Business growth is always based on data. Data plays a very important role in analytics if you want to make the right decision. The modern business market is a data-driven environment. The biggest advantage of data-driven is to empower business leaders to make a reasonable decision based on the facts, a competitive figure in the latest trends in the market.

By using data effectively, a company is able to streamline the process of acquiring a product and putting it in the customer’s hands. In this modern generation, real-time analytics provides an efficient way to analyze data in real time. Let us look in detail.

What Is Real-Time Analytics?

Real-time allows users to analyze data and related resources as soon as data enters the system. Real-time analytics applies algorithms and logics to data to provide insights to make better decisions quickly.

This type of analysis gives you the ability to access your data with near-zero latency between data ingestion and processing. Real-time data processing involves continuous input, processing, and output of data, so organizations can access their data, gain insights, and act quickly.

How Do Real Time Analytics Work?

Let’s discuss the working of real-time analytics. Working consists of 4 broad steps for how data is visualized in real-time.

Captured the streaming data

It is a process in which live streaming data is captured using tools such as callers, listeners and stored securely in a database for further analysis. The database is usually like a no SQL database such as MongoDB, Apache Cassandra, Monet DB or Hadoop’s Hive. The purpose of using a No SQL database is to provide a mechanism for the storage and retrieval of data that is modeled in ways other than the tabular relationships used in relational databases.

Data processed to data stream

The next steps are to pass the data to the data steam, with different algorithms applied to the data to partition, merge, compute and link the data with external data sources.

Data is ready by the visualization component

After implementing all the techniques and algorithms the data visualization is now ready by component. The processed data is stored in a structured format of JSON or XML in a NO SQL database. From here the visualization is prepared by the visualization component, but in some cases, the internal embedded charting library in the BI system helps in visualization, like Tableau.

Data in real-time dashboard

Here the visualization component reads data from a structured file JSON or XML and based on this data it creates user interface reporting such as charts, gauges, and various other visualizations. In some applications, like in stock trading applications, there are pre-defined rules that turn on the basis of streaming data, with the chart being rendered.

What are the uses of real-time analytics in real world?

For the post so far, it is clear that real-time analytics plays important role in modern enterprise. But how it works in real-time examples generally when we talk about huge amount of data to use.

Optimizing Business processes:  Most companies understand the importance of consistent, efficient and scalable business processes. Optimizing business processes is a way for companies to eliminate inefficiencies and reduce costs – a strategic approach that describes how an organization conducts business and manages operations for better results and increased profits. When IoT and real-time analytics and business analysts combine, a very large set of data can suddenly produce very specific information – insights that can then be applied across the enterprise to achieve greater efficiencies.

Optimizing device performance: When it comes to combining real-time analytics and machine learning to optimize device performance, big data analytics can improve device performance every year. With real-time analytics, these machines can quickly put the collected data to good use.

Optimizing customer experience: Using big data, companies can create forecasting models of demand, pricing, trends, and more. In a real-time, company can easily identify the current trends in the market and can easily assemble customer requirements. On this basis the company may be able to deliver the exact product what the customer needs.

Big data to optimize finances: Big data can be plugged into a predictive model and program in real time, giving information on decisions on everything from budget to investment.

The above use cases are useful for many industries including:

• Finance
• Retail
• Manufacturing
• Healthcare

Benefits of Using Real Time Analytics

There are many advantages and benefits of using real-time analytics in industries. Let us discuss in detail:

Monitor customer behaviour

Real-time analytics can help you gain a competitive advantage as it allows users to know the customer’s desire and the latest trends in the market. Real-time is also suitable for finding out why customers dislike products or services and helps industries to work better in that area.

Make decisions based on timely data

Real-time analytics allows user to take absolute decision on the basis of updated data.

React without delay

Accelerated changes occur at any time in the industry, such as equipment malfunctions, market changes, or consumer changes. Real-time analytics has the ability to handle sudden changes in the company and allows the end-user to understand situations, so users can react quickly to pressure situations and lose revenue, serious losses, or brand decline.

Data on mobile devices

All real-time information management programs have the potential to be used on mobile devices. Therefore, it is possible to be in a meeting on a trip and keep real and up-to-date information about the state of the business. This translates to a reduction in the time required to make decisions or resolve situations because you do not have to go to the office before you act.

Areas using real time analytics?

Healthcare Sector:

Realtime in healthcare sector provides ability to monitor patient result, patient safety, clinical risk and as well as reduce patient readmission. This overall facility helps to improve organizational efficiency and the patient experience.

Retail and customer services:

Realtime in the retail industry can provide high volume sales to retailers, identify trend insights, improve customer satisfaction. Predictive models can help increase staff retention and reduce recruitment overheads.

Manufacturing and supply chain:

Realtime in the manufacturing industry can help reduce manufacturing costs, reduce labor costs, improve production efficiency. Supply chain for raw material and spare parts can be optimized.

Transportation:

Real-time analytics at the transportation company helps track vehicle engine failure, reduces the risk of making repairs on the road, helps optimize route costs. Traffic data can also be fed into a centralized dashboard, or returned to vehicles for driver action.

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