Real-time analytics has become the most crucial term in Big data analytics for enterprises. This enables enterprises to use all available data as real-time analytics big data. This means with real-time analytics enterprises can generate analytics reports as and when the data is received. It ideally takes a minute. Furthermore, using real-time analytics, enterprises can receive fresh and contextual analytics reports. This gives close relevance to market trends. Real-time analysis happens through continuous querying. Streaming analytics or Real-time analytics enables applications to integrate with external data sources to application flow. Otherwise, it updates an external database with already processed information. This in other term is known as stream processing.

In Real-time analytics, while the stream of data moves continuously, it calculates statistical analytics on the live streaming data. Thus, it allows the monitoring and management of live streaming data. So, the business can at upon events happening at any given moment before the data loses its value.

Why is Real-time Analytics Important?

Real-time analytics allows organizations to analyze data as soon as it becomes available. Hence, it allows for analyzing risks before they occur. So, the business can find new opportunities easily which may result in an increase in profits, improved customer service and new customer ventures. A Streaming Analytics or real-time analytics platform can process millions of events per second. "Because data in a Streaming Analytics environment is processed before it lands in a database, the technology supports much faster decision making than possible with traditional data analytics technologies," Philip Howard of Bloor Research said in a recent Datamation interview.  Since using real-time analytics companies can detect different security threat patterns and risks, it helps in security protection and monitoring of physical as well network.

Types of Real-Time Data Analytics

There are two types of real-time analytics:

• On-demand real-time analytics — This is a reactive analysis approach where the user processes a request through query and then delivers the result as analytics. For example, web site analytics is a kind of on-demand real-time analytics where an analyst monitors site traffic to resist a potential crash of the website.
• Continuous real-time analytics — This is a proactive analysis approach where users are continuously updated with alerts in real-time. For example, stock market tracking with various visualization representations is this type of analytics.

What's so realabout Real-Time Analytics?

Real-time means at the very moment. Hence, real-time analytics is capable to process data at the moment it arrives in the system. So, there is no possibility of batch processing or future processing of data. Not to mention, it enhances the ability to make better decision making and performing meaningful action on a timely basis. So, real-time analytics combines and analyzes data at the right place and at the right time. Thus, it generates value from disparate data.

Advantages of Streaming Analytics

• Data visualization on a real-time basis provides Deeper Insight: 

To make a key performance on a daily basis, KPI or key performance indicator plays a vital role for companies. And Visualization is a key ingredient for KPIs. As the companies can view KPI data on a real-time basis, they can get the granular view of business data at any given point of time. This data can improve sales, identify errors, reduce costs, and provide information to react faster to risks to mitigate them. Real-time Analytics accelerates decision-making along with providing access to business metrics and reporting.

• Customer Behaviour insights: 

As real-time analytics provide real-time insights on customer data like what they are buying, their preferences, likes, and dislikes, it gives companies to retain customers as well as generate extra profits. Additionally, companies can rapidly respond to customer needs which helps in increasing revenues through cross-selling and up-selling of services and goods.

• Remain Competitive:

Real-time analytics helps to become companies more innovative and remain them competitive by strengthening the band. With real-time visualization reports it is easy to identify trends, develop use cases, white papers, and generate forecasts. This not only reduces internal and external threats but also provides advance views on industry changes.

Disadvantages of Streaming Analytics

• Lack of Experts: Though streaming analytics is a happening field, there is a lack of availability of experts in the field. The main reason behind it is the small number of Data Scientists. Since real-time analytics is still a recent technology and it shows a slow adoption by most developers due to their lack of expertise. "The streaming application programming model is unfamiliar to most application developers," wrote Forrester analysts Mike Gualtieri and Rowan Curran in a Q3 2014 Forrester report on Big Data and Streaming Analytics.

• Perform Risk Analysis: One of the main features of Streaming analytics is it shows the analyzed result of the latest industry and media news. This helps companies to keep updated on the latest development amidst high competition. Along with that, since with real-time analytics data on vendors and customers are now in hand, it helps to take action against specific risks or events.
• Securing Data by threat analysis: WithStreaming Analytics companies now can identify internal and external threats that may affect the company or industry. Identifying sensitive information that is not protected is at fingertips now with streaming data analytics. So, whether it is federal, state or regulatory information, protecting them is easy with streaming data analytics.

Conclusion

This is a real-time society and to tap into the power of data, real-time analytics is a powerful tool. Today data is considered not as valuable but also as a commodity. Nowadays, the need of the companies is to expect immediate access to the information they are seeking. This information while experimented with applications brings new insights which allow them to make decisions on the next action items with the data.

Author – Moumita M Chakraborty