Organizations are becoming more and more data-driven and are looking for ways to accelerate the usage of artificial intelligence and machine learning (AI/ML). Developing and deploying AI/ML models can be complicated in many ways, often involving different tools and services to manage these solutions from end to end. Accessing and preparing data is the most common challenge organizations face in this process, and consequently, AI/ML vendors typically incorporate tools to address this part of the process. But there are many other steps in the process as well, such as coordinating the handoff between data scientists and IT or software engineers for deployment to production. This can potentially slow down the entire data-to-insights process. End-to-end platforms for AI offer the promise of simplifying these processes, allowing teams that work with data to improve organizational results.
IBM Planning Analytics, formerly known as TM1, is a comprehensive planning and analytics application designed to integrate and streamline an organization’s planning processes. It can support multiple planning use cases on a single platform, including financial, headcount, sales and demand planning. The software automates enterprise-wide data collection to make it repeatable and scalable across multiple users and departments. It supports sophisticated driver-based modeling that enables rapid what-if or scenario-based planning, while its built-in analytics provide deep business intelligence capabilities. This enables senior executives and managers to work interactively to immediately assess their current position and consider the impact of various options to address opportunities and issues rather than laboring through a lengthy process.
Process-mining software isn’t exactly new, but it’s also not widely known in the software technology market. The discipline has been around for at least a decade, but is generating more interest these days with both specialist vendors and major enterprise software vendors offering process-mining products and services. We assert that through 2022, 1 in 4 organizations will look to streamline their operations by exploring process mining.
Digital commerce affects almost everyone’s lives. It is hard to remember a time when one could not sign on to a website like Amazon, order a product, pay for it and have it delivered to your front door within days, not weeks. Although catalogues have been around for a century or so, the digital-commerce revolution has changed the way we think about shopping for many of our everyday and special occasion products. Extend this to digital services, such as streaming videos or online games, and there is barely a sector that has not been touched by digital commerce. And, for organizations, it is an essential component of their revenue-management efforts that enables the digital transformation and monetization of goods and services.
Organizations are accelerating their digital transformation and looking for innovative ways to engage with customers in this new digital era of data management. The goal is to understand how to manage the growing volume of data in real time, across all sources and platforms, and use it to inform, streamline and transform internal operations. Over the years, the adoption of cloud computing has gained momentum with more and more organizations trying to make use of applications, data, analytics and self-service business intelligence (BI) tools running on top of cloud-computing infrastructure in order to improve efficiency. However, cloud adoption means living with a mix of on-premises and multiple cloud-based systems in a hybrid computing environment. The challenge is to ensure that processes, applications and data can still be integrated across cloud and on-premises systems. Our research shows that organizations still have a significant requirement for on-premises data management but also have a growing requirement for cloud-based capabilities.