By 2026, the global predictive analytics market is expected to get to a value of about $5.7 Billion. Many organisations have incorporated predictive analytics in their operations since its an important technique that helps them determine risks and future business trends. 

Although predictive analytics isn't entirely new, this concept is now used across various industries, from the hospitality industry to the airline business. The aim is to boost their business operations to help achieve business differentiation. Read on for more insights.

What Is Predictive Analytics?

Predictive analytics uses statistical algorithms, artificial intelligence, data mining, and machine learning approaches to predict future outcomes by utilizing historical data. This way, you can get future insights with a great degree of accuracy and better evaluate what will most likely happen in the future.

This concept uses various analytical predictions and techniques to enable company management to make future predictions. By successfully utilizing predictive analytics, organizations can effectively interpret big data.

Benefits of Predictive Analytics

The majority of organizations in the globe have realized the immense benefits of predictive analytics leading to large-scale adoption. 

Here are some of the advantages:

Optimize Market Campaigns

When organizations use predictive analytics to optimize market campaigns, they can get new customer feedback or purchases. The technology also helps businesses to promote new opportunities. Still, predictive analytics assist enterprises in getting, nurturing, and retaining their highly valued clients.

Improve Operations

Unlike previous tools, you can now make better, more accurate, and reliable future predictions by utilizing predictive analytics. Retailers can use predictive models to configure store layouts to maximize sales, manage shipping schedules, and forecast inventory requirements. Airlines are also relying on predictive analysis to set ticket prices by analyzing past ticket trends. Still, the hospitality industry players can now bank on this technology to predict the expected number of guests to help them maximize revenue and occupancy.

Detect Fraud and Cyberattacks

You can also use predictive analytics to detect and stop criminal acts before any severe damages to your system. The technology gives you the ability to focus on user behaviors. This way, a company can easily assess any suspicious activities such as impending cyberattacks. As a result, predictive analysis is an integral part of each IT team in small and big organizations to help them curb potential cyber threats. When you combine multiple analytics techniques, it improves the pattern of detection, thereby preventing criminal behavior. As cyberattacks continue to be a major threat in most enterprises, high-performance behavior analytics helps assess all actions in a network in real-time. 

Reducing Risk

When evaluating a buyer's likelihood for default when making a purchase, credit scores are used as the basis of assessment. Credit score relies on predictive analytics to generate the numbers that incorporate all the relevant data to a client's creditworthiness. The technology is also used in risk-related uses such as collections and insurance claims.

Predictive Analytics in ITSI

Predictive analytics assists in predicting the health score value of a particular selected service in IT service intelligence. By using KPI data and historical service health score, the model helps you to approximate or predict what a service's health will most likely look like 30 minutes before they happen.

With ITSI, you will get visualization tools that will guide you through the entire procedure of creating machine learning models. Even better, you will not have to go through complex technological processes or machine learning algorithms.

When your systems are down, it's impossible to complete customer transactions, leading to the loss of millions of dollars. Fortunately, implementing predictive analytics helps to identify and fix issues such as outages before they happen. When you receive an alert that your service will likely degrade in half an hour, you can take the necessary steps to fix the issue before it affects other areas. The ability to predict failures saves you money and time while also helping you create a more resilient IT infrastructure.

ITSI avails two visualizations to helps you determine whether your service is a good fit for predictive modeling. You can do a trial with different periods to help you determine which one most likely represents your data. The focus is to provide data that reflects what happens in your organization's scenario.

Predictive Analytics in DevOps

When you adopt predictive analytics in DevOps, it crucial in improving customer satisfaction and boosting efficiency. This technology is mainly used in DevOps initiatives to increase the application delivery capabilities in performance, security, tracking, and quality. For instance, if a testing tool in DevOps detects a new error, it alerts the QA team who accelerates bug-fix via a deep learning algorithm. This way, they improve application quality and test efficiency. Therefore, DevOps engineers can benefit by addressing potential issues like extended build time, unnecessary time-consuming tasks, reduced release speed, and other bottlenecks.

Synthetic Monitoring & Predictive Analytics

Direct monitoring or synthetic monitoring is a method for tracking business application performance through simulation of a path via the application. Creating behavior scripts helps simulate an action or route that an end-user or client would take on an application or software. The paths are then monitored continuously for performance such as availability, response time measures, and functionality. The three aspects that have increased interest in synthetic monitoring include the increased value of transactions on apps, customer focus on digital enterprises, and an increased number of apps.

Also, modern businesses are customer-focused and software-driven. Therefore, predictive analytics is key since manual monitoring is no longer effective with increased applications and changing business environments. Any technical problem or insufficient future prediction can lead to huge losses. For instance, Amazon lost about $72 million due to an hour-long downtime in 2018.

There are various ways in which IT teams and business operations can use predictive analytics. As an IT leader in your organization, applications embedded with predictive analytics will benefit your infrastructure and the overall business objectives. You can use 2 Steps, a Splunk verified application that delivers synthetic monitoring capabilities to Splunk users. Contact us today to learn more. 


Let's get started.

Sign up to find out more.