10 Best Data Mining Tools

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Data mining software solutions are indispensable tools for uncovering valuable insights and patterns within your organization’s data, facilitating discoveries that lead to better decision-making and more effective strategic initiatives. We evaluated the most popular enterprise solutions to see how they compared on features, support, integrations, and price—here are our top picks for the best data mining software of 2023:

Top Data Mining Software Comparison

In comparing and contrasting these top data mining software contenders, we evaluated each offering from the perspective of a data professional looking to implement a cost-effective, enterprise-focused solution. The following chart shows how they compared at a glance.

Core Features Enterprise Features Support Integrations Pricing
SAS Enterprise Miner ☆☆☆☆☆ ☆☆☆☆ ☆☆☆☆☆ ☆☆☆☆

~$100,000 per year

No free trial

Oracle Data Miner ☆☆☆☆☆ ☆☆☆ ☆☆☆☆ ☆☆☆½ Free
IBM SPSS Modeler ☆☆☆☆☆ ☆☆☆☆☆ ☆☆☆☆½ ☆☆☆☆☆ $499 per month (~$6,000 per year)
TIBCO Data Science ☆☆☆☆☆ ☆☆☆☆ ☆☆☆½ ☆☆☆½ -$2,000 per year
Apache Mahout ☆☆☆☆☆ ☆☆☆☆☆ ☆☆ ☆☆☆☆☆ Free (open source)
DataMelt ☆☆☆☆ ☆☆½ ☆☆½ ☆☆☆☆ Free
MonkeyLearn ☆☆☆½ ☆☆½ ☆☆½ ☆☆☆ $299 per month (~$3,600 per year)
Integrate.io ☆☆☆½ ☆☆½ ☆☆½ ☆☆☆½ $15,000 per year
Snowplow Analytics ☆☆☆☆ ☆☆☆ ☆☆☆ ☆☆ $800 per month ($9,600 per year)
Dundas BI ☆☆☆☆ ☆☆☆½ ☆☆☆½ ☆☆☆☆ $6 per user, per month ($72 per year)

SAS icon.

SAS Enterprise Miner

Best for Data Analytics

SAS Enterprise Miner is a data mining and analytics platform that allows organizations to make better-informed strategic decisions based on predictive data models. The solution enables data professionals to find patterns in data, model complex relationships, and identify exceptions.

As the flagship data mining offering from SAS, Enterprise Miner taps into the analytics software giant’s familiar interface to simplify the data mining process. With its unified user interface (UI), data professionals can access a wide set of analytics functions, data science toolkit, and statistical modeling tools that enable the creation of predictive and descriptive models on expansive data sources.

The SAS Data Miner interface.
The SAS Data Miner interface.

Pricing

  • Basic license starts around $100,000 per year
  • No free trial

Features

  • SAS Rapid Predictive Modeler provides a graphical user interface (GUI) for managing data mining workflows
  • Visual assessment and validation metrics for verifying results
  • Open source integration with R

Pros

  • Easy to create advanced models via an easy interface
  • Supports all data mining tasks, metrics, and processes including random forest, neural networks, support vectors, and ensemble modeling

Cons

  • One of the more expensive data mining solutions
  • Significant programming skills to customize outputs

Oracle icon.

Oracle Data Miner

Best for Oracle Databases

Oracle Data Miner is an extension to Oracle SQL Developer for performing data mining on Oracle databases, viewing data, quickly developing multiple machine language (ML) models, comparing and evaluating performance across multiple models, and more.

The solution features a drag-and-drop workflow editor and extensive graphical analytical workflows that enable data professionals to easily explore data and develop ML methodologies.

The Oracle Data Miner UI.
The Oracle Data Miner UI.

Pricing

  • Free as part of Oracle SQL Developer (also free) and Oracle Database, which costs about $47,500 per processor

Features

  • Interactive workflow tool lets users create, evaluate, modify, and share ML methodologies
  • Integrates with R for user-defined functions
  • Works with Big Data SQL to access data across sources, including Oracle Database, Spark, and Hadoop

Pros

  • Support for various graph nodes for visualizing data (e.g., histograms, summary statistics, scatterplots, box plots, and more)
  • Capable of ingesting/processing structured data in tables and views (numeric and varchar datatypes), unstructured data and character large objects (CLOBs), transactional data, aggregations, and spatial and graph data
  • Model Build node automatically builds multiple ML comparison models

Cons

  • Optimized for Oracle Databases
  • User interface is outdated and requires a refresh

IBM icon.

IBM SPSS Modeler

Best for Statistical Analysis

IBM SPSS Modeler offers data mining tools that enable you to quickly develop predictive models per domain expertise and rapidly deploy them into production environments. Designed around the industry-standard CRISP-DM model, IBM SPSS Modeler was designed to support the entire data mining process from planning to data collection to analysis, reporting, and production deployment.

The SPSS Modeler interface.
The SPSS Modeler interface.

Pricing

  • Starts at $499 per month
  • Free 30-day trial available

Features

  • Advanced statistics like univariate and multivariate modeling for complex analysis
  • Data preparation tools for streamlining collection for more efficient analysis and accurate predictions

Pros

  • Powerful, industry-leading statistical tools and methods
  • Easy-to-use forecasting features allow for non-technical users to quickly build time-series forecasts

Cons

  • Some reported performance issues with large datasets
  • More expensive option when compared with similar solutions

TIBCO icon.

Tibco Data Science

Best for Core Features

TIBCO Data Science is a unified data mining platform that brings together capabilities from the vendor’s leading solutions (Statistica, Spotfire Data Science, and Enterprise Runtime for R), allowing organizations to expand and manage data science deployments with flexible authoring and deployment capabilities.

The collaborative UI enables all data stakeholders in the organization to work together on data science projects and build ML workflows with a minimal amount of code.

The TIBCO Data Science UI.
The TIBCO Data Science UI.

Pricing

  • Upward of $2,000 per year (with Spotfire as base)
  • Free trial available

Features

  • Collaborative web-based user interface for creating ML and data preparation pipelines
  • Access to sophisticated advanced analytic workflows with 16,000 functions
  • TERR high-performance, enterprise-quality statistical engine for predictive analytics

Pros

  • Flexible visual query enables quick answer retrieval
  • User interface designed for novice data science users and professionals alike

Cons

  • Extensive point-and-click approach can make customization difficult for advanced users
  • Quality and options for support are limited

Apache icon.

Apache Mahout

Best for Distributed Data Mining

The open-source Mahout Framework allows mathematicians, statisticians, and data scientists to quickly implement their own algorithms. Built on Apache Spark as the distributed backend out-of-the-box, Mahout can be extended to work with other distributed backends.

Mahout is especially favored by data professionals and scientists accustomed to using the Scala language, since the platform’s distributed linear algebra framework and mathematically expressive DSL is designed in Scala.

A recommender system based on Mahout.
A recommender system based on Mahout.

Pricing

  • Free (open source)

Features

  • Uses the mathematically expressive Scala DSL
  • Developed on Spark, but supports multiple distributed backends
  • For ML, modular native solvers enable CPU/GPU/CUDA acceleration

Pros

  • Expansive collection of libraries and algorithms for data processing, analysis, and optimization
  • Open source codebase allows for extensive customizations

Cons

  • Can be challenging to use for data professionals unfamiliar with Scala
  • Computing time can be relatively slow compared to other frameworks—especially for ML-heavy workloads

DataMelt icon.

DataMelt

In data science, Python and Java are the two heavyweights in terms of mainstream programming language adoption. One of the most well-known open source data mining tools written in Java, DataMelt (referred to as DMelt in the data mining community) offers a powerful visualization library and computational platform for supporting a wide range of data mining use cases.

As an all-in-one data mining and analytics tool, DataMelt integrates robust mathematical and scientific libraries for statistical analysis and data visualization with a particularly strong suit in handling massive data volumes, such as in financial market applications.

The DataMelt UI.
The DataMelt UI.

Pricing

  • Free

Features

  • Chart plotting and statistical libraries
  • Sophisticated data mining/analysis capabilities
  • 2D/3D visualizations and support for vector graphics

Pros

  • Open-source and fully-customizable
  • Expansive support resources from community

Cons

  • Desktop-only version
  • Lack of cloud/SaaS scalability

MonkeyLearn icon.

MonkeyLearn

MonkeyLearn is a data mining platform that focuses on text-based data analysis, providing instant data visualizations and detailed insights for use cases like labeling or visualizing customer feedback.

The MonkeyLearn platform comes with pre-built and custom ML models that allow for AI-powered data mining, all without writing code.

The MonkeyLearn interface.
The MonkeyLearn interface.

Pricing

  • Around $299 per month
  • Free trial available

Features

  • Easy ML model training and deployment for automatically tagging and classifying text

Pros

  • Easy-to-use, all-in-one solution
  • Comes with a variety of pre-built templates

Cons

  • Limited integrations with external data sources
  • Lack of sophisticated visualization tools

Integrate.io icon.

Integrate.io

Formerly known as Xplenty, Integrate.io offers a unified stack that enables the creation of no-code data pipelines across the entire data’s journey. The platform offers a complete set of extract, transform, and load (ETL) tools and connectors for easily building and managing clean, secure data pipelines for driving organizational decision-making and strategy.

The Integrate.io UI.
The Integrate.io UI.

Pricing

  • Starts at $15,000 per year

Features

  • Allows data professionals to create no-Code ETL data pipelines in minutes
  • Offers self-hosted, secure REST API code automation

Pros

  • Modern, highly-scalable SaaS platform
  • Streamlined UI and navigation
  • Powerful set of ETL/ELT capabilities and data monitoring/alerting features

Cons

  • Lack of free trial for evaluating the platform
  • Relatively high price point

Snowplow icon.

Snowplow Analytics

Though Snowplow bills itself as a Behavioral Data Platform (BDP), the solution is a data mining and analytics platform for creating and operationalizing rich, first-party customer behavioral data directly from an organization’s data warehouse or data lake in real-time.

As a BDP, the solution is focused on helping organizations across all industries glean insights in their customer behavioral data.

The Snowplow.io UI.
The Snowplow.io UI.

Pricing

  • $800 per month
  • Free trial available

Features

  • Automated testing suite, sandbox environment, and full staging environment
  • Alerts for monitoring, debugging, and reprocessing events
  • Out-of-the-box workflows and descriptive fields (over 130)

Pros

  • Offers open-source option
  • Integrates well with leading data warehouses like Snowflake

Cons

  • Developer-focused (may be difficult for non-technical users)
  • Lack of customizations like advanced visualizations and custom events

Dundas icon.

Dundas BI

The Dundas BI platform offers data exploration, visual analytics, and dashboards and report sharing and creation in a streamlined business intelligence and data platform. The solution can be deployed as a standalone portal or integrated as part of an embedded BI solution.

The integrated platform offers myriad features for data mining and analysis, as well as interactive data visualizations, open APIs, and more.

The Dundas UI.
The Dundas UI.

Pricing

  • Starts at $6 per user, per month

Features

  • Strong ETL capabilities, including a scheduler
  • Wide array of supported data sources, from Oracle to Hadoop
  • Comes with pre-built reporting templates and dashboards

Pros

  • Strong customer support and troubleshooting options
  • Data analysis can be performed on-the-fly

Cons

  • Versioning feature requires enhancement
  • May have difficulty in handling larger datasets

Key Features of Data Mining Software

The following key features will help you guide your data mining tool selection process, keeping in line with your organization’s organization’s data mining objectives, scalability, and usability requirements.

Data Sources and Connectors

When evaluating data mining software, analyze its compatibility with various data sources. Data can come from a wide range of places, including databases, spreadsheets, cloud storage, web APIs, and more. Effective data mining software should support seamless integration with these data sources, allowing users to easily import and access the data they need for analysis. Look for software that offers a wide range of connectors or APIs to ensure access to data no matter where it’s stored.

Data Preprocessing Tools

Data preprocessing is a crucial step in the data mining process that involves cleaning, transforming, and structuring data to make it suitable for analysis. The software you choose should provide tools for data cleansing, handling missing values, normalization, and feature selection. Additionally, it should offer the ability to explore and visualize your data to better understand its characteristics.

Algorithm Library

A competent data mining software should offer a diverse selection of data mining algorithms, including classification, regression, clustering, association rule mining, and more. Be sure to look for software that provides both a wide range of algorithms, as well as documentation for those algorithms.

Automations and Workflows

Data mining is a complex process that involves multiple steps, from data preparation to model deployment. Software that offers automation and workflow capabilities can streamline these processes and make them more efficient. Look for software that allows you to create and customize workflows, automate repetitive tasks, and schedule regular data mining processes. This can save time and reduce the risk of human error.

Visualizations

Data mining results are often more interpretable and actionable when presented visually. A data mining solution should therefore offer data visualization tools for creating charts, graphs, and interactive dashboards to convey insights effectively. Effective visualization tools can help you communicate your findings to non-technical stakeholders and make data-driven decisions more accessible.

How to Choose the Best Data Mining Software for Your Business

The key features mentioned in this article should guide your selection process and help you choose a solution that aligns with your organization’s data mining objectives, scalability, and usability; keep the evaluation criteria listed in the next section in mind when analyzing options per your organization’s unique requirements and environments.

How We Evaluated Data Mining Software

We evaluated the software against the criteria below, using a rubric to score them on a 0-5 scale. We then aggregated the scores to rank the systems to determine the top data mining solutions.

Core Features | 25 percent

AI/ML Visualizations, Data Workflow, Management, Advanced Model Creation, Statistical Toolkit

Enterprise Features | 20 percent

Multi-Language/Region Availability, Cloud and On-Premise/Desktop Option, Data Privacy/Compliance Controls, Data Estate Management Tools, Regular Feature Enhancements

Pricing | 10 percent

Free Trial/Tier, Overall Cost, Pricing Tiers, Add-on/Option Pricing, Upgrades/Discounts

Support | 15 percent 

Live Chat, Phone, Email, Documentation/Knowledge Base, Premium Support

Integrations | 10 percent

API, Ecosystem, Developer Resources, Plugins/Library, Usability

Vendor Profile | 20 percent

Breadth of Vendor Suite, Vendor Business Type, Customer Base, Length of Time in Business, Reputation

Frequently Asked Questions (FAQs)

What is data mining software, and what does it do?

Data mining software are specialized tools that allow data professionals to analyze large datasets for discovering hidden patterns, trends, and valuable insights.

How does data mining software work?

Data mining software employs various algorithms and techniques to extract knowledge from data, making it useful for tasks such as predictive modeling, classification, clustering, and association rule mining.

What types of data can I analyze with data mining software?

Data mining software can analyze a wide range of data types, including structured data (e.g., databases, spreadsheets) semi-structured data (e.g., XML files), and unstructured data (e.g., text documents, images, and videos).

Does data mining software work with streaming data as well?

Some advanced data mining software can handle real-time and streaming data as well, for use cases like live stock trading and security/surveillance.

Do I need programming or technical skills to use data mining software?

The level of technical expertise required to use data mining software varies—as you can see from the tools in this list, some software requires programming skills to create custom algorithms or scripts, while others provide UIs and no-code interfaces for non-technical users.

Bottom Line: Enterprise Data Mining Software

Data mining software enables organizations to discover valuable insights, patterns, and relationships within their data. But not all solutions are created equal—to ensure you select the right software for your needs, keep these considerations and key features top-of-mind when evaluating tools for your data mining projects.

Read Data Management: Types and Challenges to better understand what pain points enterprise organizations commonly encounter when working with large stores of data.

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