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Data Science And Its Application In Various Industries

Introduction

Believe it or not – data is everywhere and data science is an inter-disciplinary field that utilizes scientific methods, algorithms and systems to obtain knowledge and insights from multiple sources of structured and unstructured data.

Data is continuously generated from various sources every day. As per Seagate’s report, the data generated by internet users all over the world will reach about 175 Zettabytes by 2025. We can collect the data, analyze it and draw a conclusion from the same which can benefit and improve organizational processes. This will help organizations to adopt new strategies and improvise their growth by taking certain measures. Finance, education, healthcare, travel, energy, manufacturing, gaming and pharmaceuticals are some of the industries where we can apply data science to improve business efficiency.

As mentioned earlier, we can utilize data science (DS) anywhere, wherever data is involved. Certain industries can adopt DS and the organizations get benefitted as they find optimal solutions that help the organization as well as its customer. Organizations using DS in their day-to-day processes observed that there was a significant increase their productivity, effectiveness and customer satisfaction with effective approach towards solving the problem. This in turn will help the industry to sustain longer and provide reliable service to their customers. Finance, education and healthcare are the most prominent industries which leverage the methods of DS. Let us discuss more on these industries.

Finance

Finance is one of the most noteworthy industries in the world which is number-driven. So, DS will be the perfect tool to help financial organizations to get more insights on their organization’s transactions and obtain the results which will lead to a sustainable development. One of the well-known finance organizations, Bank of America (BofA) spends around $3 billion buying and developing emerging technologies. BofA extensively uses Natural Language Processing (NLP) to understand the text and speech which are collected during the financial transaction (in the form of review/feedback or conversation) and Machine Learning (ML) algorithm to understand the deep insights of customer data.

Following are the key sub-domains of finance where we can apply the DS methodology and gain effective results:

Risk (Credit/Market) Analytics

Risk analytics is an important area of finance which will make the organization capable of taking strategic decisions, increasing the brand value and security enhancement. Risk analytics and management is a number crunching process and requires strong mathematical, statistical and problem solving abilities. Organizations may face various risks from their competitors, credit and market risks.

Real-time Analytics

Organizations require quick processing and analysis of results in real time. Real-time analytics provide reliable results with minimal latency of accessing the data. Time Series data plays a major role in finance and the customer will be able to enjoy the credit score and other important features of finance.

Personalized Services

Providing personalized services to customers have become an integral part of every service. Financial organizations collect all possible data provided by the customer with respect to product and services. They rely on techniques such as Speech Recognition for voice data which is in the form of conversation and NLP for text data which is in the form of customer review/feedback.

Consumer Analytics

Customer analytics is based on the transactional level of customer data. Every customer will be segmented and defined into a particular bucket which will help the organizations to provide the best and reasonable services to him/her.

Fraud Detection

Fraud detection and prevention is a major concern among the financial organizations. Most of the frauds in finance arise from credit card transactions. These kind of frauds can be detected by leveraging the technologies such as Big Data and Data Science. Anomaly detections and unusual pattern of customers in trading data can be utilized to segregate the transactions and alert the organizations to take necessary steps for preventing fraud in real time.

In finance, DS will be commonly used for risk management and analysis. Certain financial organizations use it for customer management to improve the service provided over the period of time and analyze the market trends.

Education

Education is a critical domain in the society which creates huge amount of data but it remains untouched and unutilized. Following are some of the data generated from Educational institutions:

  • Assessment data
  • Parent/guardian data
  • Student behavioral data
  • Cultural and demographical data
  • Student test data

The major issue with the huge amounts of data collected from education industry is that it is unstructured and it will change from one demographic to another. A lot of research needs to be done with respect to assessing the students, not only based on the test scores but also their overall behavior.

Segmenting the student based on their assessment can be improvised by adopting certain statistical methods. Percentile or normal distribution is one of the concepts which can be utilized in the student assessment and recruitment for particular academics.

DS can help academic institutions with the following:

Following are the promising results which can be expected if we include DS in education:

Data Science in the education sector has a long way to go, but we are hopeful that inclusion of AI and other state-of the-art methodologies will help academic institutions to provide better education and in building a better education system in the upcoming future.

Healthcare

Unfortunately, healthcare industry is not utilizing the DS extensively due to risk factors. But there are some processes which are suitable for applying DS capabilities which yield accurate and fruitful results.

Following are some of the most important fields in healthcare which can utilize DS:

Drug Discovery

  • Drug discovery is an expensive field and includes iterative tasks. Pharmaceutical companies should leverage the clinical trial data to speed up the process of drug discovery.
  • Generally drug discovery costs up to $2.6 billion and takes 12 precious years to bring a medicine into the world. In this practice, we need to work repetitively on the development of drug which should be effective towards the particular disease with no or minimal side effects.
  • Big data and data science help the pharmaceutical companies to understand and simulate the behavior of medicine/drug with the proteins and different types of cells present in the body. Owing to this simulation and understanding, pharmaceuticals have higher likelihood and probability of developing a new drug in a much shorter period of time.

Disease Prevention and Diagnosis

  • Disease prevention and diagnosis is a crucial part of healthcare industry.
  • Disease prevention includes identifying the risk and assessing them before they reach incurable state or other complications.
  • Based on historical clinical data, we can recommend the preventive measures or plans before it causes major health complications.
  • We can use wearables or other health tracking devices to monitor the health in real time and provide suggestions as per the sensor data.
  • Diagnosis is a crucial part of medical practice which will enable the doctor to decide on the subsequent steps to be taken. Large scale predictive analytics and medical imaging using DS that will enable the doctor to take the right decision.

Conclusion

Data science has a long way to go. We can use data science capabilities in various industries which involve data. It provides accurate and reliable results which help the organizations to take effective decisions that improvise their ongoing process. Artificial Intelligence is a boon, let’s utilize it and its subdomains effectively for a brighter future.

References

  1. https://en.wikipedia.org/wiki/Data_science
  2. https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf

Contact for further details

Chanabasagouda Patil
Specialist – Data Science – Analytics
chanabasagoudap.in@mouritech.com
MOURI Tech

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