![]() ![]() In choosing the right predictive analytics tools for the job, it is essential to identify the enterprise, business or functional needs of your use cases. "It's a much more automated and augmented process, so it is more accessible," she said.įinding the right tool for the right job. ![]() The new capabilities of automated machine learning, or AutoML for short, reduce the need to deeply understand how the variables affect each other, automatically choosing the best combination of algorithms for a given task. What used to require weeks of writing code can now be accomplished with a few mouse clicks and a lot of automation on the back end. "You don't have an be an expert to go in and use these tools anymore," Idoine said. Today, enterprises and vendors are exploring various types of augmentation to reduce the expertise and time required for many of the steps in this process. Idoine cited as an example her early (pre-Gartner) work in building predictive models to improve logistics. Her work then required deep knowledge of how the algorithms worked and also how to code them. The major shift in these tools is that it is getting easier to tune existing predictive modeling services or craft new ones from scratch. Here are some other important aspects of modern predictive analytics tools to consider.Įasier to implement. ![]() For example, a sales manager cares about a better lead scoring algorithm, a marketing manager wants a better click-through rate and the finance team wants to reduce fraud. Predictive analytics is just one aspect of these tools, and in practice, users may not even directly refer to the term when applying predictive analytics to use cases. These tools are used to develop a variety of analytics and artificial intelligence (AI) models used for descriptive, diagnostic, predictive and prescriptive analytics. Today, they are commonly referred to as data science and machine learning tools. The terms used to describe the various tools for building predictive models have also evolved over the years. This is now starting to change with dramatic improvements in the capability of tools designed for both data analytics experts and regular business users, said Carlie Idoine, research director at Gartner. 7 top predictive analytics use cases: Enterprise examples.This article is part of What is predictive analytics? An enterprise guide ![]()
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