Best data mining software for small and large companies

There is no doubt that our world today is driven by data. People also believe that data is cornerstone of the biggest decisions of modern business, and data mining become an effective and essential tool for businesses of all sizes to do that.

So, how to perform data mining efficiently to get enhanced business performance, increased productivity, and better decision making? Data mining software is the right answer. It will help you gain insights from large volumes of customer data and predict their behaviors in a short period of time.

Below, we will explain you what is data mining? Why do you need data mining and recommend you the 13 best data mining software for small and large companies in 2022.

What is Data Mining and data mining software?

Data mining refers to a process of analysis of massive datasets, including data searching, extracting, and evaluation, wherein data can be textual graphic patterns such as calligraphy, literary and language figures, statistics, etc.

An objective of data mining is to identify hidden relationships, patterns and trends, thereby data scientists can organize these trends into predictive models to make informed business decisions.

In the past, data mining was a manual coding process that allowed people to interpret useful information from raw data. At current, data mining processes have improved significantly. It includes statistics, automated machine learning, interactive data exploration, as well as database systems that transform data into meaningful information.

Data mining software is a software that allows enterprises and other users to extract usable data from a larger set of raw data to find correlation, patterns and anomalies, thereby the companies predict outcomes. Working collaboratively with predictive analytics, data mining software use complex algorithms to solve problems from raw data.

How does data mining work?

Data mining for any kind of industry involves various processes, starting primarily with understanding the business requirements, followed by data collection from multiple sources and finally teams analyze data assets for valuable insights.

Below, we list three main phases in data mining, consisting of: data pre-processing, data mining, and data valuation.

Data pre-processing – Understanding and Preparing the data

Data pre-processing is necessary to understand available resources and the current scenarios of the business as well as identify the objectives and the scope of the data obtained.

So, before data miming, you must collect the data, check it, assemble it and match it to prevent over information and bottlenecks within the organization.

Data mining – Modeling and transformation

After pre-processing the data, the actual process of data mining starts with 5 main steps as follows:

• Anomaly detection: Involving in identifying irregular datasets that can be useful or have some errors therein.
• Dependency modeling: Finding the relationship between different variables or the association rule learning or market basket analysis.
• Clustering: Involving in discovering structures and groups in data sets that look similar.
• Classification: classifying data based on certain parameters.
• Regression: Discovering the relationships between data sets or data to that in a function that can model the data with the least error possible is found out.
• Summarization: This is where you visualize data and generate reports to provide a compact, more meaningful representation of the extracted data.

Data valuation

This is the final step of knowledge discovery from collected data to verify the patterns generated in data mining.

Not all the patterns discovered by data mining algorithms need to be valid. Hence, you must test the data set where the discovered patterns are applied. Then, the resulting output is put into comparison with the desired output.

If the desired standards patterns are met, learned patterns are interpreted and turned into meaningful knowledge. If not, you must reevaluate the results by making the required changes in the pre-processing and data mining stages.

Advantages of data mining software

• Data Mining is an important part for business intelligence and advanced analytics, thereby the business can gain deeper knowledge about their organization, customers, competitors…
• The primary business benefit of data mining software is the enhanced capacity to find hidden patterns, trends, and correlations in data sets. By mixing traditional data analysis with predictive analytics, the knowledge can be used to improve the decision-making and strategic planning of business.
• Also, data mining software especially makes data visualization easier as well as supports interfaces with standard database formats.
• Further, data mining software may support the detection of anomalies in your models and patterns, thereby preventing your system from being compromised.

The 13 Best data mining software for small and large companies in 2022

There are many data mining software and most of them offer more advanced functionalities. Therefore, your choice of data mining software depends on your needs or preferences.

#1 RapidMiner

RapidMiner is a free open-source data science platform which supports all analytics users across the full AI lifecycle. This software features 1500 functions and algorithms for data preparation, machine learning, deep learning, text mining, predictive analytics, fraud detection, and more.

Since 2007, over 1 million professionals and 40,000 organizations in over 150 countries have relied on RapidMiner to bring data science closer to their business.

The drag-and-drop interface and pre-built models of RapidMiner allow non-programmers to intuitively create predictive workflows for specific use cases such as fraud detection and customer churn. Meanwhile, programmers can take advantage of RapidMiner’s R and Python extensions to tailor their data mining.

Furthermore, with RapidMiner you can create easy-to-explain and easy-to-understand visual data mining workflows and also deploy code-containing and code-based models into the platform.

#2 MonkeyLearn

MonkeyLearn is a cloud-based text analytics solution for data mining. This Machine Learning platform especially suitable for Text Analysis, wherein users can easily get actionable data from raw text such as topic or sentiment expressed in texts like tweets, chats, reviews, articles, and more.

In particular, data is automatically classified, extracted and tagged using prebuilt text analysis models, and users can also create their own custom tags and models.

Key benefits of using MonkeyLearn:

• Having a user-friendly interface, thereby you can easily integrate MonkeyLearn with your existing tools to perform data mining in real-time.
• Pre-created models for common use cases in sentiment analysis, topic classification and entity extraction.
• No need of data science or coding knowledge.
• Users also connect their analyzed data to MonkeyLearn Studio, a customizable data visualization dashboard that makes it even easier to detect trends and patterns in your data.

#3 Oracle Data Miner

Oracle Data Miner is an extension to Oracle SQL Developer, a graphical user interface to Oracle Data Mining, a feature of Oracle Database for enabling users to build descriptive and predictive models to: Predict customer behavior, Target best customers, Identify customer retention risks, Identify promising selling opportunities and Detect anomalous behavior.

Oracle Data Miner offers a comprehensive set of in-database algorithms for performing a variety of mining tasks, such as classification, regression, feature extraction, anomaly detection, clustering, and market basket analysis.

This platform generates PL/SQL and SQL scripts and quickly offers an API for building and maintaining models and a family of SQL functions for scoring.

Some advantages of Oracle Data Miner are: no data movement, security, time-saving data preparation administration, ease of data refresh…

This software is ideal for businesses, data analysts, and data scientists to view data and work directly inside the database by using a simple drag-and-drop workflow editor.

#4 SAS Enterprise Miner

SAS Enterprise Miner – a robust data mining software is ideal for optimization, and data mining. This software provides a variety of methodologies and procedures for executing various Analytic capabilities, thereby you can streamline the whole process and evaluate the organization’s demands and goals.

SAS Enterprise Miner comprises Descriptive Modeling to categorize and profile consumers, Predictive Modeling to forecast unknown outcomes, and Prescriptive Modeling to parse, filter, and transform unstructured data.

Further, SAS offers an easy-to-handle GUI, batch processing, high performance, advanced predictions, open-source integration, cloud deployment option, scalable processing, and more. Finally, this platform can eliminate manual rewriting by allowing users to deploy the model automatically and generate scoring code for all stages.

# 5 KNIME

KNIME is a free and Open-Source Data Mining and Machine Learning tool that offers end-to-end data science support for your business and enhances productivity.

This software is user-friendly, open, intuitive and can include everything from modeling to production. A variety of pre-built components thereof also allow for quick modeling without having to write a single line of code.

KNIME is a flexible and scalable platform for processing complicated forms of data and using advanced algorithms thanks to its range of robust extensions. KNIME server is especially useful for team collaboration, management, deployment, and automation.

# 6 Orange

Orange is a powerful platform to perform data analysis and visualization with a clean and open-source data visualization and machine learning. It makes it easy to see data flow and become more productive.

The basic data mining units in Orange are widgets, wherein the File widget reads the data and communicates this data to Data Table widget that shows the data in a spreadsheet. The output of File is connected to the input of Data Table.

Orange has a nicely designed graphical user interface for someone who is not a programmer but wants to execute analytic workflows on machine learning on their dataset.

Orange also allows you to go deeper with hierarchical clustering, heatmaps, decision tree, linear projections, and MDS. Especially, Orange can convert multidimensional data into 2D visualization with great attribute selections and rankings. It also uses different add-ons to mine data from external sources, perform natural processing and text mining. 

# 7 Qlik

Qlik is a platform that uses a scalable and flexible method to handle Analytics and Data Mining. This platform can bridge the gap between insights, data, and action while giving you AI-driven, collaborative, actionable, and real-time data and analytics visualization.

Qlik provides a simple drag-and-drop interface that responds quickly to changes and interactions. Also, Qlik supports a variety of data sources and seamless connections with a variety of application formats via connectors and extensions or a set of APIs.

The platform may help users reduce cost, risk as well as time to deliver an agile cloud data warehouse. With Qlik, you can use push-down, and modern ELT approaches to convert, standardize, enrich, consolidate as well as join data from heterogeneous structures.

#8 Teradata

Teradata‘s multi-cloud platform provides amazing capabilities and engines that unifies everything for enterprise analytics. It offers a hybrid approach to satisfy the demands of a modern enterprise by allowing enterprise data analytics ecosystem, predictive intelligence as well as delivering actionable answers.

With a portable and flexible platform, Teradata helps you to deploy anywhere, such as on-premises and public clouds (Azure, AWS, Google Cloud).

Some advantages thereof are being accomplished by embedding Analytics near to data, removing the need to transport data, and allowing users to run their analytics quicker and more accurately on a larger dataset.

#9 Sisense

Sisense is an API-first analytics platform, which delivers completely customizable and white-labeled analytics whenever you need it.

Going beyond traditional business intelligence, Sisense provides organizations with the ability to infuse analytics everywhere, embedded in both customer and employee applications and workflows. 

Sisense also provides an open-cloud platform which can be extended through tech partnerships in order to enhance scalability. Another outstanding feature of Sisense is its collaboration tools which allow report accessing, monitoring, and sharing – without downloading the file.

With Sisense Fusion – a highly customizable and AI-driven analytics platform, Sisense helps customer breakthrough the barriers of analytics adoption to infuse analytics effectively and make better business decisions.

#10 Zoho Analytics

Zoho Analytics is a self-service business intelligence and analytics software which helps you create dashboards and analyze data. With Zoho, users can expericene an end-to-end business analytics platform, thereby improving decision-making at every layer of their organization, from frontline workers to C-suite executives.

Zoho Analytics is built on a broad market knowledge across over 50 different business app categories, thereby delivering superior, out-of-the-box data modeling designed to process and categorize data from all of the most popular business apps used by enterprise organizations.

Some other advantages of this software comprising:

• Allowing users to construct informative dashboards quickly and graphically evaluate any data.
• Providing over 100 100 ready-to-use connections for major business software, cloud storage, and databases.
• Providing unified business analytics which allows users to analyze data from all of their company systems in one place.
• Providing AI-powered assistant that answers all queries of customer with intelligent and useful responses.

#11 H2O

H2O is an advanced AI Cloud Platform especially designed to simplify and accelerate making, operating and innovating with AI in any environment.

This data mining software is a fully-open source, distributed in-memory machine learning platform with linear scalability. It supports the most widely used statistical & machine learning algorithms including gradient boosted machines, deep learning, generalized linear models, and so on.

H2O provides an intuitive AI AppStore, thereby you can deliver innovative solutions easily to the end-users. At current, H2O is used by over 20,000 organizations for data mining technology. Especially, H2O can help optimize your operations through the delivery of actionable insights, reduced risks, streamlined operations, as well as personalized experiences.

#12 InetSoft

InetSoft is a web-based platform and a very beneficial intelligence tool. With InetSoft, the data analyzing become faster and easier. InetSoft can handle big data projects in a rapid and flexible manner under the MapReduce principles.

Also, it provides customizable and secure data exploration and reporting options as well as facilitates access to organized, semi-structured data and on-premise applications. With the help of Inbuilt Spark platform, InetSoft allows you scale up for massive data sets of users.

Some other advantages of this software comprises: Collaboration & Social BI, Mobile Exploration and Authoring, Interactive Visual Exploration, Self-Contained Extraction, Transformation & Loading (ETL) & Data Storage, Ease of use to deploy and administer, Ease of use for content authors and for content consumers…

#13 Alteryx Analytics

Alteryx Analytics is an open-source platform used for Data analytics which helps in Preparing and analyzing the data. Alteryx Analytics portfolio comprises Alteryx Designer, Alteryx Server and Alteryx Analytics Gallery. It is also estimated as one of the fastest & easiest software to use for data processing, data modeling, and data visualization.

Main benefits of this software comprise: perform advanced analytics, share analysis with decision maker and eliminate redundant work. Below are some advantages of Alteryx Analytics:

• Providing analytics to small and medium-sized businesses.
• Ad Hoc Analysis is possible.
• providing online analytical processing in a timely manner.
• Creating a secure, private studio for analytic apps.
• Run predictive, spatial and statistical analytics with coding.
• Automatically Scheduled Reporting is also included.
• Having a dashboard that may be completely customized.

Conclusion

Data mining software is becoming a critical tool for businesses, especially in making better decisions via revealing hidden relationships and patterns in data. In the era of technology, there are more and more options of data mining software available for customers and choosing the best for your business will depend on your goals and your budget. Hopefully, the 13 best data mining software for small and large companies in 2022 we recommend above will be useful for your choice.