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Where Is Data Analytics Used? A Complete Guide

Data analysis is a process of collecting, organizing, and analyzing raw data to draw conclusions or to find patterns and trends in a specific problem. It includes a number of techniques varying in different industries, which can be helpful in predicting the future trend scale or requirement of the company from the previously recorded data. If you want to learn where is data analytics used? Then check here the top 6 uses of data analysis. 


For Example –

In an educational institute, one can use data analytics to find which courses are in demand and which are the courses that can be added to the curriculum.

In a cosmetic company, one can use data analysis to find which one of their products are best sellers and what are the current market trends.

Let Us Learn About Where is Data Analytics Used in More Detail –

Online data is available about anything and everything in today’s world. To interpret this data, we require Data analysis. Data analysis can be used in all different kinds of industries – manufacturing industry, service industry, etc.

Before diving into where is data analysis used, let us learn about the types of data analysis.

Types of Data Analysis

There Are Majorly Four Different Types of Data Analysis–

  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics

Descriptive Analytics:

Descriptive Analysis is the base of data analysis, on which the other types are built. In Descriptive Analytics, you go back in your track records of the previous years and collect that data.

With the help of this, you can analyze the past trends and the patterns that are being repeated in the aspect of sales or complaints or anything that adds value to the success of the company, and suggest better techniques to improve their brand or sales, etc.

Descriptive Analytics includes common tools that are used in an organization daily i.e. Microsoft Excel or data visualization tools like Google Charts.

Diagnostic Analytics:

Diagnostic Analytics can be considered as the next step after Descriptive Analytics. The reason is, that in Diagnostic Analytics, you try to find the root cause of the problem.

In other words, once you find the patterns or the trends followed in an organization using descriptive analytics; then you can use Diagnostic Analytics to figure out why this particular pattern or trend is being followed.

Diagnostic Analytics can help give greater insights into the data of the organization than other types. It can also be helpful to create or test different hypotheses based on the data that is collected. This can also help an organization avoid the same mistakes again in the future.

Diagnostic Analytics can be calculated manually with the use of an algorithm or using tools like Microsoft Excel.

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Predictive Analytics:

Predictive Analytics is one of the advanced forms of data analysis. Here, you try to predict what could be the future possibilities for an organization.

In this, you try to figure out the potential scenarios based on the data available from the previous years, on what could be the future trends or sales or growth, etc. seen in the company. It helps evaluate if the previous pattern may repeat in the future or if there may be some change.

Predictive Analytics Can Be Beneficial in Several Ways Such as –

  • Improve production efficiency – In the manufacturing industry, this method can be quite beneficial, because it will help you predict possible future sales, help set goals, and keep track of the inventory accordingly.
  • Risk reduction – With predictive analytics, as you have the data records of the past, you can identify the anomalies and prevent making the same mistake in the future; and form strategies to improve your business.
  • Meet consumer expectations – Based on the previous record of the data, the sales record, and the social media; you can identify what the consumers are demanding in the sector and make strategies to meet maximum consumer expectations.

Predictive Analytics, where is data analysis used, requires advanced machinery tools to calculate such analysis. Some examples of models used are – classification models, clustering models, time series models, etc.

Prescriptive Analytics:

Prescriptive Analytics is a result of the combination of descriptive and predictive analytics. In Prescriptive Analytics you take action on the suggested strategies and ideas that are developed with help of the predictive analytics.

In prescriptive analytics, there are larger data sets, and hence, you require advanced tools like machine learning and artificial intelligence (AI).

Prescriptive analytics can be beneficial in integrating various possible outcomes of different strategies and help choose the best plan of action for the organization.

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Where is Data Analysis Used?

Now we will be looking into various sectors, where data analysis is used to help an organization or a company help improve their scale of success. We will be learning about each sector in detail. Let’s dive into where data analysis is used and how the different types of data analysis play a role in them.


1. Education:

In the past decade, the traditional ways of learning and teaching have evolved in more ways than one. Hybrid learning is often used nowadays, and this has improved the learning process for students and also enhanced the performances of the teachers or professors.

Here is the List of Few of the Applications, where is Data Analysis Used in Education –

  • With the help of data analysis, universities can analyze their student’s performance in one or more courses individually. This will allow them to bifurcate where the student is falling behind and where they excel. This data can also be evaluated to find in which courses the students perform better and in which courses they don’t. Accordingly, the universities can identify in which areas they need to improve.
  • Data analysis can help in making personalized learning systems. For instance, universities can develop personalized learning plans for students who fall behind in certain subjects; this data can be made available by tracking the records of the students each semester with the help of descriptive analytics.
  • By learning student behavior using data analysis, universities can also plan on bringing in new courses as per the student’s interests or changing their current curriculum as the requirements in the market change.


  • With the help of data analysis, universities can also improve their teaching methods, by analyzing the areas of improvement; and then building professional development opportunities and programs for the faculty.
  • With the help of predictive models, based on the recorded data of the previous years of the students like the attendance records or the scores in previous semesters, universities can then evaluate the future performance of the students, their graduation rates, academic success, etc.
  • Data analysis can be used to track the performance of the faculty members, and this data can help the university decide on the promotion rates and bonus salary rates for the professors and teachers.
  • Universities have data records that may need high security. This kind of data can be kept safe with the help of data analysis, and all the student’s records which may include their private details and other documents can be kept confidential without any worry of piracy.


2. Healthcare:

With the help of data analysis, healthcare providers will be able to give patients better care and advice about their illness or any other problem.

Here is the List of a Few Applications, where is Data Analysis Used in Healthcare –

  • Hospitals can use data analysis to keep track record of the patient’s medical records. This will save their time and will make it easier to keep track of the patient’s recovery.
  • The availability of operating rooms can be difficult to track. With the help of data analysis, hospitals can forecast the number of operating rooms that will be required in the coming days; by calculating the patient and room ratio. Data analysis can help hospitals better schedule the operating rooms for the patients.
  • The staff and the members of the hospital are an integral part of the unit. Keeping a record of their data and schedules is equally important. Data analysis can help hospitals with the same. They can keep track of their attendance, if they are facing any issue with a particular facility etc.
  • Data analysis can help hospitals keep their data records confidential, and hence prevent any type of cyber-crimes.
  • Data analysis can also help hospitals keep a record of the supply chain for the utilities or medical instruments that are being used.
  • Medical errors caused by lack of information by the staff can affect some patient’s life. Data analysis can be used to track any such error.


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3. Marketing/ Business:

In marketing, data analysis is used to collect data from various digital sources and then use this data to make enhanced marketing strategies to improve business.

Here is the List of Few Instances, where is Data Analysis Used in Marketing –

  • With the help of data analysis, based on the large set of data acquired from social media platforms and the business website; you can gain an understanding of who is your target audience.
  • Data analysis can also help understand which one of your products or services is being sold in high proportion. This will help you strategize your service or product portfolio accordingly.
  • Using the data records from the past, data analysis can be used to understand the patterns and behavior of the consumer. This data can be used to identify how to enhance customer experience further.
  • Data analysis can be useful in understanding your competitors. You can identify their strengths and weaknesses, which in turn will help you improve and build up refined strategies to increase business.
  • Another application where is data analysis used in a business is to predict future outcomes and sales. This will help you set goals for your business and collectively make efforts to fulfill the goals.
  • Along with predicting future outcomes, data analysis can also be used to identify the obstacles that may be faced in the business. For example, if a vendor is not available in summer for the next delivery; the management can find a replacement earlier so that the sales are not affected.
  • The leakage of high confidentiality data can become one of the reasons for the business to crash. Data analysis can help with securing this data, and make sure that confidentiality is maintained.


4. Finance/ Banking:

Data analysis is extremely important in the finance sector. Each company organization or business makes millions of capital every year. Management of such revenue and cash flow is a vital process that will require you to use high technology and data models to evaluate large sets of data. This is where data analysis can be made useful.

Here is a List of Instances, where is Data Analysis  Used in Finance –

  • Data analysis can be used to make credit score profiles for the consumer. It can better analyze large sets of data found in the institution than the traditional method. Data analysis will help you identify which consumer has more credit score and present them with customized offers accordingly.
  • In the investment sector, data analysis plays an important role in making wise decisions. Data analysis helps identify market risks, and predict market trends shortly; therefore, helps guide the customer to invest accordingly.
  • Understanding customer’s financial behavior is an important aspect of the world of banking. This will help the banks identify possible risks of fraud, customize services and offers to the clients, and also help in making improvised risk management strategies.
  • Data analysis helps you be adaptable to change. In finance, there is a continuous change that takes place in stock markets interest rates, etc. With data analysis, you are up-to-date with the current finance trends in the market and this gives you a head-start to improve your strategy.
  • Data analysis models such as predictive analytics models have made the employee’s work easier. Data analysis collects and analyses data much faster than usual manual reports and charts; this gives the employees a chance to add value to other responsibilities like maintaining customer relations.

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5. Manufacturing Industry:

A manufacturing industry includes multiple complex operations. Each one of them produces large sets of data; which can include information about the raw materials, manufacturing equipment, labor, packaging materials, etc. Having a disciplined and organized record of all this data is important to have a successful transaction. This can be done using data analysis.

Here is a List of Situations, where is Data AnalysisUsed in Manufacturing Industry –

  • Manufacturing equipment needs to be taken into service at regular intervals for it to function smoothly. This cannot be unplanned as it may hinder the production process. Data analysis is used to predict when the manufacturing equipment will need service.
  • Supply chain management in the manufacturing industry is of top priority. This is what helps you schedule the delivery and production of a product. Data analysis is used to keep track of this large set of data which generally includes the availability of raw materials, packaging material, production line, etc., and helps in aligning dispatch and production dates as per the client’s requirement.
  • Data analysis can be used to maintain the warehouse operations. By tracking the production forecast, it is possible to increase the number of laborers (if required) during busy schedules.
  • Data analysis can be used to trace records from departments like quality control, production lines and stores to check if there are any defects or issues that are being repeated.
  • Data analysis can also help in identifying the threats to the quality of the product from consumer reviews.
  • Data analysis can be used to identify market trends from various social media platforms. This can help understand customer needs and what is in demand today.

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6. Transportation:

The transport industry has gone down in recent years due to the pandemic. Getting back on track has been a journey that took effort and time. How do you think analytics can be made useful in transportation systems? Let’s find out!

Here is a List of a Few Instances, where is Data Analysis Used in Transport Systems –

  • Data analysis can be used to understand travel patterns i.e. which is the most commonly used route by people, how they travel, and their destinations. All of this can be recorded and analyzed to increase efficiency in transportation.
  • Data analysis can make use of real-time data provided by the traffic systems and cameras to keep track of and maintain the traffic, which will help prevent accidents and also reduce delays in deliveries.
  • Based on the records of population growth, travel demands, and development in urban areas, predictive analytics can be used to identify transport challenges that may be faced by people soon.
  • Predictive analytics can be used to foresee what impact the development projects will have on the system.
  • Data analysis can be used to make shipment schedules by analyzing weather reports. It can also be used to track when the ship equipment will need to be changed.

Now that we’ve discussed the major sectors, where is data analysis used the most according to you? And what other applications are possible in data analysis?


Career Opportunities in Data Analysis

Now that we have learned about the basics and where is data analysis used, if you found any of it interesting, you can pursue a career as a data analyst!

Data analysis is a vast ocean of possibilities and there are many roles that you may find suitable for you.

With a growing number of businesses, large sets of data are also being produced all over the country. This increases the demand for a data analyst. To be a data analyst, a basic understanding of the statistical approach, mathematical inclination, and business learning are some of the qualities you will require.

As we saw previously, in today’s world, data analysis is not only used in the IT or E-commerce industry but is widespread in every sector possible such as the manufacturing industry, healthcare, marketing, finance, and education. This opens a wide range of opportunities for you to choose one path.

The average salary in data analysis ranges between ₹2 lakhs to ₹10 lakhs. Companies like Google, Accenture, Amazon, Flipkart, Citi Bank, etc. are top recruiters.


FAQs –

Q. What is the difference between data analysis and data science?

Data analysis and data science are closely related and can be misinterpreted easily. While data science focuses on building different models using the available data to foresee an outcome; data analysis focuses on past data and analyzing the same to make improved decisions. Data analytics can be considered as a part of data science.

Q. Will Artificial Intelligence (AI) replace data analysis?

No, AI cannot fully replace data analysis. AI can increase the efficiency of analyzing the data and help in better outcomes. Even though there have been advances in A.I. and there will be more in the future; the demand for a data analyst is predicted to increase by 36% by 2031. So, sit tight without any worry! Your place won’t be taken by a robot!

Q. Is coding required in DA?

Yes, coding is required when you want to learn data analysis. However, it does not demand you to have higher programming skills. You require a basic understanding of Python and R. Other than that SQL is also important in data analysis. And based on the industry you desire to join as a career, they may have their requirements.

Q. Where is data analysis used in daily life?

Data analysis is used in daily life, and often we don’t realize it. For example, we use data analysis to manage our time and sort our activities throughout the day. We also use data analysis to maintain our monthly budget. In this way, data analysis  is used in multiple forms in daily life too!


Data analysis has a wide ocean of applications in every sector possible. In this article, we learned where is data analysis used in Healthcare Finance to Manufacturing. All these industries work with different rules, but one thing they all have in common is they all produce large sets of data (big data). Data analysis helps sort and analyze this data and then further helps make improved decisions, which could be with regards to a patient’s file a customer’s banking records, or a manufacturing equipment’s shipment records. Data analysis hence plays an important role in the success and growth of an institution.

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