Predictive Analytics Software
Predictive Analytics Software TrustMap
TrustMaps are two-dimensional charts that compare products based on trScore and research frequency by prospective buyers. Products must have 10 or more ratings to appear on this TrustMap.
SAP Analytics Cloud combines planning, business intelligence, and predictive analytics as a unified cloud-based platform allowing visualization, planning and prediction to be performed within a single tool. It is built on top of the very fast SAP Cloud Platform, and integrates natively…
RStudio is a modular data science platform, combining open source and commercial products. The vendor states their open source offerings, such as the RStudio IDE, Shiny, rmarkdown and the many packages in the tidyverse, are used by millions of data scientists around the world to…
Alteryx aims to be the launchpad for automation breakthroughs. Be it for personal growth, achieving transformative digital outcomes, or rapid innovation, the vendor boasts users will see unparalleled results. Alteryx converges analytics, data science and process automation into one…
SPSS Statistics is a software package used for statistical analysis. It is now officially named "IBM SPSS Statistics". Companion products in the same family are used for survey authoring and deployment (IBM SPSS Data Collection), data mining (IBM SPSS Modeler), text analytics, and…
MicroStrategy Analytics is an enterprise business analytics and mobility platform. Key features include automatic big data analysis and reporting, data discovery and visualization, digital security credentials, and support for mobile devices.
Anaconda is an open source Python distribution / data discovery & analytics platform.
JMP is a division of SAS and the JMP family of products provide statistical discovery tools linked to dynamic data visualizations.
Logi Info (or the Logi Analytics Platform) is a developer-grade analytics platform designed for application teams needing to rapidly build, deploy, and maintain mission-critical applications. Logi serves the embedded model, so companies increase the likelihood of building valuable,…
The DataRobot AI Cloud platform is presented as a solution that accelerates and democratizes data science by automating the end-to-end journey from data to value and allows users to deploy AI applications at scale. DataRobot provides a centrally governed platform that gives users…
SAS Advanced Analytics is the company's suite of applications and modules for advanced statistical analysis and predictive modeling. Products from the suite are available a la carte or in bundles, such as SAS Analytics Pro, which offers an alternative to mixing and matching software…
IBM SPSS Modeler is a predictive analytics platform that helps users build accurate predictive models quickly and deliver predictive intelligence to individuals, groups, systems, and enterprises. With an intuitive interface and drag-and-drop features, the software is designed to…
Lexalytics (formerly Semantria) is a software-as-a-service and services provider specializing in cloud-based text analytics and sentiment analysis. This business intelligence (BI)/analytics tool offers an easy way to unlock meaningful insights and sentiment analysis from large amounts…
The former Mattersight Predictive Behavioral Routing service is now part of NICE Nexidia Analytics since the
Designed to deploy predictive modelsOne tool for data processing, analysis and modeling! The user-friendly workflow interface helps you explore all your data & more. Supports all analytical tasks: Extracting and saving data from/to different database systems, files, and…
What are Predictive Analytics Tools?
Predictive Analytics tools are used to analyze current data and historical facts in order to better understand customers, products, and partners. They are also used to identify potential risks and opportunities. Predictive analytics platforms tend to be very complex products and require advanced skill sets in order to use them effectively.
In the hands of skilled data analysts and data scientists, predictive analytics can be an extremely powerful tool. It can, for example, calculate customer lifetime value for a given customer; estimate sales projections for the next quarter, or predict what product an online shopper is likely to purchase next.
Predictive analytics tools make these analyses much easier and more accessible for organizations. They make leveraging disparate data sources into single analyses a lighter manual load. Predictive analytics platforms can standardize or even automate some repeated analyses. Intelligent analytics engines can also make new or ad hoc analysis more accessible for technical and business analysts alike. These tools lower the barrier to entry for many non-specialized users, although this is not universal across all analytics platforms.
What is Predictive Analytics?
Predictive analytics explore, process, and aggregate historical data, then apply various predictive modelling techniques to determine potential future outcomes. Predictive analytics help users and organizations better understand what is likely to happen in the future, based on what has occurred in the past.
Predictive analytics is one of the more advanced forms of business analytics. It fits within this range of analysis types available to organizations:
Descriptive analytics: provides a real-time view of what is occurring right now. Current stock prices are one example of descriptive analytics.
Diagnostic analytics: These analyses help explain why or how something occurred or is currently happening.
Predictive analytics: These analytics use descriptive or diagnostic data to determine future outcomes.
Prescriptive analytics: These help guide what should be done to create a given outcome.
Predictive analytics can often overlap or evolve into prescriptive analytics. The functional difference is how the analytics tool is utilized or deployed. Predictive analytics can also be used prescriptively to inform other business decisions, either via standardized reporting or ad hoc strategic assessments.
The Importance of Good Data
The basis for good predictive outcomes is good data. For example, to make predictions about what customers are likely to buy in the future, it's essential to have detailed data on what they have bought in the past, attributes of purchased products, etc.
Provided there is adequate high-quality data, predictive models are created using techniques like regression testing, decision trees or other methodologies to measure degree of correlation between variables to help predict future behavior. Predictive analytics tools will often leverage big data to make more informed and accurate predictions and assessments that are beyond the scope of manual analyses.
Predictive Analytics vs. Business Intelligence
Predictive Analytics tools are strongly related to Business Intelligence, and they are sometimes considered as part of the BI universe. The distinction between BI and predictive analytics is that BI is usually considered descriptive, i.e. looking at what happened in the past. Predictive analytics is about finding hidden patterns in data using complex mathematical models to predict future outcomes.
The emergence of big data platforms like Hadoop and very fast in-memory analytics products has resulted in some blurring of the lines between big data and predictive analytics.
Predictive Analytics Software Comparison
Consider these factors when comparing predictive analytics tools:
User Base: Who will primarily be using the predictive analytics tool? Will it be business users or technical specialists, such as data scientists? The main user base will impact the necessary accessibility/ease of use for each product, as business users will likely need less technical methods of interacting with the analyses.
Integration: What data sources should the product be able to pull from? Where and how will analyses be used, or what other systems should the product send data to? Consider what existing tools the organization uses, then cross-reference that with what the product can demonstrably integrate with.
Pricing: Predictive analytics pricing can range from open-source options to tailored enterprise platforms that are thousands of dollars per user. However, organizations also get the customization and ease of use that they pay for. Once the use case, user base, and necessary features have been clarified, ensure that each product’s pricing structure aligns with your specific use case.
A couple of well-known incumbent products own the lion’s share of the market and these tools tend to be much more expensive than newer upstart products with much smaller market share. One of the leading products in the category costs over $5,000 per user. Newer, less well-known products typically cost a fraction of the cost of the market leaders.
The free, open-source R programming language has more than 3,000 community-developed analytical applications, but can be challenging to use since it is code-based and does not have a drag-and-drop visual workspace.