The 5 Best Data Mining Tools for 2020

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Nowadays, companies have many options at their disposal to turn raw data into actionable next steps with business intelligence software. Some data mining tools can speed up this process through machine learning algorithms. Data mining in the modern age goes above and beyond simple data analysis to extract useful information from huge data sets in smarter and more effective ways than ever.

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Best Data Mining Tools

You may wonder, what is data mining and do we even need it? This article will address these questions and help you compare and contrast the current leaders in data mining to see if they offer the right solution for you. Choosing the right fit for your company’s needs from a plethora of data mining software on the current market can be daunting, but we’re here to help you navigate the field.

SelectHub’s analyst team researched numerous software systems and found that these are the top five data mining software tools in their class.

Many BI tools can perform data mining to some extent, but which one is best suited for your business? Let’s dig deeper, explore your needs and find out what the right data mining tool can do for you.

Here’s what we’ll discuss:

What is Data Mining?

Data mining is the process of exploring and analyzing data sets to discover meaningful patterns. The most widely-used model for data mining, the cross-industry standard process for data mining (CRISP-DM), breaks down data mining into six major phases: business understanding, data understanding, data preparation, modeling, evaluation and data presentation. This methodology symbolizes an idealized sequence of events through the data mining process and the steps often serve as guidelines for an iterative cycle instead of a rigidly linear process.

1. Business Understanding

First, users figure out what the current situation is and what they want to accomplish through data mining from a business perspective. They define the problem, identify goals and set up a plan to proceed.

2. Data Understanding

Users should determine what data is necessary, gather their data from all available sources, examine and explore their data and then validate the quality of the data for accuracy and completeness

3. Data Preparation

A critical step in the data mining process, users will properly select, cleanse, construct, format and merge data, preparing it for analysis. While time-consuming, data preparation helps ensure the most accurate results possible by cleaning data, purging unusable data and turning raw data into something a BI solution can actually work with.

4. Modeling

Modeling is the core of any machine learning project. Users will decide which modeling technique to take to test scenarios that answer the project’s goals, then generate models through algorithms. This step consists of analyzing the data and generating tables, visualizations, plots and graphs that reveal trends and patterns.

5. Evaluation

Users will evaluate the results of the models in light of their originally defined business goals. They will make sure that the model produced is accurate and complete, and highlight what insights are most valuable from the results. Depending on what insights data mining uncovers, they may identify new objectives and additional questions to answer.

6. Data Presentation

The final step in the data mining process is turning all of this work into something useful to others, especially stake-holders. Users will take the results and determine a deployment strategy that ensures their analysis is understandable This could be as simple as creating a conclusive report, or as complex as documenting a reproducible, maintainable data mining process from start to finish. This may include delivering a presentation to the customer or decision-maker. Data presentation, or deployment as it’s sometimes referred to, summarizes the findings of the project and reviews the results to see if any improvements or next steps are necessary.

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Crisp DM Process Model

CRISP-DM helps guide data scientists and data analysts through data mining with steps that follow common sense and help them gain a deeper understanding of their data and the problem they’re seeking to address.

Data mining software tools perform two main categories of tasks: descriptive or predictive data mining. Descriptive data mining, as the name suggests, relates to describing past or current patterns and identifying meaningful information about available data. Predictive data mining instead generates models that attempt to forecast potential results. Descriptive data mining is reactive and more focused on accuracy, while predictive mining is proactive and may not deliver the most accurate results. Descriptive data mining tasks include association, clustering and summarization, while predictive data mining tasks include classification, prediction and time-series analysis. Both kinds of tasks are important for inferring what has happened, what is currently happening and what may happen in the future.

Why Use Data Mining Tools?

As a buzzword that comes up all the time in discussions of business intelligence and big data, what’s the difference between business intelligence, big data and data mining and why is data mining useful? Big data and data mining both fall under the broader umbrella of business intelligence, with big data referring to the concept of a large amount of data and the relationships between data points and data mining referring to the technique used for analyzing the minute details within data. Data mining and business intelligence are connected in a causal relationship; data mining finds the “what” in the data while BI discovers the “how” and “why” of insights that empower data-driven decision-making. Data mining finds the information needed while BI determines why it is important and what the next steps are.

Data mining helps make sense of massive blocks of big data and often provides answers to questions that you weren’t even thinking to ask. With automated machine learning, data mining accelerates many of the repetitive tasks in the data analytics and modeling processes. It can uncover previously unknown patterns, abnormalities and correlations in large data sets. Companies can use data mining software tools in business intelligence to identify patterns and connections that help them better understand their customers and their business, increasing revenues, reducing risks and more.

With applications in a wide variety of industries, including database marketing, fraud detection, customer relationship management and more, it can do such things as improve sales forecasting or analyze what factors influence customer satisfaction. It can help evaluate the effectiveness of marketing campaigns. Data mining tools identify the most relevant information in data sets, helping users turn their data into actionable insights that inform their planning and decision-making.

Best Data Mining Tools

Now that you know what data mining software tools do and more importantly, what they can do for you and your business, let’s look at some of the industry leaders. Our analyst team did the research and determined that these are the top five data mining tools currently on the market.
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RapidMiner Studio

RapidMiner Studio is a visual data science workflow designer that facilitates data preparation and blending, data visualization and exploration. It has machine learning algorithms that power its data mining projects and predictive modeling.

RapidMiner Studio

Screenshot from RapidMiner Studio of the visual workflow designer

Deployable as a SaaS or self-hosted solution for all operating systems, it is suitable for companies of all sizes. It has a perpetual free version with community support, or users can try out the Enterprise plan for free for 30 days.

What Makes It Different:

  • Free Version: RapidMiner has a perpetual free version that can process up to 10,000 rows of data coupling with one logical processor. The vendor continuously updates its open-source version as well and maintains robust community support.
  • Process Optimization: RapidMiner Studio can execute multiple processes in parallel. Users can configure the maximum number of processes running simultaneously to adapt to the resources available to the hardware in relation to the resources demanded.
  • In-Database Processing: Users can run data preparation and ETL inside databases, increasing the speed and performance of analytics and reducing the amount of information being transferred.
  • Interactive Data Preparation: A tool within RapidMiner’s platform, Turbo Prep provides a UI where data is always visible and users can make changes to it step-by-step, monitoring the results. The tool can save processes to be reused later, saving the user time in the future.

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Features:

  • Visual Workflow Analytics: The solution provides a drag-and-drop environment for building analytics processes. This user-friendly UI enables fast, intuitive data modeling.
  • Data Connectivity and Management: Users can access, load and analyze both structured and unstructured data. RapidMiner Studio can extract information and transform unstructured data into structured data. The platform can access data in a myriad of file types and locations, with wizards for Microsoft Excel & Access, CSV and databases, as well as connectivity to NoSQL databases, cloud storage, Salesforce, text documents, web pages and JDBC database connectors. This ensures that users can mine data from almost any kind of source.
  • Data Preparation: The solution can blend structured and unstructured data, leveraging newly built datasets for analysis. Functions like attribute generation, normalization, standardization, sorting, shuffling and more help users organize and cleanse data.
  • Data Visualization: Users can visualize their data in a variety of ways, including distribution plots, transition matrix and graph, charts and statistical models. An advanced chart engine enables on-the-fly grouping, filtering and aggregation.
  • Data Modeling: Through a set of modeling capabilities and machine learning algorithms, the platform can both perform predictive modeling as well as model validation. Auto Model also provides relevant models for users’ problems and enables users to compare the results of those models through a model leaderboard that contrasts the performance of different models over time.
Price: $$$$$
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Company Size Suitability: S M L

Alteryx Designer

Alteryx Designer is a self-service data science tool that performs integral data mining and data analytics tasks. Users can blend and prepare data from various sources and create repeatable workflows with built-in drag-and-drop features. It facilitates self-service data analytics and accelerates the data mining process, empowering all users, from the data analysts to the business users, to explore, analyze and model their data with ease.

Alteryx Designer

Screenshot of the Alteryx Designer data mining workflow interface.

It is part of the Alteryx suite, which consists of five products for big data analytics and business intelligence. Suitable for companies of all sizes, it can be installed as a SaaS or on-premises solution for Windows only.

What Makes It Different:

  • Technical Accessibility: Accessible to users with or without coding experience, the solution gives users the freedom to choose between a code-free or code-based interface. It also quickly connects to data sources, no code necessary.
  • Accelerated Data Prep: Through its tools that speed up the extraction and blending of data from an unlimited number of sources as well as automated workflows, Alteryx Designer is able to prepare and improve data to make it analytics-ready, letting users focus on analysis and decision-making.
  • In-Database Processing: Without moving data out of a database, Alteryx can process blending and analysis against large data sets, providing significant performance improvements over traditional analytics methods that move data to a separate environment for processing.
  • Scalability: With native integration to the other Alteryx suite products, including Alteryx Server, Connect, Promote and Analytics Gallery, Alteryx Designer can work as a part of a larger cohesive platform that can address a multitude of needs as a company grows.
  • Free Trial and Demo: Interested customers can choose between a free 14-day trial of the full version of its product via download or access an interactive online demo, no download required, that lets users try the product for 90 minutes with a guided walkthrough using sample data.

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Features:

  • Data Connectivity: With native data connections to more than 70 sources, Alteryx Designer can connect to a wide range of sources, including data warehouses, ERP and cloud-based applications, standard files, Microsoft Office files, social media data and more.
  • Data Preparation and Blending: Through a visual user interface, Alteryx Designer helps users maximize the value of their data by extracting and cleansing it, validating the completeness and quality of data sets before they’re analyzed.
  • Data Analytics and Modeling: From spatial analytics to predictive analytics and beyond, Alteryx Designer has the full spectrum of data analysis covered with its access to hundreds of analytics applications through the Alteryx Analytics Gallery. Simplifying predictive analytics, allows users to drag-and-drop a customizable set of analytics tools to build models or generate their own with custom R or Python coding or imported packages.
  • Data Workflows: Via a visual no-code drag-and-drop interface, users can create repeatable, automated workflows that build analytics models and reports. The Scheduler allows users to schedule the execution of workflows either regularly or at specific times or frequencies.
  • Reporting Options: Insights discovered in the solution can be turned into reports that can be refreshed on demand, or exported to a variety of formats, including spreadsheets, XML, PDF and formats compatible with leading third-party BI and data visualization tools like Tableau, Microsoft Power BI and Qlik.
Price: $$$$$
Deployment:
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Company Size Suitability: S M L

Sisense for Cloud Data Teams

Formerly known as Periscope Data, Sisense for Cloud Data Teams is a data analytics solution that helps users derive actionable insights from data in the cloud. Users can build cloud data pipelines, perform advanced analytics and create data visualizations that convey their insights, empowering data-driven decision-making. Dashboards updated in real time and access for unlimited users encourage organization-wide data literacy.

Sisense for Cloud Data Teams

Screenshot from a dashboard in Sisense for Cloud Data Teams

Available on an annual licensing model, it can be deployed as a SaaS or self-hosted solution for Windows and Linux systems.

What Makes It Different:

  • Faster ETL: The data engine for the platform performs large-scale data ingestion and optimizes raw data by bypassing steps of the ETL process. This allows for a hassle-free data import process via proprietary data caching technology.
  • Ease of Use: Users of all technical skill levels can explore their data and visualize trends through simple search query language, rather than via coding or modeling, making data mining and data analytics accessible for all employees.
  • Extensibility: The platform uniquely supports SQL, Python and R all in one environment, allowing users to create advanced analytics processes in any language, integrating any open-source programming or formulas from other packages or libraries. This allows it to support predictive analytics, natural language processing and data preparation for machine learning.
  • Enhanced Collaboration: With a single source of truth in a centralized data warehouse, reusable analyses and an interface that enables swift handoffs of workloads to other users, Sisense for Cloud Data Teams helps bring analysts and business users on the same page to discover and share insights with each other without starting from scratch every time.

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Features:

  • Data Connectivity: Through an ecosystem of native data connectors and ETL partnerships, the system lets users enrich their dashboards with information from a range of files, databases, drivers, applications and services.
  • Data Engine: The Sisense data engine ingests and processes data where it lives in its warehouse or other infrastructure, resulting in optimized query performance and large-scale data ingestion.
  • Cloud Data Pipelines: With the data engine, users can control when and how often their data is refreshed and what the flow of information looks like, providing visibility and control over their data pipelines with a flexible, low maintenance solution.
  • Machine Learning: Sisense for Cloud Data Teams allows users to train machine learning models using datasets from their database, and then test them on unknown data. Using R and Python, users can build even more advanced machine learning algorithms to extend the capabilities of the platform.
  • Real-Time Data Modeling: Following the “Model-as-You-Go” approach, users can explore both modeled and raw data through ad-hoc analysis without building upfront models. Data teams can query directly from the sources, answering crucial questions at the click of a button by generating custom, on-the-fly reports.
Price: $$$$$
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Company Size Suitability: S M L

TIBCO Data Science

TIBCO Data Science is a platform that combines the capabilities of multiple big data analytics and statistical packages to operationalize machine learning throughout an organization. With flexible authoring and deployment options, users can create and modify workflows and data pipelines. It also provides tools for data modeling, automation and collaboration to help increase the value of a company’s data and accelerate time-to-insight.

TIBCO Data Science

Screenshot from TIBCO Data Science that shows the data modeling feature.

What Makes It Different:

  • End-to-End AI: TIBCO Data Science helps organizations automate processes throughout the life cycle of data mining, fueled by machine learning algorithms.
  • Data Science for All: From the stakeholder to the expert data analyst, the platform enables all users to access insights with intuitive drag-and-drop workflows and more.
  • Foster Cross-Team Collaboration: Through a platform that allows users to interact with data and provide comments, including a Slack-like communication tool, TIBCO Data Science empowers people across departments to work together towards their project goals.
  • Project Management: Users can create workspaces that can be shared with anyone, attaching the workflows, data and plans with a chronological summary of each version of a workflow, the digital equivalent of a paper trail for analytics projects.

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Features:

  • Full Spectrum of Analytics: Featuring a robust collection of machine learning, predictive and text analytics, including over 16,000 advanced analytics functions, the platform allows businesses to manipulate, model and leverage their big data in any number of ways.
  • Data Discovery and Management: With native connectivity to most sources of data, including Apache Hadoop, Spark, Hive, and relational databases, the solution can dynamically index metadata about projects and analyze without moving data. Users can construct complex workflows to clean, blend, transform and prepare data.
  • Machine Learning: Automated analytics models can iteratively learn from data and optimize their performance. Users don’t need to explicitly program their computers to find new patterns and insights, as the platform will learn where to look.
  • In-Cluster Processing: When a user executes a process, the solution optimizes and pushes computations to multiple database systems automatically so that analysts can run their algorithms at scale without moving the data or optimizing their algorithms based on their database logic.
  • Visual Drag-and-Drop Interface: The visual drag-and-drop interface allows users of all skill levels to query their data without requiring knowledge of SQL queries or programming code. It guides users throughout the data science process, from data exploration and transformation to predictive modeling and evaluation.
Price: $$$$$
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Company Size Suitability: S M L

SAS Visual Data Mining and Machine Learning

SAS Visual Data Mining and Machine Learning is a multimodal predictive analytics and machine learning platform that supports end-to-end data mining through both a comprehensive visual and programming interface. With machine learning techniques, it increases productivity through automated analytics tasks. It empowers data scientists of all skill levels to take control together of the entire analytics life cycle with data wrangling, data modeling and model assessment.

SAS Visual Data Mining and Machine Learning

Screenshot of data modeling within SAS Visual Data Mining.

It can be deployed on-site on a server or through the cloud via enterprise hosting, a private or public cloud infrastructure or a platform as a service.

What Makes It Different:

  • Distributed In-memory Processing: Through SAS Viya, the vendor’s proprietary in-memory analytics engine, analytical tasks are chained together as a single, in-memory job without having to reload the data or write out intermediate results to disks. With built-in workload management, the solution provides concurrent access to the same data in memory for a multiuser environment and distributes workload operations across nodes, resulting in faster calculations overall.
  • Ease of Use: Through point-and-click functionality, best practices templates and natural language generation, the solution simplifies the analytics process and ensures consistent understanding across an analytics team.
  • Explore Options: Users can explore multiple approaches with feature-rich blocks and machine learning pipeline, comparing and contrasting them quickly, turning data mining into a visibly accessible process.
  • Code Your Way: Data scientists can use their preferred coding environment, with support for Python, R, Java and Lua languages.
  • Collaboration: SAS VDMML provides a collaborative environment for the sharing of data, code snippets, annotations, and best practices among team members, facilitating clear and consistent communication about methods, results and interpretation.

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Features:

  • Data Preparation: With distributed data management routines, the solution can perform large-scale data profiling of input data sources with intelligent recommendations for variable measurement and role. Users can combine unstructured and structured data in integrated machine learning programs to create new data types. Users can explore, summarize and enhance data in various ways from within Model Studio.
  • Drag-and-Drop Interface: SAS VDMML has an interactive drag-and-drop interface that requires no coding, though coding is still an option. With best-practice templates, users can implement machine learning tasks or use automated modeling to jump right in to data modeling.
  • Automated Modeling: The system automatically recommends the best sets of features for modeling by ranking them to indicate their importance in transforming the data. Visual pipelines, which are editable by users, are dynamically generated from the data. Users can develop models such as decision forests, gradient boosting, neural networks, support vector machines, Bayesian networks and more through modern machine learning algorithms.
  • Model Assessment and Scoring: SAS Visual Data Mining and Machine Learning automatically calculates supervised learning model performance statistics and automatically generates SAS DATA step code for model scoring that can be applied to training, holdout data and new data.
  • Automated Insights: The system can automatically generate insights and reports about projects and models, reducing the learning curve for business analysts. With embedded natural language generation, it makes interpreting reports and deriving value from data easier.
Price: $$$$$
Deployment:
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Company Size Suitability: S M L

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Final Thoughts

Choosing a data mining tool can be difficult, but it doesn’t have to be. You can start off by identifying your requirements – why not check out our handy-dandy business intelligence requirements template checklist here? Gathering your requirements is the first step to finding the right tool for your business. You can then compare vendors and create a short-list based on those requirements. SelectHub’s free software selection platform can help you accomplish all these things and more, so give it a spin

What’s your favorite data mining software tool and why? What are you looking to accomplish with data mining? Feel free to let us know in the comments below!

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Hsing TsengThe 5 Best Data Mining Tools for 2020

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