In 2020, people as a whole generated 2.5 quintillion data bytes every day. While not all of those are collected by businesses, a large portion of them are, leaving an insane amount of data that companies have to comb through to get actionable insights. Due to the sheer volume of data organizations intake, data mining is becoming big business as these organizations look to make smarter, better-informed decisions. If you need to implement data mining software in your business, this guide can help you choose the right tools.
Data mining is the process of pulling information from large datasets in order to find patterns or trends that can inform future decisions. Organizations can also use it to highlight anomalies and attempt to identify the root cause of issues. How does data mining work? Typically, it uses artificial intelligence (AI), machine learning (ML), and statistical models to identify relevant information. Data mining is a big part of business intelligence, which helps companies cut costs, improve relationships with their customers, and increase revenue.
Data mining tools are software solutions that use AI and ML to pull and analyze data, highlight trends, and provide actionable insights for businesses. The software can refine information from both structured and unstructured datasets, so organizations can make predictions and understand relationships between different parts of their business. Data mining tools allow businesses to address questions that would take too much time to answer if they had to analyze data by hand.
The following data mining tools all have good user reviews and healthy feature sets.
RapidMiner
RapidMiner offers automated data mining and modeling tools with AI and ML to provide clear visualizations and predictive analytics. The drag-and-drop interface makes it easier for analysts to create predictive models, and the library includes over 1,500 pre-built algorithms, meaning there’s a model for nearly any use case. There are also pre-built templates for common scenarios, including fraud detection and maintenance, to lower the time analysts have to spend building the models. RapidMiner can connect to any data source, or users can import data from Excel. Interested parties must request pricing from RapidMiner; it’s not available on the website.
Key Features
Point-and-click database connections
Drag-and-drop model builder
MySQL, PostgreSQL, and Google BigQuery support
Out-of-the-box algorithms and templates
Multiple types of charts and graphs
Automated machine learning
R and Python support
Pros
Direct connections to external data sources
Easy to automate entire machine learning process
Helpful and responsive customer service
Cons
Some users said the web application for AI Hub doesn’t have much functionality
The cost is higher compared to competitor platforms
Oracle Data Miner
Oracle Data Miner is an extension of the Oracle SQL Developer that helps analysts quickly build a variety of machine learning models, apply them to new data, and compare the models for actionable insights. It offers a drag-and-drop editor, allowing both data scientists and regular users to get answers to their data-related questions. The workflow API makes it easier to deploy the model throughout the business, embedding analytics into the applications where analysts are already working. Pricing is not clearly available on the website, so businesses will have to contact Oracle for more information.
Key Features
Drag-and-drop model builder
Interactive workflow tool
Multiple types of visualizations
Integration with open-source R
Automated model building
Works with BigDataSQL to access major data sources
Pros
Can ingest both structured and unstructured data
Easy to obtain and restructure data
Platform is organized and provides easy data management
Cons
The interface may not be as user-friendly as other platforms
Some users complained the processing was slow
Sisense
Sisense is data analytics software that allows users to embed analytics into the platforms they already work in, putting the information in the same place they’re making decisions. Additionally, businesses can white label the embedded analytics, so they can also push them out to their customers. With live data connections, businesses can get real-time insights and a strong self-service platform. With code-first, low-code, and no-code options available, analysts of any skill level can get their data questions answered and build helpful models. Plus, the AI allows analysts to type in a question, and then it guides them through the investigation. Pricing is not available on the website.
Key Features:
Predictive analytics
Code-first, low-code, and no-code tools
Self-service analytics
Live data connections
Embedded analytics
Cloud-based options
Pros
Provides deep insights into data
Easy to use and create dashboards and queries
Quickly connects to databases and processes data
Cons
Doesn’t always save queries users are working on
Reports don’t always update in the timeline users set
Alteryx APA
Alteryx APA offers automated analytics with machine learning across the entire process, including mining, modeling, and visualization. There are over 80 natively-integrated data sources that users can pull from, including Oracle, Amazon, and Salesforce, or they can use APIs to connect to others. Analysts can also add maps to their visualizations to highlight geographic trends. Alteryx offers step-by-step guides to help analysts of any skill level build models without coding. However, expert data analysts can also use R-based models. Pricing information is not available on the website.
Key Features
Automated analytics
Native data source integrations & APIs
Geographic analytics
No-code options
Multiple visualization options
Sharing and exporting capabilities
Pros
Reliable and efficient infrastructure
Supports processes of all sizes and levels of complexity
More user-friendly than similar platforms
Cons
Big data sources sometimes take a long time to process
Doesn’t include as many visual tools as competitors
SAS Data Mining
SAS Data Mining helps organizations answer complex questions with analytics through automated modeling and a collaborative platform. With natural language generation, the platform can create a post-project summary, detailing important trends, outliers, and insights. Then, users can add notes to the report to make communication and collaboration easier. SAS Data Mining supports a variety of coding options, so analysts can create or adjust algorithms in their language of choice. Data scientists can also combine structured and unstructured data in models to get as much information as possible. Pricing is not available on the SAS website.
Key Features
Drag-and-drop interface
Code-first and no-code options available
PDF sharing
Collaborative environment
Public API
Automatic modeling
Natural language processing
Pros
Helpful and responsive customer service
Easy to integrate data
Large number of algorithms available
Cons
Some users complained that the platform wasn’t updated very often
Difficult to determine best practices for the tool
Teradata
Teradata is a data mining tool built for organizations using multi-cloud deployments, providing access to all databases, data lakes, and external SaaS applications. No-code options allow users from any business department to get answers to their questions to make more informed decisions. Organizations can deploy Teradata on any of the major public cloud platforms, including AWS, Azure, and Google, as well as in private clouds or on-premises. Teradata doesn’t charge upfront costs, instead offering a pay-as-you-go model. A pricing calculator is available on the website to help users estimate their costs.
Key Features
Code-first and no-code options
Scalable workloads
Multiple deployment options
Integrates with a variety of sources
Support for all common data types and formats
Role-based analytics options
Pros
Consolidates data from all sources
Handles sophisticated and simple queries
Requires very little maintenance for the cloud-based options
Cons
Can be expensive compared to competitor platforms
On-premises maintenance can be difficult and time consuming
Dundas BI
Dundas BI is a data analytics platform that offers real-time insights and visually-appealing reports and dashboards. It can consolidate data from any source with open APIs, ensuring that users have all the information they need to create effective models. Users can create content that’s easy to understand with minimal input from IT. Interactive dashboards allow analysts to edit models to see how different variables would impact the business. Dundas BI offers a lot of out-of-the-box functionality without requiring add-ons or upgrades. Pricing information is not available on the website.
Key Features
Customizable dashboards
Open APIs
Drag-and-drop design tools
Multiple visualization options
Communication and collaboration tools
Automated notifications
What-if analytics
Pros
Feature-rich platform
Competitively priced compared to similar platforms
Works equally well on mobile devices and desktops
Cons
Can have a steep learning curve
Some users complained about the platform crashing
H2O
H2O is an AI cloud built for data mining to improve the insights businesses get from their data and their decision making. Automated machine learning solves complex problems while providing results in an easy-to-understand format. Analysts can train and deploy the AI in any environment, and there are several different modeling types that they can choose from. Real-time data analysis provides accurate predictions and fast insights to help businesses make quicker decisions and improve their scalability. H2O can be deployed with either hybrid or fully managed options. The platform is open-source and free to use, but businesses can pay for enterprise support and management.
Key Features
Open-source platform
Powerful AI algorithms
Support for multiple programming languages, including R and Python
Companies that use data mining software get faster access to important information and actionable insights that can improve their decision-making process. Each day, businesses take in so much data that it would be impossible to sort through manually. They need data mining tools that include AI to run what-if scenarios and get accurate forecasts. Businesses looking for the best data mining software for their business should take advantage of free trials and read user reviews to determine which one will work best for their team.
Jenn Fulmer is a writer for TechnologyAdvice, IT Business Edge, Channel Insider, and eSecurity Planet currently based in Lexington, KY. Using detailed, research-based content, she aims to help businesses find the technology they need to maximize their success and protect their data.
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