Overview

Big data analytics is the process of analyzing huge and fluctuated informational collections – collections that can reveal shrouded designs, obscure connections, advertised patterns, client inclinations, and other valuable data that can create associations to facilitate more-educated business choices. Speed and efficiency are some of the new benefits that are offered by big data. A few years ago, it was impossible to process a huge amount of data in a short span of time, but with real-time analytics, businesses can quickly and easily identify insights for immediate decisions and more effective digital business transformation. The capacity to work quicker and remain deft gives organizations an aggressive edge they didn’t have previously.

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Why Real-Time Analytics?

With real-time big data analytics, it is possible to process data as it arrives and provide insights about the data instantaneously. It helps businesses conduct more effective marketing campaigns, discover new revenue opportunities, improve customer service delivery, enable more efficient operations, and gain a competitive edge. Companies implement real-time big data analytics because they want to make more informed business decisions.

Traditional business intelligence queries answered basic questions about business operations and performance. Big data analytics, on the other hand, uses complex applications powered by high-performance analytics systems. Streaming analytics applications are also becoming common in the big data environment as organizations need real-time analytics on data fed into Hadoop systems. Of late, big data analytics has started adopting the concept of a Hadoop data lake that serves as the primary repository for incoming streams of raw data. This provides the facility to directly analyze the data in a Hadoop cluster or run it all through a processing engine like Apache Spark.

What is Spark? And Why Use It?

Spark is a broadly useful and extremely quick group figuring framework. It is the future of big data analytics. Earlier, with Hadoop MapReduce, it was conceivable to break down the colossal measure of information that was available at that point, but Spark offers a way to analyze fresh streaming data and is faster than MapReduce. Apache Spark has plainly turned into the successor to Hadoop MapReduce for investigative and machine learning tasks at hand because of Spark’s convenience and in-memory execution.

Where is Real-Time Big Data Analytics Being Utilized?

Real-time big data analytics is being used in the following industries:

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Advantage of Real-Time Analytics

An organization has the opportunity to reap numerous benefits by employing real-time big data analytics. The following are just a few of those benefits:

  • Real-time insights can help organizations correct their mistakes quickly
  • It can help organizations stay one step ahead of the competitions
  • Organizations can provide quick suggestions in real-time that can help in customer retention
  • Real-time fraud detection can help in avoiding huge loses
  • Even though the initial cost of a real-time system is high, it will prove to be a substantial cost-saving in long run

Conclusion

The real challenge with real-time big data analytics is coming up with the right combination of technologies and to integrate them to build a complete and effective system that is fast and gives accurate results. There are many solutions available for this problem, which have proved themselves effective and reliable. Mastech InfoTrellis – the Data and Analytics business unit of Mastech Digital – is at the forefront of making these solutions a reality for leading organizations.

Start running on real-time today. Start running with Mastech InfoTrellis.

 

Garima_Jain
Garima Jain
Technical Consultant, Mastech InfoTrellis



Prescriptive analytics is a branch of data analytics that uses prescient (predictive) models to propose actions for ideal results. Prescriptive analytics is associated with both prescient and descriptive analytics. While prescient analytics figures what might happen, and descriptive analytics provides data into what has happened, prescriptive analytics helps provide the best results among different choices, given the known parameters. Prescriptive analytics stretches out past prescient analytics by indicating both the activities important to accomplish anticipated results, and the interrelated impacts of every choice.

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Prescriptive analytics endeavors to evaluate the impact of future choices so as to exhort conceivable results before the choices are actually made. Prescriptive analytics predicts what will happen, as well as why something will happen, giving recommendations with respect to actions that will make a favorable position of the predictions.

Prescriptive analytics utilizes recent advancements such as machine learning and artificial intelligence to comprehend what the effect is of future choices and utilizes those situations to determine the best results. Advancements in the computing speed and the development of complex mathematical algorithms connected to informational indexes have made prescriptive analysis conceivable. Specific procedures used in prescriptive analytics include streamlining, simulation, game theory, and decision-analysis methods.

Application of Prescriptive Analytics

Prescriptive analytics is utilized in situations where there are an excessive number of choices, factors, requirements, and information for the human mind to proficiently assess without technology assistance. It is likewise utilized when testing in the real world would be prohibitively costly, excessively hazardous, severely time-consuming.

In a perfect state, any business issue would experience every one of these stages (Descriptive, Predictive, and Prescriptive) to ascertain a solution. In any case, there are certain business issues that absolutely require prescriptive analytics, such as:

  • Supply Chain Optimization
  • Logistics is the biggest consumer of this form of analytics. It utilizes network flow modeling to resolve transshipment issues, shortest path issues, maximal flow issues, transportation/assignment problems, and generalized network flow problems. It involves minimizing the cost of risk while getting from source to destination.
  • Operations Management
  • It uses linear and, more often, nonlinear programming to outline and control the process of generation and redesigning business activities in the production of goods or services. It includes minimizing throughput or maximum output of the whole task/process.
  • Inventory Management
  • Here, it’s all about setting generation levels and stock levels to meet forecasted demand at sales locations. How much should each plant supply to each distribution center? Which stockrooms should serve which sales locations? The list goes on.
  • Price Optimization
  • Organizations can be confident in making pricing decisions because prescriptive analytics helps organizations identify and understand patterns and insights.

Benefits

The benefits of including prescriptive analytics as part of your analytics hub can be outlined as follows:

  • Anticipates what will happen, when something will happen, and why it will happen
  • Suggests choice alternatives to exploit a future opportunity
  • Mitigates a future hazard
  • Ingests new data to re-predict and re-recommend, thus automatically improving forecast precision and endorsing better decision options without compromising other priorities
  • Automatically and reliably processes up-to-date data to improve the accuracy and efficiency of predictions, and to give better decision choices
  • Predicts various prospects and enables organizations to evaluate various conceivable results in light of their activities

Conclusion

Prescriptive analytics is the prospective of Big Data, but it is as yet far away before it will be common language. The potential is colossal; however, it requires gigantic measures of information to be able to make correct decisions. Only a handful of organizations and industries have that measure of information and informational indexes to make something useful out of prescriptive analytics. However, in 5-10 years, prescriptive analytics will be as typical as business intelligence today. Gartner gauges the prescriptive analytics software market will reach $1.1 billion by 2019.

Mastech InfoTrellis – a pioneering data and analytics company – is a leader in the implementation and use of advanced analytics applications across a wide array of industries.

Get in touch today to learn more about how you can take advantage of this new wave of analytics and gain actionable insights through a relevant, powerful analytics hub. Improve your organization’s decision-making capabilities today.

 

Garima_Jain
Garima Jain
Technical Consultant, Mastech InfoTrellis