Data mining tools focus on extracting value from all the bits and bytes that fill enterprise databases and stream across various computing devices.
These data analytics tools are considered a subset of the larger universe of Big Data, and they are valuable tools for organizations looking to gain insights into a complex array of events. When data mining is used effectively, organizations discover patterns from various forms of data, both structured and unstructured. The results, using data mining’s analysis and visualization features (increasingly tied into machine learning), guides decision-making in industries such as agriculture, medicine, retailing, and financial services.
The market for data mining tool is red hot: a study from ReportLinker noted that the market will top $1 billion in sales by 2023, up from $591 million in 2018.
What should you know when selecting a data mining tool for your business?
In a nutshell, it’s simplicity vs. the power of the tool. Today’s solutions are not only more powerful, they are much easier to use than their earlier versions. There’s a growing use of drag-and-drop interfaces, pre-built templates and pre-designed algorithms that simplify tasks previously requiring sophisticated programming and computer science skills.
Yet sometimes, as these tools simplify the interface, the depth of the resulting “answers” is also lessened. So look at any data mining tool you’re considering: is it designed more for the average sales rep looking for fast answers, or a dedicated data scientists looking to tackle more complicated tasks?
One more thing: Understanding various features, products and options is critical in choosing the right solution. As with any category of products, vendors all have their own approach and, sometimes, their own terminology and workflows. So be aware of “terminology confusion” and don’t hesitate to ask a vendor if one term is analogous to another.
Datamation has sorted through lists of features, studied analyst reviews, examined customer testimonials and plugged in independent research to identify eight top vendors in the data mining and BI marketplace. Sources for this guide include: Forrester Wave, Gartner Magic Quadrant (MQ) for Analytics and Business Intelligence Platforms, Gartner Magic Quadrant (MQ) for Data Science and Machine Learning Platforms, Gartner Peer Insights, and vendor web sites and other online sources.
- Zoho Analytics
- IBM Cognos Analytics
- Microsoft Power BI
- Oracle Business Intelligence
- Salesforce Einstein Analytics Cloud
- SAP Business Objects
Focusing on ease of use – a particularly key attribute as data tools grow – Zoho Analytics is a self service option. Meaning that users will not need the assistance of IT staff or professional data scientists to glean insight from data. Notably, the Zoho data software has an intuitive drag and drop interface, as well as a classic spreadsheet-style interface.
Recognizing today’s diverse data sources, Zoho Analytics allows connection to a broad array of data repositories. These include files stored locally, in cloud drives, many key business applications, databases, and even your company’s own custom-built applications. After a query, results can be viewed in charts, tabular views, standard dashboards, and KPI visualizations.
Zoho is a good choice for businesses that need to provide convenient, accessible data analytics insight to staffers at every level.
IBM Cognos Analytics
IBM is among the most trusted names in data science – a big plus for this data mining tool. Big Blue’s flagship data product, Cognos Analytics, has emerged as a top contender in the world of data mining and BI. It is billed as an all-in-one solution.
IBM Cognos Analytics delivers guided data discovery, automated predictive analytics, and cognitive capabilities. Data preparation functions include: the ability to import numerous file formats, including spreadsheets and CSV files; data source search using natural language; and tools to simplify verifying and combining data sources using automated modeling.
However, the platform also offers visual data exploration tools, including a smart visualization feature that recommends the best format or chart; and dashboards that offer deep and rich reporting capabilities. It includes scheduling and alerting functions. Gartner ranks IBM Cognos a “Visionary” in its MQ.
Microsoft Power BI
In recent years, Microsoft has transformed itself into a premier provider of enterprise applications and solutions. Microsoft Power BI falls in line with this approach. It is a robust data science platform that supports a wide range of activities, including data preparation, data discovery, and augmented analytics.
The platform runs as a SaaS option in the Azure cloud or as an on-premises solution, Power BI Report Server (which allows users to share reports but not dashboards). Both formats include data aggregation and data discovery functions, including support for Salesforce, Microsoft Dynamics, Azure Blog Storage and more.
They support mobile devices, and even Microsoft’s mobile augmented reality technology, HoloLens. Microsoft offers the added appeal of being a low-cost provider in the data mining and analytics world. Gartner ranks the company a “Leader” in its MQ.
Oracle Business Intelligence
Oracle Business Intelligence 12c provides an integrated suite that supports eight distinct platforms spanning big data requirements. Together or apart, components in the suite supports self-service data discovery and visualizations, data lifecycle management and a variety of analytics tools for different needs and purposes.
The platform is designed to operate without the need for IT management. It offers a unified dashboard and supports custom reporting along with the ability to distribute and share reports. It’s available in cloud, hybrid and on-premises versions. Gartner ranks Oracle as a “Niche Player” in its Analytics and Business Intelligence Platforms MQ.
In addition, the company offers Oracle Data Mining (ODM), a discreet tool that operates within Oracle Advanced Analytics. It delivers data collection, validation and predictive modeling capabilities within Oracle Database.
Data discovery, business intelligence and analytics are at the center of Qlik’s platform. The vendor, which touts the concept of “democratized data,” offers powerful tools for handling a wide variety of data mining, predictive analytics and other data science tasks.
The platform is available in both on-premises and cloud versions. Both include robust tools for building analytics apps and visualizations that tap machine learning and AI to generate suggestions and recommendations. Qlik is designed for those lacking data science skills. It accommodates a wide variety of data formats, includes powerful data discovery capabilities and offers particularly strong geo-analytics capabilities. What’s more, the vendor’s network of integrators and partners is vast.
Qlik boasts more than 1,700 partners worldwide. Gartner ranks the company a “Leader” in its MQ. It praised the company’s commitment to innovation and cited its strong community of users as positives.
RapidMiner has emerged as a strong performer in data sciences by delivering a robust suite of solutions that tackle a wide array of tasks. The data mining platform offers a drag-and-drop interface as well as integrated tools that address data preparation, machine learning, deep learning, text mining, and predictive analytics.
RapidMiner Studio helps users connect to data regardless of the format or where it resides. This includes pre-built templates for common use cases such as customer churn and predictive maintenance. In addition, the platform handles repeatable data prep and extract, transform, load (ETL) processes through automation and scheduling functions.
Finally, RapidMiner allows users to explore data interactively to determine health, completeness and overall quality. RapidMiner studio includes a library of more than 1,500 machine learning algorithms and includes wizards that make recommendations based on data and usage patterns. RapidMiner is ranked as a “Leader” in both the Gartner MQ and the Forrester Wave.
Salesforce Einstein Analytics
The enormous popularity of Salesforce makes it a wise data mining and analytics choice for many organizations. The platform supports data mining for sales, service, marketing, event monitoring and other categories. It delivers predictive analytics by pulling data from a Salesforce database—though it supports data import from outside sources as well.
Einstein Discovery uses AI to spot relevant and meaningful patterns without a need to build sophisticated models from scratch. The platform comes with 15 prebuilt dashboards with views into 30 events. The platform is also appealing to users because it handles Salesforce actions and events from within the analytics program.
This includes functionality supporting data quality, new campaigns and targeted outreach. Gartner ranked the vendor a “Visionary” in its MQ. It noted that the platform’s ability to embed analytics content within the overall framework is a particular strength, along with a robust partner ecosystem that includes ETL, data science and machine learning vendors.
SAP BusinessObjects is available as either an on-premises solution or a cloud solution. Both are heavily tilted to data mining, analytics and other data science functions using SAP enterprise applications, including the vendor’s popular enterprise resource planning (ERP) software. The unified platform tackles BI and predictive analytics with a large and growing library of pre-built content.
SAP BusinessObjects focuses on modules designed for specific vertical industries and a variety of business functions. The platform supports machine learning and delivers strong predictive analytics tools. The vendor focuses on a self-service framework that encourages non-data scientists to aggregate, combine and analyze data.
SAP BusinessObjects supports robust cross-enterprise sharing and role-based dashboards. SAP offers integrated security features and support for GDPR and other regulatory requirements. Gartner ranks SAP a “Visionary” in its MQ.
Tableau promotes its data mining and business intelligence framework as a straightforward and powerful solution for non-data scientists. It requires no coding or technical expertise. The vendor offers an intuitive, visually-oriented drag-and-drop interface that connects to numerous data sources, including AWS, Amazon Redshift, Google BigQuery, Microsoft Excel, SAP and Salesforce.
Tableau offers its data science solution in three formats: Tableau Desktop, Tableau Server and Tableau Online. The latter is a cloud-based solution. All support strong content discovery, governance, data preparation, data access and collaboration. The platform supports multiple devices, including desktop PCs and laptops using a browser, mobile devices and embedded capabilities. Gartner ranks the vendor a “Leader” and describes it as a “gold standard” for interactive visual exploration. It ranks near the top for customer scores and overall satisfaction.
|Vendor and features||IBM Cognos||Microsoft Power BI||Oracle Business Intelligence||Qlik||RapidMiner||Salesforce Einstein Analytics Cloud||SAP Business Objects||Tableau|
|Focus||Strategic management through guided data discovery.||Wide ranging functionality in an easy-to-use package.||Designed to work seamlessly within the Oracle product universe.||Data science for everyone.||All-in-one platform with a comprehensive set of tools and features||Designed to extract data from Salesforce CRM and sales and marketing data sources.||Powerful platform designed to work primarily within the SAP universe.||Unified data science platform designed for all levels of users.|
|Key features||Strong ETL and reporting tools. Powerful machine learning and AI. Contextualized smart searching and data modeling based on keywords and customized requirements.||Connects to numerous data sources. Highly scalable dashboards, strong filters and strong reporting and visualizations. Connects to Microsoft’s HoloLens.||Tightly integrated with Oracle databases and applications. Supports Hadoop and other open source formats. Strong collaborative tools and includes mobile tools.||Scalable in-memory engine that handles data extraction, prep and data management.||Offers pre-defined machine learning libraries but connects to numerous third-party libraries. Runs ETL directly within the database. Supports MySQL, PostgreSQL and Google Big Query.||Powerful machine learning and AI. Robust SDK. A strong partner ecosystem provides additional ETL, data science and machine learning capabilities.||Integrates with SAP products seamlessly. Also integrates with Microsoft SharePoint and Java. Supports all major device types, including mobile. Strong reporting and visualizations.||Powerful drill-down and visualization features. Live and in-memory data processing. Connects to numerous platforms and data types that span structured and unstructured data. Strong mobile support.|
|User comments||Mature platform that offers a high level of flexibility. Some concerns about stability and self-service functions.||User friendly and extremely powerful. Some difficulties with more advanced visualizations.||Powerful and flexible platform. But it can be complex. Some describe it as “old school” and cite infrastructure challenges. Some also complain the platform is pricy.||Highly favorable reviews. Flexible and intuitive. Great ETL features. Some challenges with global deployment.||Powerful and flexible, very fast; offers advanced features. Accessible to non-data scientists but can present a learning curve.||High customer ratings. Powerful and efficient at a reasonable price. Some complaints about limited built-in ETL tools and navigation.||Overall high marks but some comments about too much of a legacy oriented focus. Some complaints about usability.||Among the highest rated at Gartner Peer Review. Powerful, easy-to-use, seamless integration with other products. A few complain that the software requires additional configuration for maximum results.|
|Pricing and licensing||Trail version of the premium edition is free. Premium edition running in a multi-tenant cloud costs $70 per user per month. Enterprise edition cost available directly from the company.||Desktop version is free for individual users. $9.99 per month per user. Power BI Premium, which acts as a virtual server in the cloud, starts at a cost of $4.995 per month, though the price varies, depending on scalability and usage requirements.||Free trial. Per user licensing starts at $3,675 per year. The Oracle Foundation Suite pre-processor license costs more.||A Cloud Basic version of Click Sense is free. Cloud Business option is $15 per month per user. QlikView offers a Personal Edition for free with unlimited access. Enterprise edition pricing is available from the vendor.||Small version costs $2,500 per year per user. Medium version costs $5,000 per user per year. Large version runs $10,000 per user per year.||Einstein Predictions costs $75 per user per month. Einstein Recommendations costs between $1,250 and $3,750 per user per month (higher for a customized version). Einstein Social Studio add-on runs $1,250 per user per month and up. Other components vary.||SAP does not provide pricing information publicly.||Tableau Creator, built for individual analysts and power users, starts at $70 per month per user. Tableau Explorer offers a governance self-service model for $35 per user per month. Tableau Viewer, for those that require only visualizations and reports, starts at $12 per user per month.|