Machine learning is valuable for organizations, but it can be hard to deploy. Our Machine Learning Dynamic Insights research identifies that not having enough skilled resources and difficulty building and maintaining ML systems are pressing challenges organizations face in applying ML. Traditional ML model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. And as the number of ML models grow, their management becomes difficult. By bringing automation to ML, organizations can reduce the time it takes to create production-ready ML models. AutoML can also enable organizations to make data science initiatives more accessible across the organization.
The amount of data flowing into organizations is growing exponentially, creating a need to process more data more quickly than ever before. Our Data Preparation Benchmark Research shows that accessing and preparing data continues to be the most time-consuming part of making data available for analysis. This can potentially slow down the organizational functions which depend on the analysis results. Trying to get ahead of the backlog with incremental improvements to existing approaches and traditional technologies alone can be frustrating.
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.
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.