IBM Watson Analytics vs Pentaho

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Our analysts compared IBM Watson Analytics vs Pentaho based on data from our 400+ point analysis of Business Analytics Tools, user reviews and our own crowdsourced data from our free software selection platform.

IBM Watson Analytics Software Tool
Pentaho Software Tool

Product Basics

IBM Watson Analytics is a software platform that leverages artificial intelligence and machine learning to empower businesses with data-driven insights for improved decision-making. It caters to businesses of all sizes and across various industries seeking to enhance their competitive edge. Benefits include optimized decision-making, increased productivity, cost reduction, and improved customer satisfaction. Popular features encompass predictive and prescriptive analytics, natural language processing, and data visualization. Pricing is determined on a per-user basis, taking into account the number of users, features utilized, and the chosen subscription plan. User experiences suggest that IBM Watson Analytics offers a user-friendly interface and robust capabilities, making it a valuable tool for businesses seeking to harness the power of data analytics. However, it's recommended to explore alternative options and consider factors such as pricing, features, and user reviews to determine the best fit for your specific business needs.

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Pentaho is a data integration and analytics platform that helps businesses extract, transform, analyze, and visualize data from diverse sources. It caters to organizations navigating growing data volumes and seeking insights for informed decision-making. Users praise Pentaho's open-source, free core version, making it a budget-friendly option for beginners or small teams. Its wide range of tools tackles various data challenges, from basic reporting to advanced analytics. Scalability shines for larger datasets, handling complex processing effectively. Additionally, an active community offers valuable support. However, prepare for a steeper learning curve compared to more user-friendly options. Limited documentation can occasionally leave users struggling. Users report encountering bugs and glitches, potentially requiring technical expertise. Be mindful of resource intensiveness, as large-scale operations might demand powerful hardware. While customization options exist, some users crave more flexibility. Overall, Pentaho offers a powerful, free data platform, with trade-offs between its extensive capabilities and ease of use. Consider its strengths and limitations in the context of your specific needs and technical expertise.

Pros
  • Open-source and free
  • Wide range of tools
  • Scalable for large datasets
  • Active community support
  • Integration with various platforms
Cons
  • Steeper learning curve
  • Limited documentation
  • Occasional bugs and glitches
  • Can be resource-intensive
  • Limited customization options
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$500 Monthly
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Tailored to your specific needs
$100 Monthly, Freemium
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Tailored to your specific needs
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Product Assistance

Documentation
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Live Online
Videos
Webinars
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Videos
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Email
Phone
Chat
FAQ
Forum
Knowledge Base
24/7 Live Support
Email
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24/7 Live Support

Product Insights

  • Enrich Data Interactions: Reduced reaction times give users nearly instantaneous access to data. The solution can be trained to prioritize common requests in order to optimize future interactions with users. 
  • Apply and Discover Data: Speedy data discovery helps process millions of data points in a timely manner to get on with the work that matters most. 
  • Anticipate and Prevent Disruptions: Identify potential issues in workflows that can cause long-lasting damage to an organization using AI and pattern identification. 
  • Detect and Mitigate Risks: Security measures like role-based access, single sign-on and advanced learning combine to create a secure system that protects data from theft and leakage. Train the solution to adapt to and follow the latest privacy regulations to preemptively discover compliance breaches. 
  • Democratize Access and Use: Foster data literacy throughout the organization by utilizing employee knowledge combined with industry teachings and empower the whole workforce through AI-augmented learning. 
  • Customize and Teach the System: It learns as it is used to adapt to individual user’s needs and habits through AI, NLP and machine learning. 
  • Gain a Competitive Advantage: Using the data already available to an organization, managers can predict the shape of future trends that will affect their business. It also positions businesses to remain agile in the face of changes. 
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  • High-Level Overviews: Track KPI progress, capitalize on wins or improve upon stagnant growth. 
  • Enhanced Productivity: A code-free design produces a 15x boost in productivity. Execution of Spark or Hadoop jobs in clusters leads to high-performance output. 
  • Big Data Analytics: Integrate with Hadoop and Spark to ensure big data aggregation, preparation and integration, interactive visualization, analysis, and prediction. Blend multiple sources and process data at scale in a visual design environment. 
  • Efficient Data Management: Improve pipeline management for structured and unstructured data using flow orchestration, managed from a single console. Data engineers and analysts can perform automated data integration tasks for onboarding and data prep using templates.  
  • Predictive Analytics: Monitor, evaluate, compare and rebuild predictive models to perform predictive analysis using machine learning algorithms. Maximize model accuracy while in production and choose from a broad selection of evaluation statistics to identify degraded models. Analyze model performance and uncover inadequacies using rich visualizations. 
  • Community-Driven Tools: Use external components or plugins to extend standard functions. Installed on top of the platform, the plugins aid customizations. 
  • Metadata Editor: Automate data ingestion and onboarding, and cleanse and blend to create analytics-ready data models.  
  • Embedded Analytics: Embed real-time reports and dashboards into existing applications, web-based user interfaces and web APIs. Leverage multiple options to embed the application into clients’ systems. Supports multi-tenant deployment with single sign-on and security integration. 
  • Multi-Cloud Support: Deploy in a multi, hybrid or private cloud environment, with a single tool to simplify architecture management. Ensures connectivity to cloud storage and computing in AWS, Google Cloud and Microsoft Azure. Supports bulk loading of cloud data warehouses, including Amazon Redshift, Snowflake and Google BigQuery. 
  • Streaming Analytics: Get constant statistical analysis from data streams, collected from sources such as log files, social media, IoT platforms, telemetry and more. Manage, monitor and record real-time analytics of live streaming data to quickly extract the necessary information. 
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  • Watson Discovery: Watson Discovery, IBM’s advanced AI, offers smart document understanding that lets users visually label text in documents and incorporate structured understanding. 
  • Simplified Predictive Analysis: Automated analysis techniques simplify the roles analysts must play in the data analytics process. Build advanced predictive models with the bare minimum requirements. 
  • Dashboards: Customizable dashboards let users interact with data in real time, as well as collaborate on visualizations. 
  • Speech to Text: Converts spoken words to text as well as written text to audio. 
  • Image Analysis: Analyze visual content such as video or images through machine learning capabilities. 
  • One-Click Visualization: Data discovery and analysis via visualization are accessible with single-click exploration. 
  • Natural Language Processing: Users can perform queries in plain English, and the system will carry on a natural language dialogue to facilitate intuitive searches. 
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  • Data Visualizations: Includes built-in tools and panel configurations. In-memory data caching aids speed-of-thought analysis on large data volumes. Understand and exclude outliers and drill down into supporting reports using visual lasso filtering and zooming.  
  • Data Source: Build interactive analysis reports by using data from CSV files as well as relational and multidimensional data models. 
  • Data Integration: Flexible data ingestion ensures no limitation in terms of data type or source that’s accessible. Provides Extract, Transform, and Load (ETL) capabilities to capture, cleanse and store data using a uniform and consistent format. 
  • Reporting: View interactive reports in dashboards, with different capabilities such as column resizing and sorting, drag-and-drop report design, font selection, unlimited undo and redo functionality, and more. Export formats include HTML, PDF, CSV, Excel and Excel 93-2003. 
  • OLAP Analytics: Mondrian, an open-source business analytics engine, enables interactive data analysis in real time. Build business intelligence solutions as an Online Analytical Processing (OLAP) engine, enabling multidimensional queries against business data using the MDX query language. 
  • Data Modeling: Maps the physical structure of the database into a logical business model and Streamlined Data Refinery (SDR) using a relational data model. Helps augment and blend raw data through a request form to then be published. 
  • Data Transformation: Design transformations and jobs to run with a graphical user interface, executed in XML or in a database repository. A data transformation engine reads, writes and manipulates data to and from various sources. 
  • Role-Based Security: Restricts access to certain portions of a metadata model that are used as a data source. Offers table, column and row-level authorization control. 
  • Mobility: Get immediate access to business analysis at any time using a mobile app for iPad that uses touch navigation technology. 
  • Big Data Sources: Supports more than 15 big data sources such as Microsoft, Google Cloud, Apache Hive, MAPR and more.  
  • Data Model Integration: Integrate third-party models like R, Python, Scala with Spark MLlib and Weka into data flows. 
  • Integrations with Data Processing Distributions: Integrates Hadoop (and its distributions), NoSQL stores, log files, and JSON and XML data formats. 
  • Customization: A highly-customizable, web-based UI and its API integrations ensure complete control over look, feel and function. 
  • Tailored Training: Access architect-level staff with a proven track record of success with hundreds of customers. 
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Product Ranking

#22

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Business Analytics Tools

#12

among all
Business Analytics Tools

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User Sentiment Summary

Great User Sentiment 291 reviews
Great User Sentiment 307 reviews
84%
of users recommend this product

IBM Watson Analytics has a 'great' User Satisfaction Rating of 84% when considering 291 user reviews from 3 recognized software review sites.

81%
of users recommend this product

Pentaho has a 'great' User Satisfaction Rating of 81% when considering 307 user reviews from 4 recognized software review sites.

4.1 (141)
4.0 (47)
n/a
4.2 (39)
4.4 (89)
4.0 (100)
4.1 (61)
4.1 (121)

Synopsis of User Ratings and Reviews

User-Friendly Interface: It has an advanced, intuitive and user-friendly interface, as noted by 61% of reviewers who mention user-interface.
Integration: Around 70% of the users referring to integration state that it enables easy integration with various technologies and data sources.
Implementation: It has a quick and straightforward implementation process, as observed by 89% of reviewers who specify implementation.
Customizable Reports: All the users specifying reports state that it provides highly customizable reporting capabilities.
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Open-source and free core version: Makes Pentaho accessible to individuals and small teams, reducing initial investment costs.
Wide range of tools: Covers various data analysis needs, from basic reporting to advanced analytics, eliminating the need for multiple tools.
Scalable for large datasets: Handles growing data volumes efficiently, ensuring smooth performance for complex analyses.
Active community support: Provides valuable resources and troubleshooting assistance, especially for the open-source version.
Integration with various platforms: Connects seamlessly with existing data sources and BI tools, simplifying data workflows.
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Learning Curve: It has a steep learning curve for beginners, as noted by 50% of the users referring to the learning curve.
Customer Support: Over 60% of reviewers who mention customer support specify that customer support responses often get delayed.
Slow Performance: Some of its functions and features work slowly at times, as stated by 71% of the users who observe performance.
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Steeper learning curve: Compared to user-friendly options, Pentaho's interface and features might require more technical expertise to master.
Limited documentation: While resources exist, some users find the documentation incomplete or outdated, hindering troubleshooting and advanced usage.
Occasional bugs and glitches: Users report encountering bugs and glitches, especially in the open-source version, potentially impacting data analysis workflows.
Resource-intensive: Large-scale data processing and complex analyses can demand powerful hardware, increasing infrastructure costs.
Limited customization options: While customization is possible, some users crave more flexibility and control over the platform's look and feel.
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IBM Watson is an open and multi-cloud platform that helps users automate their AI lifecycle and create powerful models. It is easy to implement and ensures a smooth customer experience through its advanced, intuitive and user-friendly interface. It offers comprehensive integration and reporting capabilities for achieving greater insights. Many reviewers have noted that it works slowly sometimes, affecting overall performance. Also, delayed responses from its customer service team and steep learning curve for beginners can be tedious.

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Reviews of Pentaho paint a picture of a powerful, open-source data platform with both promise and pitfalls. Many users cite its wide range of tools and impressive scalability as major strengths, allowing them to tackle diverse tasks without needing multiple products. "It's a Swiss Army knife for data," one reviewer enthusiastically declared. But this power comes with a caveat – a steeper learning curve compared to more user-friendly options like Tableau. "It's not drag-and-drop intuitive," another user cautioned. Documentation is another point of contention. While some praise the available resources, others lament it as incomplete or outdated, often requiring community forums for troubleshooting. This is where the strong, active community becomes a saving grace – a true differentiator for Pentaho compared to pricier competitors. "The community is like having a built-in support team," a user noted, highlighting the value of shared knowledge and collaboration. However, users also report occasional bugs and glitches, especially in the free Community Edition. This can be a frustration for those seeking enterprise-level stability. And while Pentaho handles large datasets admirably, its resource-intensive nature can demand costly hardware upgrades, a factor to consider against competitors with built-in cloud options. Overall, Pentaho emerges as a versatile platform for those willing to invest time in learning its intricacies. Its open-source nature and powerful toolset make it a budget-friendly choice for startups and data-savvy teams. But for those prioritizing user-friendliness and seamless workflows, alternatives might be more appealing. Ultimately, the choice boils down to balancing Pentaho's strengths and weaknesses against your specific needs and technical expertise.

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