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Top 7 Applications of Data Science in Business

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  • Last Updated : 19 Jul, 2021
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Data Science is a sub-part of Big Data that has characterized itself by velocity (of datasets reached unprecedented heights), variety (of datasets that can be collected and delivered in a single format), and volume (of datasets which are increasing massively with changing times). This lets businesses evaluate complex structured or non-structured datasets and draw relevant insights in an efficient way. Whether we talk about E-Commerce or Healthcare, Data Science and its applications are setting trends as well as acting as a fuel for the industries that want to get rid of loads of paperwork.


You may think about how existing businesses are able to derive data-driven decisions while assessing the purchasing power of their customers! Through analysis and segmentation established by analytics of Data Science and ML algorithms, it is possible for businesses to keep themselves steadily ahead in the competition. Let’s find out about the top applications of Data Science, which are letting organizations inch a bit towards the interests and enthusiasm of their customers while purchasing the products fulfilling their requirements.

1. DS is Capturing the E-Commerce Market

E-Commerce (& Retail) Corporations primarily focus on selling or purchasing goods and services via the internet. With the help of Data Science, filtering the styles of products that customers prefer to recommend can be done in a collective manner. Moreover, if some changes or fluctuations may occur in the prices, this may also be identified via predictive analysis. Such an analysis accepts statistical algorithms for predicting the likelihood of interest or hatred towards the products customers may select in the future. Indeed, if the trends related to the services may change and point-of-views of customers fluctuate, this is promisingly detected via sentiment analysis somewhere analyzing the feedbacks well through online surveys/ reviews. All this can be spotted in the applications of Trivago, Starbucks, and H&M, which are booming in the E-commerce market in the year 2021. In this way, E-Commerce or retail corporations are successful in classifying the content of their services flexibly, thereby empathizing with their customers’ emotions by providing them with relevant insights in the form of recommendations.  

2. DS is All-Pervasive with Banking and Finance

Banks and other financial institutions are always looking for smarter ways to sort lending criteria and manage consolidated debts or other finances. The reason is obvious – all this will help categorize the finances for personal, public, or corporate usage. Here, the biggest challenge for financial institutions is keeping themselves ahead in the competition and, because of that, Data Science lets those institutions make smarter decisions that can be difficult for the human eye to spot when required. Through CLV (an unavoidable part of DS) i.e Customer Lifetime Value Prediction, those institutions may accurately estimate how long their customers can generate revenue for them? Meanwhile, if the banks may get stuck at selecting the best marketing decisions due to some risks associated with credit cards or insurance, this can be assessed through risk modeling. All this is done by banks through apps like Personal capital, Every dollar, Mobills which pervasively accepted risk modeling and value prediction notions of Data Science.    

3. DS has Linked Itself Well with the Healthcare Industry

The Healthcare industry is always into (and will always be) managing the health of the admitted patients by dispensing medication at proper times within the patient’s budget. But sometimes, there are anomalies identified in day-to-day operations. Such anomalies occur in the form of improper mental health or nutrition which aren’t detected many a time through MRIs or CT-Scans. There, Data Science comes to the rescue with its Medical Image Analysis and Predictive Modeling of a patient’s gene expression. The benefit is that doctors primarily dealing with complex surgeries can use powerful IR (Image Recognition) tools to deeply understand anomalies in those surgeries and search for clues that classify the disease well with its appropriate medical solutions.  

Undoubtedly, predictive modeling of a patient’s gene expression helps doctors review and analyze the historical data of the admitted person. Later, they can make excellent correlations with the molecular biology of central dogma whose queries are answered well by intelligent bots somewhere involved with NLP, i.e. Natural Language Processing. There are some applications like Medscape, UpToDate, Lexicomp, which practitioners use for accessing knowledge about a wider variety of medications (or infectious diseases) for prescribing to their patients, who have been linked with the healthcare industry for a longer span, in a better way. All this DS assistance has helped the practitioners, doctors bookmark the anomalies in the diseases which patients have been going through without complaining to anyone about the same.    

4. DS is Prevalent in Manufacturing Too

Manufacturing and its core processes primarily focus on identifying ways that may transform raw materials into a product. Such a product may either be related to households, aircraft, automobiles, or sports. One can’t deny the fact that all such products have their historical data which needs to be maintained and reviewed every year through inventories. Here, Data Scientists (highly specialized in classifying and analyzing large sets of structured or unstructured data) apply reinforcement learning techniques for predicting manufacturing-related problems so that the manufacturers may monitor the products’ costs and also, optimize the production hours. This is really helpful as the production unit can now achieve the pre-defined goals while working in a potentially complex environment. Even a continuous improvement, in manufacturing processes, can be spotted in real-time as the autonomous reinforcement systems are there to improve the quality of manufacturing products and boost the positivity rate in customer reviews processed for such prevalent manufacturing lines.  

5. DS is Another Framework for Customer Data Management

Customer Data Management demands smooth processing of the analysis of the structured or unstructured data of the customers connected with the organizations for a longer span. This is because customer experiences are channel-agnostic and comprise a multitude of opportunities from a business perspective. With the help of customer segmentation, companies may get deeper insights into the gender, ethnicity, lifestyle, education level, and personality characteristics (behavioral or non-behavioral) of their customers and then, apply the procedures of data analysis for predicting how relevantly those segmented customers are communicating with their business units? The benefit is that cross and up-selling opportunities through credible marketing resources are maximized, thereby encouraging the segmented customers to buy more products. As a result, touchpoints like sales and customers’ accounts are sorted well as per the purchase history and now, it will be much easier for the organizations to identify the valued and appreciated customers, not neglecting loyalty and empathy in their buying patterns.  

6. DS is Shaping the Risk Analysis Well

Risk is associated with every business and therefore, it becomes essential for companies to crucially analyze risk areas before any decision is taken within their production units. This will be reaping fruits as customers will be nodding their heads towards the security and trustworthiness of an organization that offers services a customer may demand. Through analytical tools of Data Science, experts working in a specific department will first identify the factors generating risks like cost, technology, time, resources, or communication. Later, they will examine the impact of those risks on the existing project and rank that in accordance with its significance on a reasoned basis. After all, this is done, an appropriate risk response plan will be prepared that may terminate or transfer the risk. When the plan is executed and risks are detected as per their response plan, the trend and significance of risk may be reassessed so that its consequences may be monitored and controlled with appropriate measures. All such analysis lets companies validate the creditworthiness of their customer base, thereby keeping them ahead of their competitors at optimized costs.

7. DS is Focusing on Targeted Advertising

Targeted Advertising is an online method in which an organization can determine the interests and preferences of their customers, which may vary in age group and income. The benefit is that companies can determine which customers are serious or creepy and later, this helps in reducing their expenses by meaningfully targeting responsive customers. Data Science here helps as the advertisers can now understand the browsing patterns of their customers (through ML applications) and identify the trends reflecting the interests of customers in a real sense. Now, the trends are identified well and organizations can now run online ads whose insights capably focus on the customers having excellent browsing and purchasing habits. Companies like HubSpot, MetaData, Acuity Ads follow this advertising technique and are successful in enhancing their goodwill and expanding their quality products in the market. On an overall basis, the customers benefit as they are getting quality products and there is no need to pay a middleman to get what they really want!!    

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