If you had the power to predict the future, how would you use it?
Gartner refers to the predictive modeling process as that which collects data, formulates a statistical model, makes predictions, and then validates the model as additional data becomes available.
In the world of intelligent automation, teams use predictive modeling to formulate how future outcomes or behaviors will be, by using historical data, machine learning (ML) algorithms, and analytical techniques.
IT teams use intelligent automation to join the forces of predictive models with those of robotic process automation (RPA), artificial intelligence (AI), and other advanced technologies to streamline and enhance business operations.
RELATED WEBINAR: Can RPA and ITPA be Friends?
What is Predictive Modeling all About?
Predictive modeling looks a bit different from an IT Process Automation (ITPA) angle, defined as the application of data-driven techniques and machine learning (ML) algorithms to anticipate future outcomes, events and constants within IT systems and processes.
Predictive modeling in demand forecasting, for example, can help answer the question: “What will the demand for products or services be like tomorrow?”
Future-focused knowledge enables businesses to optimize inventory management, production schedules, and resource allocation.
Predictive models collect and thoroughly analyze historical data, unleashing what they can do for many different industries. For instance, when it comes to maintenance work, teams usually repair what’s broken. However, knowledge gained from past data allows predictive models to anticipate when machinery or components may require servicing or replacement. In doing so, maintenance teams can get ahead of the game, reduce downtime, and minimize costs.
Financial institutions are turning to predictive modeling for help in identifying unusual transactions or patterns that might indicate phony activities, as well as evaluating the creditworthiness of potential borrowers. The institutions are then set up to respond proactively and stop losses from happening, and also make informed lending decisions and manage risk more effectively.
RELATED BLOG: Why Automating IT Incident Response Matters for Financial Institutions
7 Real-world Examples of ITPA and Predictive Modeling Working Together
The benefits of predictive modeling reach the organization as a whole, allowing it to improve decision making, optimize processes, and adapt to changes with speed and ease by anticipating events and trends. Below is a collection of real-world examples where predictive modeling validates its ability to secure a better future.
1. IT Infrastructure Capacity Planning
Using historical data analysis, predictive models can enable organizations to anticipate future demands, as well as optimize their infrastructure to meet these types of needs.
SOLUTION SPOTLIGHT: IT Operations Automation, to streamline IT resource management with automated provisioning and IT oversight.
2. Anomaly Detection in Network Traffic
Predictive models can analyze network traffic patterns, and from there, identify unusual behavior or potential threats, allowing IT teams to respond proactively and minimize the impact of security incidents.
3. Incident Management and Resolution
Efficient incident management is crucial for maintaining service quality and customer satisfaction. IT teams can use the help of predictive modeling to prioritize incidents based on factors like severity, impact, and likelihood of resolution, enabling them to allocate resources more effectively and reduce downtime.
4. Automated Root Cause Analysis
Another example of historical data analysis is that of support tickets. Patterns and correlations, as they’re identified, can actually help guide IT teams to the root of a problem. It allows for quicker resolution of issues – a major goal for any IT team.
5. Software Development and Testing
Predictive models can help organizations estimate project completion times, identify potential bottlenecks, and optimize resource allocation during software development and testing phases.
6. IT Service Desk Optimization
Once more, analyzing historical data (this time on support tickets), predictive models help IT service desks anticipate—and be ready for—the volume of incoming requests. They can then assign resources accordingly, leading to faster response times and improved customer satisfaction.
7. Cybersecurity Threat Prediction
Predictive models can learn about past security incidents and threat intelligence to anticipate potential cybersecurity risks. It sets organizations up to implement proactive countermeasures and minimize potential damage.
When integrated into both, intelligent automation processes and ITPA, predictive modeling is ideal for any organization to step up efficiency, lower costs, and drive business growth, and so it makes sense for predictive modeling to become even more prevalent and widely used.
An August 2022 study’s findings support the marketing reaching $38 billion by 2028, growing at a compound annual growth rate (CAGR) of just over 20 percent from 2022 to 2028. The global market size in 2022 was reported at $12.49 billion.
It’s no wonder more and more organizations are using tools like predictive modeling to forecast trends and behaviors – for a successful tomorrow and years to come.
Learn more about predictive modeling for your organization by requesting a demo.
