketing promotion, surveillance detection, manufacturing automation, manufacturing inspection, healthcare product precision, surveillance camera positioning, weather prediction, etc.

Finance Use Cases in Business

So far, we have examined the usage areas of data science in e-commerce, natural language processing, and image processing. Another area where data science is effectively used is the finance sector. In finance, data science can be used in risk analytics, real-time analytics, customer analytics, personalized product recommendation, fraud detection, trading algorithms, etc.

Risk Analytics:

Analyzing risk is very important for large companies to make strategic decisions. Data science has a significant role to analyze the risk specific to the customer.

Real-Time Analytics:

Thanks to the integration of data science models into live databases, real-time analysis, and accurate decisions can be made. The dynamic data pipelines link the artificial intelligence algorithms and the databases, this allows rapid analysis. This method enables data science to be used effectively particularly in the banking sector.

Customer Analytics:

Customer Segmentation/Customer Lifetime Value Prediction methods have already been described under the e-commerce title. The same methods can also be used in the finance sector.

Personalized Product Recommendation:

Offering products that can be of interest to the customer is an action that will increase the profit of every business. This is made possible by analyzing the customers’ data.

Fraud Detection:

As the number of transactions in financial institutions increases, the number of fraud transactions will inevitably increase. It is impossible to manually find out which are fraud transactions among billions of transactions every day. Thanks to the integration of artificial intelligence’s algorithms for fraud detection, these problems are solved to a great extent.

Trading Algorithms:

Financial markets are extremely sensitive to daily events and stockholders want to sell their stocks or buy new stocks in the face of these daily events. Today, trading algorithms can perform these transactions on behalf of customers according to the information they gather from the daily broadcast stream. Thus, customers do not miss daily opportunities thanks to machines that operate in a much shorter time than their action time.


There are many implementation areas of data science for companies and these areas are increasing day by day. Data Science helps companies take actions to mitigate risk, strengthen their competitive position, and increase their profits. Businesses that benefit from the power of data in their services provide significant advantages over businesses managed with conventional methods and that’s what we are giving utmost importance to data science implementations in our solutions, here in Nioya Tech.

In this article, we mentioned the uses of data science in e-commerce, finance, natural language processing, and image processing areas. However, the uses of data science in the business world are of course not limited to these fields, and the number of businesses that benefit from the power of data science is increasing continuously.

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