what is predictive analytics software : Buyers Guide for you. The realm of predictive analytics has garnered increased attention within the realm of business intelligence, especially as BI itself continues to surge in popularity. Leveraging mathematical models and algorithms, predictive analytics software delves into an organization’s data, offering users forecasts of future outcomes and events. With numerous vendors offering predictive analytics tools, our comprehensive buyer’s guide aims to illuminate the diverse options at your disposal.

Understanding Predictive Analytics Software

While traditional BI software typically analyzes past and present trends, predictive analytics solutions pivot towards the future, aiding decision-makers in forward planning. These systems extract and code historical data, uncovering patterns that serve as the foundation for predictive models, enhancing the ability to forecast trends and outcomes. Although not foolproof, predictive analytics software excels in highlighting upcoming opportunities and potential risks, thereby refining decision-making quality.

Four Types of Analytics

  • Descriptive: Identifies real-time trends.
  • Diagnostic: Determines the cause of past events.
  • Predictive: Using previous historical data to predict future events as a preparatory step.
  • Prescriptive: Recommends specific actions by integrating descriptive and predictive data.

Key Features of Predictive Analytics Software

  • Predictive Modeling: Statistically predicts outcomes using historical data.
  • Data Mining: Extracts information to identify patterns and understand data sets.
  • Text Analytics: Mines textual sources for categorized information.
  • Data Visualization: Translates predictive insights into charts, graphs, or maps.
  • R Integration: Advanced feature using the open-source programming language R.

Identifying Your Buyer Type:

Before diving into predictive analytics software, clarify your purpose. Common applications span various industries, including banking and financial for credit score prediction, retail for optimizing marketing, pricing, and product distribution, and healthcare for improving patient care and disease prevention.

Current Market Trends

  • Greater Ease-of-Use: User-friendly interfaces catering to non-IT professionals.
  • Movement to the Cloud: Growing availability of cloud-based predictive analytics systems.
  • Increased Adoption Beyond Finance: Expansion into industries like healthcare, driven by a surge in customer data.
  • Overall Market Growth: The proliferation of data and technological advancements fueling a significant expansion in the predictive analytics market.

In essence, as you embark on evaluating predictive analytics software, be attuned to the evolving landscape, where user-friendly interfaces, cloud-based options, and a broader industry-specific adoption underscore the growth and dynamism of this field.

10 best Predictive Analytics Software

IBM SPSS Statistics:

Known for its advanced statistical analysis and predictive modeling capabilities.

SAS Advanced Analytics:

Offers a comprehensive suite for data analysis, predictive modeling, and machine learning.


An open-source platform with user-friendly interfaces for data preparation, machine learning, and predictive analytics.

Oracle Advanced Analytics:

Integrates predictive analytics into Oracle Database, providing scalability and efficiency.


Empowers data analysts with tools for predictive modeling, data blending, and advanced analytics.

Microsoft Azure Machine Learning:

A cloud-based service with powerful predictive modeling and machine learning capabilities.

KNIME Analytics Platform:

Open-source software that allows seamless integration of various data processing and machine learning components.

TIBCO Spotfire:

Combines data visualization and predictive analytics for interactive and insightful decision-making.


An automated machine learning platform that simplifies the process of building and deploying predictive models.

Google Cloud AI Platform:

Offers a range of tools for building, training, and deploying machine learning models on the Google Cloud infrastructure.

These platforms cater to different needs, so the best choice depends on specific requirements, data complexity, and integration preferences.