• Sanjay Kumar

Use Cases For Data Science And Predictive Analytics

Updated: Jan 17


Data Science And Predictive Analytics - Datahod

Table of Content

  1. Web Search:

  2. Fraud and Risk Control:

  3. Consumer Interaction:

  4. Healthcare Industry:

  5. Energy Management:

  6. Quality Assurance:

  7. Wrapping Up:


The data collected and stored by organizations is increasing day by day. Tonnes of collected data are not that much important- but the critical part is how companies are effectively using them. The structured and unstructured data flowing across industries at an unpredicted rate, creating connections, and fetching valuable insights from them is the most critical challenge among organizations. Everyone has their own answer to these questions. But I’m taking a straightforward approach here. Here I will tell you about the application areas of data science in the real world. You can go through each point and decide which kind of opportunity you want to grab.

Table of Content


1). Web Search:

Whenever we talk about search, the first thing that goes through our mind is Google search. Right? Apart from Google, there are various more search engines such as Yahoo, Bing, Ask, etc. There’s one common thing about all these search engines- they all use data science algorithms to provide the best and faster search results. Google processes around twenty petabytes of information in a single day. Without data science, even Google is not sure how to accomplish such a typical task.


2). Fraud and Risk Control:

It is considered the first application of data science in the finance sector. Various financial and banking organizations were facing losses due to continuous forgery-like scenarios. As they collect information from users in case of loans, credit cards, etc., here data science rescues the organizations by organizing users’ profiles, expenditures, and other information. Thus, they can analyze data collected from these data sets and extract valuable insights to detect fraud or default scenarios.


3). Consumer Interaction:

Insurance agents can conduct regular surveys and handle claims effectively by applying data analytics to customer data. It assists them to manage their services in good ways and point out the areas for improvement. Collected insights tell them about consumer behavior such as which customer is available to communicate over the call, mail, or personally. These customer demographics and feedback practices help agents to enhance their services to close more deals. A survey concludes that the older generation prefers to talk on a phone while new generation people refer to web mediums such as social media, emails, etc., for interaction.


4). Healthcare Industry:

The healthcare industry has touched a new level since it embraced data technologies to improve its infrastructure. From predicting disease to identifying the cure, Big data and ML have created a dependent and collaborative engagement among the patient and doctors. Data science assists doctors by analyzing millions of patients’ data to present the cure of a disease. It follows the pattern recognition approach to track patient records. Thus it prevents the occurrence probability of these diseases in the future.


5). Energy Management:

The new generation firms are making use of data analytics in energy management. This way, they optimize energy, manage smart-grids, automate billing, utility, and energy distribution in efficient ways. With data-analytics enabled applications they are able to handle monitoring and controlling of the grids, crew, resources, and services. It integrated millions of data points to enhance the utility performance over their expanded network.


6). Quality Assurance:

Quality assurance is one of the major aspects to maintain the bottom line, improve user experience and operation costs as well. Data science is efficiently driving the QA in industries like automotive, manufacturing, supply chain, oil, and gas, energy, utilities, etc. An inefficient quality control decreases customer relations, buying behavior thus reducing revenue and market share. Therefore, you need to spend more on customer support, warranty or guarantee issues, costs, etc. Predictive analytics can avail useful insights to identify flaws and quality issues.

Wrapping Up:

From the above-explained scenarios, you have understood how data science is transforming traditional business operations by creating endless potential opportunities. If you are still unaware of the technology of generating insights, you can go through an online certification training course for data scientists where you will get to learn regression techniques, data exploration, testing hypotheses, and applying big data on large datasets which is the technology of future.

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