In the present scenario data analytics applications are not bound to manufacturing industries. It is involved in almost every ‘if not all’ field.
In this contemporary world, big data is a game-changer for many industries and companies. Increasing internet access enables us to analyze this raw data to convert it into valuable information. Moreover, with the help of various statistical tools. The data-driven information helps the organization in decision-making.
Businesses have realized that this information they can apply to enhance the customer service. To understand the market trend. And it helps the corporations to know which area of their services they need to improve. If any company intends to attain such valuable insights. Moreover, it is essential to analyze the raw data and conclude specific information to improve the market and business aspects.
Moreover, from social media to large companies, they rely on data. Each company, institution maintains a large amount of data over the spending months and years. Almost every field now focuses on analyzing this data. And adopts data analytics to predict the demand and trend of the products and services.
Data analytics is a huge and important field to cover, and in this blog, we will dive into the best data analytics applications.
What is the meaning of Data Analytics?
It is a process to bring out valuable insights. Data analytics or analysis of data is used to find out market fluctuations. Anonymous correlation, hidden patterns, and customer demand. It assists the industries in better decision-making, detecting fraud practices, and more.
Top 10 Data Analytics Applications
Some of the fascinating data analytics applications of real life. That are being used currently in various organizations worldwide are-
Using data analytics practical applications significantly predictive analysis. It becomes possible to mitigate the crime ratio in several areas. Large numbers of cities worldwide have adopted predictive analytics to reveal the places where the crime rate could jump.
They analyze the geographical and historical data to predict the crime ratio in major cities like Chicago and Los Angeles, and London.
So it is helpful to drop the crime rates by increasing the police patrols. Although it is possible to arrest the offenders with the help of special tools or applications. It mainly focuses on attenuating the crime rather than capturing the criminals after the crime.
It brought the transportation revolution within a few years. It is beneficial for the places where you are willing to transport a huge number of people. At one particular site and need smooth transportation. Let’s have the example of the London Olympics a few years ago; there was a requirement to make approximately 18 million journeys. How do they achieve it?
They use a train operator and TFL (Transportation for London) journey planner application to fulfill their requirement. It allows using data from similar events. So that it will be easy to predict the number of people who would travel and ensure consistent transportation. Transport was very efficient and effective to bring the athletes and visitors to and from the stadiums.
3. Risk and Fraud Detection
Fraud and risk detection is considered one of the initial applications of data science taken out from finance. A large number of organizations worry about the debt they had experienced. They applied data science to the data that was collected at the time when their customers applied for loans.
This application liberated them from the losses and debt. They implemented the ‘divide and conquer’ formula on the customer’s data, expenditure, profiles. And other valuable information to predict the customer defaulting. Finally, it lessens fraud and risk.
4. Manage Risk
Risk management is the prime focus in the insurance industry. Data analytics provide the insurance companies claims data, actuarial data. And risk data information to cover the crucial decision that a company requires to take.
They use this application because many people do not know that risk involved is based not only on the information. But also on the data analyzed before decision-making while insuring a person.
Many large companies like DHL, FedEx, UPS and more are using data to enhance their efficiency in operations. Data analytics applications allow them to find out the preferred routes for shipping.
The best time to deliver, the most appropriate means of transport to choose to attain cost efficiency and more. They also use GPS on the data produced by these companies. In order to obtain the opportunities to take the benefits of data analytics.
6. Digital Advertisement
Do you know why digital advertisements are getting more CTRs (Click Through Rates) than traditional advertising techniques?
The answer to this question is data analytics. Data analytics revolutionizes digital advertising. Almost all advertising sources employ data analytics using data algorithms. These days digital advertising focuses on the analysis of the past behavior of the users for future predictions.
Data analytics is helpful to track the treatment of patients and the use of the equipment in the hospitals. It saves the hospitals from struggling with modern healthcare costs. Including the complex machinery, medicines, etc.
8. Web Provision
Many people believe that smart cities have fast internet speeds offered by companies or the government. But it is not necessary to access Facebook and YouTube at high speed to make a smart city.
This is only one aspect to declare it smart. The city should be located at a proper place and should have the assessment of the right people. It should be able to switch bandwidth at the right time and location. And all this can come true with the help of data analytics.
It is considered that commercial areas should have a higher bandwidth during weekdays. Whereas residential areas should get it on weekends. There is a complex reason behind this, and the data analytics application can solve it.
9. Customer Interactions
It is one of the insurance data analytics applications to reveal the information about their services. With the help of consistent surveys after interacting with claim handlers. They can use it to estimate which service they serve well and where they need improvement.
Considering the analysis of customer’s feedback can help insurance companies. To enhance the customer experience based on customer behavior and proven insights. In contrast, to improve customers’ experience, insurers need to provide a range of communication methods for their customers.
It is clear from the discussion of data analytics applications. That if we can understand data and analyze it, it helps increase the overall job efficiency to a higher degree. However, misuse or inefficient analysis of data can result in various problems. So it is vital for data scientists to know how to use data appropriately with the right data analytics applications.
In this blog, we have discussed the various applications of data analytics. Along with the definition of big data, data analytics, and the advantages of data analytics. I hope this blog will be helpful for you to understand the useful applications of data analytics. if you need help with excel, then contact our online excel help experts.
Frequently Asked Questions
Why is data analytics important?
Data analytics helps people and organizations make sense of raw data. Raw data is analyzed for insights and trends that help the industries or organizations in decision-making. The raw data is analyzed with the help of different tools and techniques.
What are the types of data analytics?
There are different types of data analytic such as descriptive, prescriptive, predictive and diagnostic analytics. Each type has its separate purpose based on the query a data analyst intends to solve. For instance, if a data analyst wants to know why something happened, he will use diagnostic analytics for it.
What are some big data analytics tools?
There are numerous tools used in data analytics. Some of them are-
Hadoop-It helps to store and analyze data.
Talend-It is used for business and management purposes.
MongoDB: It is useful when datasets change consistently.
Spark: Spark is used for analyzing a huge amount of data and for real-time processing.
Cassandra- used to tackle chunks of data.