Predictive IT is a powerful new approach that uses machine learning and artificial intelligence (AI) to predict incidents before they impact customers and end users. By using AI and predictive analytics, IT organizations are able to deliver seamless customer experiences that meet changing customer behavior and business demands. Discover the critical steps required to build your IT strategy, and learn how to harness predictive analytics to reduce operational inefficiencies and improve digital experiences.
Download this executive brief from CIO to learn:
5 steps to an effective predictive IT strategy
Where AI can help, and where it can’t
How to drive revenue and exceptional customer experiences with predictive analytics
IT organizations are now responsible for delivering seamless customer experiences while preventing outages and managing an increasing number of systems. With growing responsibility placed on IT, there is an opportunity to drive strategy for company-wide business processes and operations.
Companies using machine data powered platforms like Splunk collect disparate data types to quickly troubleshoot and monitor systems. By adding predictive capabilities, IT can glean critical insights for the business and develop strategic initiatives on issues that matter.
Download the white paper “Embracing the Strategic Opportunity of IT” to learn how to:
Enable a business aware IT organization
Unlock operational efficiencies
Solve problems with predictive analytics
With tight budgets, it isn't easy to create the operational dexterity needed to thrive in a competitive marketplace. View this demo to find out how IBM® SPSS® solutions for predictive operational analytics help manage physical and virtual assets, maintain infrastructure and capital equipment, and improve the efficiency of people and processes. By using your existing business information, IBM SPSS software can help you: predict and prevent equipment failures that can lead to disruptive, costly downtime; quickly identify and resolve product quality issues to mitigate risks and reduce warranty costs; optimize product assortment planning to increase revenue, reduce working capital requirements and improve the return on inventory investments; and act to retain your best employees by developing predictive attrition models to identify the workers at greatest risk of leaving the organization.
As a claims management professional, you have to deal daily with formidable - and sometimes competing - challenges: provide superior customer experience; achieve operational excellence and cost containment; and effectively manage risk. Predictive analytics can help you improve each of these three outcomes, but more importantly, it helps strike the right balance among these three objectives for each new claim received. Read this white paper from IBM to learn about applying predictive analytics to claims management, including the typical ROI achieved, how embedded analytics improves decision making, and technology components of a predictive analytics solution.