Home > Big Data Analytics Tools > QlikView > QlikView vs IBM Watson Studio

QlikView vs IBM Watson Studio

Last Updated: November 18th, 2024

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

Overview Pricing Benefits & Features Analyst Ratings Comparison Charts User Ratings Analyst Summary Screenshots

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Product Basics

QlikView is a data discovery and customer insight platform from Qlik, a leader in the insight and intelligence space. However, it is not available for purchase any longer. Qlik Sense, Qlik’s next-generation offering, is available for new customers. It offers self-service data that can help drive decisions and generate significant ROI for technical skill level users.

It’s built from the ground up to be affordable, scalable and adaptable. It can ingest data from diverse sources like big data streams, file-based data, and on-premise or cloud data. It is well-known for its data associations and relationship functionality, keeping data in context automatically. It delivers results quickly via its patented in-memory data processing module, processing data down to as little as 10% of its original size.

Pros

Intuitive interface
Fast data visualization
Easy data exploration
User-friendly for non-technical users
Strong community and support

Cons

Limited data modeling capabilities
Licensing costs can be high
Customization can be challenging
Version control can be a concern
Performance can slow with large datasets

IBM Watson Studio is a powerful platform designed to empower organizations in their data science and machine learning endeavors. It serves as a comprehensive hub for data analysis, model development, and collaboration among teams. Key features include advanced analytics tools, AutoAI for automating machine learning tasks, and a collaborative workspace for seamless teamwork. Users benefit from the ability to create, train, and deploy machine learning models within the platform, simplifying the transition to production environments. Watson Studio also offers data visualization tools for effective communication of insights. Its strengths lie in its versatility, collaboration capabilities, and automation, making it a valuable asset for organizations seeking to harness the potential of data-driven decision-making.

$2,500/User, Annual

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$30 Monthly

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Tailored to your specific needs

Small

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Chromebook

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Cloud

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Product Assistance

Documentation

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Live Online

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Phone

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Knowledge Base

24/7 Live Support

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Product Insights

Optimize Connections: Spot connections in data even when working with large data sets. Take a granular look at data through drill-down and view high-level trends in customer/employee behavior.
Increase Data Security and Control: Ensure total control over sensitive data. Prevent unauthorized access to crucial data sets through user-control, filtered views and permissions. AES-256 GCM provides data integrity and keeps information secured. Grant access to view data using user-specific access.
Enhance Functionality: Build the product in alignment with user needs and specifications. Extend functionality with QlikView Workbench to customize the app with custom scripting.
Increase Insight: Gain unparalleled insights into business operations, as well as the market at large. Track KPIs like sales, budgets, employee performance, and hundreds of other pre-built or custom data points.
Flexible Deployment: Choose from a cloud analytics solution, an on-premise installation, or a combined solution to get desired scalability and flexibility.
Intuitive Interface: Obtain data results via the self-service and code-free visual tool. Its accessible interface provides updated information instantly.
Empower Company Culture: Foster a data-driven culture by empowering employees to share data and insights directly or outside the platform.
Increase Competition: Monitor and track employee performance using KPI dashboards across organizations. Analyze changes in empirical data and make decisions to capitalize the profits.
Automated Recommendation: Receive and recommend automated context-aware suggestions using machine learning (ML). Prioritize recommendations on visualization type for easy access.
Accelerate Business Growth: Scale the businesses by deploying self-service and predictable BI. Test business performance using free tools available on the webpage.
Enhance Accessibility: Access all features from anywhere and on any device using an HTML5 client. Provides mobile push notifications to ensure unrestricted and consistent access.
Worldwide Support: Support fifteen languages and native currency conversions while detecting which language to use according to the browser or operating system.
Optimize Deployment and Configuration: Deploy quickly and allow for even quicker configuration because data is not required to be stored in any silos or cubes.

Advanced Data Analytics: IBM Watson Studio empowers users to perform advanced data analytics and gain deeper insights from their data. It offers a wide range of tools and capabilities for data exploration, transformation, and analysis, enabling data-driven decision-making.
Collaborative Environment: The platform provides a collaborative environment where data scientists, analysts, and stakeholders can work together seamlessly. It facilitates team collaboration, version control, and sharing of insights, fostering a culture of data-driven collaboration.
Machine Learning Capabilities: IBM Watson Studio offers robust machine learning capabilities, allowing users to build, train, and deploy machine learning models. This benefit enables organizations to leverage predictive analytics for a variety of applications, from fraud detection to customer churn prediction.
Model Deployment and Monitoring: Users can easily deploy and monitor machine learning models within the platform. This streamlines the process of putting models into production and ensures they continue to perform effectively over time.
Data Visualization: The platform offers data visualization tools that help users create compelling and informative visualizations. Data can be transformed into clear, interactive charts and graphs, making it easier to communicate insights to stakeholders.
Integration Capabilities: IBM Watson Studio integrates with a wide range of data sources, databases, and other IBM services. This flexibility enables organizations to work with their existing data ecosystem and technology stack, enhancing efficiency and productivity.
AutoAI: The AutoAI feature automates the machine learning pipeline, making it accessible to users with varying levels of expertise. It simplifies model development and accelerates the time-to-value for AI projects.
Scalability: IBM Watson Studio is designed to handle large-scale data projects. It scales to accommodate growing datasets and computational needs, ensuring that it remains a reliable solution as organizations expand their analytics initiatives.
Security and Compliance: The platform prioritizes data security and compliance with industry standards and regulations. It includes features like data access controls and audit trails to safeguard sensitive information.
Cost-Efficiency: By providing a comprehensive suite of data science and machine learning tools in one platform, IBM Watson Studio helps organizations optimize their resources and reduce the cost of managing multiple separate tools and platforms.

Direct Data Source Connection: Connect to almost any data source, including cloud, big data, file-based and on-premise data. Pull information from many services (Salesforce, Hive, Teradata) and combine intel seamlessly into unique and intuitive dashboards.
Intelligent Visualization: Offer interactive displays and represent data in multiple ways for better data analysis. Flexible visualizations allow users to change and adjust graphics according to screen size.
Enterprise Collaboration: Facilitate collaboration for users to share the same dashboard, look at the same view or track one another as they navigate the application.
Strong Associations: Leverage the strength of the platform’s built-in association engine to conduct direct and indirect searches across data or within a single field. Identify data that is related and not associated.
Self-Service App Building: Build apps and files via the drag-and-drop function. Create individual lists with their visualization while managing and sharing across organizations.
Associative Indexing: Combine, transform and ingest data from multiple sources. Gathers data and indexes it to find logical associations. Explore and search big data repositories freely while keeping data intact.
Interactive Dashboards: Provide visualization capabilities and improve interaction using tooltip, lasso selection, filtering and drill-down functions. Encourage viewers to explore data by creating smart dashboards and distributing them using interactive elements.
In-Memory Application: House the software in memory, so conversions, queries and searches happen quicker and more efficiently. Eliminate problems that traditionally plague slow, on-disk applications. Locate all data in RAM.
Web Connectors: Extract data from multiple social networking sites and web-based sources using web APIs. Built-in connectors easily connect to any URL and fetch data.
Robust Data Controls: Enable meaningful data manipulation within the application by leveraging unique dashboards, reports and filter views.
Data Alerts: Spot anomalies and outliers by requesting context-aware alerts. Monitor and manage data without limitations.

Data Preparation Tools: IBM Watson Studio offers a range of data preparation tools that enable users to clean, transform, and shape data for analysis. These tools simplify the data preprocessing stage, ensuring that data is in the right format for analysis.
Collaborative Environment: The platform provides a collaborative workspace where data scientists, analysts, and business stakeholders can work together. It supports version control, project sharing, and real-time collaboration, enhancing teamwork and knowledge sharing.
AutoAI: AutoAI is a feature that automates the machine learning pipeline. It automates tasks such as feature engineering, model selection, and hyperparameter tuning, making it easier for users to build and deploy machine learning models without extensive manual work.
Model Building and Training: IBM Watson Studio includes tools for building and training machine learning models. Users can access a wide range of algorithms and frameworks, allowing them to create predictive models for various applications.
Data Visualization: The platform offers data visualization tools that help users create interactive charts and graphs. These visualizations make it easy to communicate insights and patterns in the data to both technical and non-technical stakeholders.
Deployment and Monitoring: Users can deploy machine learning models into production environments directly from the platform. Additionally, IBM Watson Studio provides monitoring capabilities to track model performance and make adjustments as needed.
Integration: The platform offers seamless integration with various data sources, databases, and cloud services. This ensures that users can access and analyze data from a wide range of systems, enhancing data availability and flexibility.
Security and Compliance: IBM Watson Studio prioritizes data security and compliance. It includes features like access controls, encryption, and audit trails to protect sensitive data and maintain compliance with industry regulations.
Customization and Extensibility: Users can customize and extend the platform's functionality using open APIs and integration options. This flexibility allows organizations to tailor IBM Watson Studio to their specific needs and workflows.
AutoML: AutoML capabilities automate the machine learning process, making it accessible to users with varying levels of expertise. It simplifies model development and accelerates the time-to-value for AI and machine learning projects.

Product Ranking

#32

among all
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#54

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Analyst Rating Summary

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User Sentiment Summary

82%

of users recommend this product

QlikView has a 'great' User Satisfaction Rating of 82% when considering 1859 user reviews from 4 recognized software review sites.

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Synopsis of User Ratings and Reviews

Data Visualization: Approximately 80% of users who review its data visualization capabilities are satisfied with its intuitive drag-and-drop feature, rich libraries and its range of aesthetically appealing data representation options.

Data Preparation: Of users who mention data processing, 83% appreciate the platform’s seemingly limitless data transformation capabilities that help them deep-dive into all possible data relationships to glean actionable insights.

Functionality: Among users who share their views on this platform, around 68% say that they are satisfied with the power of its associative query engine that enables faster on-the-fly calculations and analytics aggregation at the speed of thought.

Sharing and Collaboration: About 83% of users who comment on sharing capabilities appreciate its multi-tier permissions capabilities and easy sharing of reports with clients via external sharing options.

Setup: Around 66% of users who mention ease of setup say that QlikView has a fast implementation cycle.

Advanced Analytics: Users appreciate the platform's robust data analytics and modeling capabilities, allowing them to extract meaningful insights from their data.

Collaboration: Watson Studio's collaborative environment is well-received, enabling teams to work together effectively on data science projects.

AutoAI: Users value the AutoAI feature, which automates machine learning tasks and accelerates model development, making it accessible to users with varying skill levels.

Data Visualization: The platform's data visualization tools help users create informative visualizations, simplifying the communication of insights to stakeholders.

Model Deployment: Users find it convenient to deploy machine learning models within the platform, streamlining the process of putting models into production.

Integration: Watson Studio's integration capabilities with various data sources and services receive praise for their flexibility and ease of use.

Security: Users appreciate the platform's robust security features, ensuring the protection of sensitive data and compliance with regulations.

Customization: Watson Studio's customization options allow users to tailor the platform to their specific needs and workflows, enhancing its adaptability.

Community Support: Many users benefit from the active and helpful user community, which provides resources and assistance for problem-solving and knowledge sharing.

Documentation: IBM's comprehensive documentation is seen as a valuable resource, aiding users in effectively utilizing the platform's features.

Cost: Pricing plans are inflexible and can be cost-prohibitive for small organizations and startups, though large organizations may find that it offers high value, as stated by 93% of users who mention its cost.

Performance: Approximately 42% of users say that performance-wise, this platform is resource-hungry and liable to slow down when crunching large amounts of data on local machines.

User Interface and Graphics: Of users who mention user interface, around 44% say that it needs improvement in deep-dive capabilities, as well as its quality of graphics.

Reporting: Of users who mention reporting, approximately 46% say that it lacks ad-hoc reporting and built-in reporting capabilities, requiring paid plugins to enhance the graphics quality of reports.

Complexity: Some users find the platform complex, especially for beginners in data science, which may require a steep learning curve.

Resource Demands: Handling large datasets and complex analyses can be resource-intensive, posing challenges for organizations with limited computational resources.

Data Quality Dependency: The effectiveness of Watson Studio relies heavily on the quality and cleanliness of input data. Inaccurate or incomplete data can impact analysis outcomes.

Interpretability Challenges: Highly complex machine learning models can be challenging to interpret fully, especially in regulated industries where interpretability is crucial.

Integration Efforts: Integrating Watson Studio into existing IT environments can require significant effort, particularly for organizations with complex tech stacks.

Customization Complexity: Extensive customization may demand advanced knowledge and development skills, potentially limiting accessibility for some users.

Scalability Management: While scalable, effectively managing scaling processes, especially for large enterprises, can be complex and require specialized expertise.

Documentation Gaps: Users have reported occasional gaps in documentation and support resources, which can hinder troubleshooting and development efforts.

Model Deployment Challenges: Deploying models in production environments, particularly in highly regulated industries, can require additional considerations and expertise, posing challenges.

Algorithm Selection: Choosing the right algorithm for specific use cases can be challenging, demanding a deep understanding of the platform and algorithm nuances.

QlikView is one of the foremost BI solutions in the market today, mainly due to the power of its associative query engine to link data from multiple sources that drives its visually impressive dashboards. With its strong data visualization capabilities, users can perform search and filter through data on-the-fly and conduct deep-dives to glean insights that matter to them. With a fast setup, users can have their first data model up and running in very little time. The software resides in-memory and houses data in RAM for quicker retrieval. With multi-tier access permissions for in-organization users, it enables users to view executive summaries at a glance, while allowing them to drill-down into data to find out more. Sadly, Qlik is now scaling back on improvements and updates for QlikView and focusing on promoting QlikSense instead, a possible reason why its filter and search functions, ad-hoc reporting and graphics are lagging in terms of quality, as mentioned in many user reviews. Also, this platform can prove to be resource-heavy for databases housed on local machines, especially when performing batch update jobs. In addition to inflexible pricing plans and the cost of licensing, quite a few necessary add-ons are paid. In summary, QlikView is one of the leading in-memory BI tools available in the market today and rates excellently with users in terms of data aggregation and visualization capabilities; however, buyers should factor in its pricing plans and other limitations when searching for the perfect BI solution for their enterprise.

User reviews of IBM Watson Studio provide valuable insights into its strengths and weaknesses. The platform is lauded for its advanced analytics capabilities, allowing users to conduct in-depth data analysis and modeling. Collaboration features are appreciated for enabling effective teamwork, fostering knowledge sharing among data scientists, analysts, and stakeholders. AutoAI is a standout feature, automating machine learning tasks and making it accessible to users with varying skill levels. Users find the data visualization tools helpful for creating compelling visualizations that communicate insights effectively. Model deployment within the platform simplifies the transition from development to production environments. On the downside, complexity is cited as a drawback, particularly for newcomers to data science. Resource demands for handling large datasets can be challenging for organizations with limited computational resources. The platform's effectiveness is highly dependent on data quality, which can pose issues with inaccurate or incomplete data. Some users note challenges in interpreting highly complex machine learning models, especially in regulated industries where model transparency is crucial. Integration and customization efforts may be complex and require advanced expertise. In comparison to similar products, IBM Watson Studio is often seen as a robust contender, offering a comprehensive suite of data science and machine learning tools. However, the learning curve and resource requirements may be factors for consideration. User reviews reflect a mix of praise for its capabilities and challenges in mastering its advanced functionalities.

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