What is Data Science? | Uncovering The Explicit Truth

Data Science Become a Major Player in 2022: Know-How.

Technology keeps revolutionizing as it gets better and better every day. Many unique and customized models are developed according to the business needs as well as operations. The old business model is outdated, and customers have changed how they buy products or interact with the organization during the sales process. This has created a new study on re-calibrating business processes for better customer service at a low price.

What is Data Science?

Data science is the field of study combining programming, domain expertise, skills, and knowledge of mathematics and statistics to derive meaningful insights from data. A person who practices data science applies Machine Learning algorithms to images, texts, video, and audio to analyze the same as a human but beyond the human-bound accuracy level. In return, such analysis gives out precise and future-proof insights that analysts and managers can later transform into a business value.

With the help of data science, information is converted into business strategies generating more revenue from the businesses.

Data Science is now leading all the industries. We can say each successful business entity is the outcome of data science.

What Exactly is Data Science? The Perspective From Different Industries

1. Data Science in Tele-communications

In the telecommunication industry, data science is nothing but a 'promise of Big data. 'Yes, the telecommunications business is the largest source of data on a daily basis. Due to the same reasons, the expense of data storage and management used to be huge in this industry. Data Science has proven an extensive blessing to the telecom industries. Not only that, but the service outcomes are also touching the peak of excellence only because of data science and AI innovation. In this industry, data science means the following:

1. A promise of amazingly individualized services-

Data Science allows companies to offer customized services or products at each procedure of service delivery. Businesses can personalize messages that would appear on the desired channels. (ex: call center, in-store, mobile) with hitting the right areas, right images, and words.

Efforts like these do not stop at any scale. Big data helps telecom companies to learn customer experience thoroughly, from the first vendor communication to post-purchase behavior.

When these are combined with different Key Performance Index Values, the data analysis can help in

• Reveal cross-channel data

• Analyze a subscriber's lifetime value

• Create ideas to improve brand

• Reduce customer churn

2. A scope of unlimited network optimization-

An unlimited optimization of the network makes customers happier. It boosts efficiency to gain maximum revenue. Telecom companies also have the feature to combine performance networks with internal data, i.e marketing strategies, external data, seasonal trends, redirect resources, capital investment for hotspot networks.

The cost of these services by telecom companies can go up when it is not utilized, reaches the maximum level, or are overtaxed. In previous years, telecom industries have solved this issue by adding caps on data and developing tier pricing models. In the near future, the utilization of predictive analytics and real-time data can help companies to analyze consumer behavior and create personalized network service policies. This analysis can also help in damage control, for example

-If the server is down, then they can know the effects, know the location that got affected, and can directly imply a solution to the issue.

-If a consumer immediately exits a shopping cart, customer relationship management can talk to consumers to solve issues by call, text, or mail.

Many telecom companies choose to outsource this task to different vendors in the industry.

3. The magic key to churning prognosis-

Getting new clients is much more expensive than keeping old ones. Customer churn is not only the loss of golden customers but also drops the reliability of service towards prospective customers. High prices, poor service, bad connections, new rivals, and old technologies cause churn.

Predictive analytics can help telecom data scientists prevent churns to:

• Forecast the likelihood of change, and combine information (e.g., calls made, minutes utilized, number of texts sent, average bill amount).

• Know whether a customer goes to a competitor's website, switches SIM cards, or switches devices.

• Use social media sentiment analysis to spot shifts in public opinion.

• Personalize promotions for specific client categories based on previous behavior.

• As soon as a change is detected, act to retain customers.

2. Data Science in Healthcare

Data science is like 'God' in healthcare. Humanity has been fighting life-threatening diseases, including cancer, for decades. However, AI, data science, and ML have made the most ever developments. Carcinogenic cell proliferation can now be predicted. Data science and AI in clinical and healthcare industries:

1. A serious health-issue preventive wearable device-

The data that a human body generates on a daily basis is two terabytes. Collecting this data has become easy by advancements in technology. These data include heart rate, sugar, sleep patterns, stress levels, and even activities in our brains. With so much health data, data scientists are going beyond the boundaries of monitoring health.

Machine Learning algorithms are used to detect and track common conditions like heart or respiratory disease. Technology can identify the tiniest changes in a patient's health indicators and predict possible problems by collecting and analyzing heart rate and breathing patterns. While 600,000 people in the United States suffer from sudden heart attacks each year, the ability to predict the problem and give out timely alerts could save thousands of lives.

2. Bringing precision medicine to perfection-

Doctors can track the clinical course of patients with verified diagnosis in the same manner that scientists collect and analyze health data to uncover symptoms and identify diseases. Technology-enabled personalized treatment and informed care can drastically cut death rates and predict medical outcomes.

As a result, experts predict that "one size fits all" treatments will be phased out. Instead, precision medicine will be prescribed and, therefore, more successful treatment. Instead of treating a patient for lung cancer, we will soon be able to characterize every single symptom of the disease, the patient's individual condition, his medical history, and even his genetic information in order to adapt treatment and maximize the likelihood of a positive outcome.

3. A prospect of eradicating prescription errors-

MedAware, another cutting-edge firm, attempted to eliminate prescription errors. The company says that its solutions can save hospitals up to $5.6 million in addition to lowering the likelihood of fatal consequences. MedAware's self-learning software system compares all prescriptions to similar cases in the database and alerts the clinician when the prescription deviates from the standard treatment plan.

If 99 percent of patients with the same symptoms are treated with a certain amount of drug, A prescription, prescribing a different medicine, or modifying the dose will cause a system alert, questioning if the doctor is sure about his or her prescription. As a result, such an approach has the potential to save hundreds of lives while also reducing costs associated with avoidable readmissions or extended hospital stays.

3. Data Science in Banking and Finance

The trending financial technology domain, popularly known as 'the Fintech sector, is one of the sectors that keenly depends on top-edge technologies. Both the user and financial institutions depend on secure transactions with minute customer service communication. For example, AI and chatbots have reached a level where they can easily interact with customers that once required human communication.

So, the meaning of data science in banking and financial institutions is something like these:

• Automated and early-stage financial fraud detection
• Microservice architecture
• Deep learning assisted credit score analysis and prospective customer identification
• ML-assisted blockchain technology implementation for edge-to-edge maintenance of centralized banking services.
• Sentiment analysis powered automated recommendation engines building
• Highly secured mobile banking platform

4. Data Science in E-commerce

Today without any doubt, organizations, whether big or small, are integrating data science and its applications within their business system. The importance of data in the present world scenario has reached a new bookmark that organizations are making decisions only after a complete analysis of relevant data.

This is specifically found in the e-commerce and retail industry.

E-commerce giants can predict profits, losses, and purchases and even influence their customers to buy additional products based on their buying patterns. Organizations have started to buy data to create a psychological portrait of a consumer of products. This helps them to drive customer loyalty and hence increase revenues. Data analytics in the e-commerce industry. is becoming an integral part even for the basic level of decision making.

1. The data science applications in which the e-commerce giants work are

• Market Basket Analysis

• Recommendation engines

• On-time and automated price Optimization

• Warranty Analytics

• Location of new stores

• Inventory Management

• Customer sentiment analysis

5. Data Science in Sales and Marketing

There are a lot of high-value data industries that just cannot let go of it. They are real data and can be used to implement new strategies by leveraging these data. Companies today can easily collect these data from their customers, sales, operations, and marketing. So the information gained by CRM, ERP, and marketing does not directly lead to improving revenue or gaining profit.

In a study by McKinsey, , 72% of the rapid-growing B2B organizations stated that analytics helped them to drive sales compared to 50% of the other slow-growing companies. The reputation of the sales sector has become too dependent on live data tracking. Here comes the need for data science, and that can be deployed in many ways in sales as follows. Or we can say data science in sales and marketing means,

• Maximization of customer lifetime value (CLV)

• Inventory Management

• Cross-sell recommendations

• Merchandising

• Price optimization

• Chatbots

• Implementation of Augmented Reality

So, that's all about the industrial perspective of data science. Until now, I hope you have understood the fact that data science can't be explained through two-or three-line definitions anymore. Data science is not just for one industry but for all. It can be useful for organizations and even for consumers. It has opened up a new gateway for organizations and consumers to communicate and do business together. As a whole, the entire world is standing on data science.

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