February 27, 2024

The Best Predictive Analytics Tools and Software for 2022

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The Best Predictive Analytics Tools and Software for 2022

Predictive analytics tools and software have existed since the 1960s, but it was not until the early 2000s that they became ubiquitous in many organizations, thanks to their relatively affordable price tags and easy-to-use interfaces. By 2022, more than half of all organizations will have implemented predictive analytics tools, with three-quarters of them being satisfied with the ROI they got from doing so. In order to learn which predictive analytics tools are likely to be popular by 2022, we compiled this list of our favorites right now. If you’re looking for the best predictive analytics tools and software for your needs, read on!

 3 Reasons Why the Use of Predictive Analytics Is Growing

#1: The implementation of predictive analytics has significantly increased as businesses make the switch to agile methods of management. In 2017, the research found that companies which use agile management systems have a 2x chance of using predictive analytics. This has created an increased demand for new predictive analytics tools and software solutions in order to meet this need. #2: The use of artificial intelligence (AI) will continue to grow in its capacity to store huge amounts of data that can be analyzed.

 5 Most Popular Types of Data To Analyze

To find the best predictive analytics tools and software, you’ll want to know what types of data to analyze. There are a number of categories that can be helpful in this endeavour, including financial data, health data, social media data, emails, call records.

 10 Best Predictive Analysis Software And Tools Of Today

Predictive analytics software is one of the most valuable business assets for companies across all industries. But with the sheer number of predictive analytics tools out there, finding the best solutions can be difficult. To help, here are 10 predictive analysis software solutions to consider.

  • Tableau Public
  •  Stitch Fix
  •  Good Data Solution
  •  Descartes Labs
  •  IBM Watson

 

Predictive analytics are an important part of running a business. It allows the company to identify any potential risks or issues before they arise, which can help businesses mitigate damage and save money in the process.

SAS Institute

The SPSS predictive analytics software is an industry-leading tool that covers both the qualitative and quantitative aspects of any given data analysis project.

 SPSS Modeler

In the digital age, we are often faced with a torrent of data. While the big data can be overwhelming at times, these resources will make it much easier to turn that data into useful knowledge.

 Tableau

  • Takes data of all different shapes, sizes, formats, in a single view
  • All of your complex analytics are created using drag-and-drop technology. So you’ll be able to easily visualize the most relevant data from any source and explore the implications from multiple perspectives.
  • Has everything you need to take command of any project
  • from managing people to controlling budgets, forecasting demand for services or meeting timelines. 

Rapid Miner

One of the best predictive analytics tools and software is Rapid Miner. It has a plug-in library to create complicated models without advanced coding, as well as scripts to make your own models from scratch.

Alteryx

At Alteryx, we care about making your data work better. To help our customers make informed decisions, we design, develop, and deploy predictive analytics software that automates time-consuming tasks.

 Birst

Today, predictive analytics is widely used in order to make data-driven decisions and tackle difficult problems. The following are the top 5 predictive analytics tools and software of the year.

 

JMP Section: Platfora Section: WibiData Section: Microsoft Power BI Section: Amazon Redshift Section: BigML Section: OrangeOpenSource

There are many predictive analytics tools available to help businesses make smarter decisions. These tools can be broadly grouped into four categories: data mining, visualization, statistics and machine learning.

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