TrustRadius Insights for KNIME Analytics Platform are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Business Problems Solved
KNIME Analytics Platform has proven to be a valuable tool for a wide range of users and industries. Novice data scientists appreciate the platform's no-code environment, which allows them to focus on the methodology and intent of their analyses without getting bogged down by syntax errors. The intuitive visual node structure is particularly beneficial for non-technical clients who prefer a user-friendly interface over coding, enabling them to find solutions quickly. Experienced data scientists also find value in KNIME Analytics Platform, as it allows them to work in their preferred language using the R and Python nodes.
The platform's flexibility and ability to integrate well with other systems like SQLite and Python make it suitable for consulting work with financial institutions. KNIME Analytics Platform also provides self-documenting capabilities, eliminating the need for manual documentation tasks. Additionally, the platform offers access to AI machine learning tools that prove advantageous for data analysis and finding patterns. It is commonly employed in risk analytics and model development within the banking industry, facilitating univariate and multivariate analysis as well as determining the statistical significance of variables.
Beyond finance, KNIME Analytics Platform finds utility in advanced data analytics and AI experiments. It is used for data analysis in sourcing and sales areas, including running prediction models. The platform allows users to start with simple tasks and gradually increase analysis complexity, making it accessible for organizations of varying skill levels. By automating data preparation and transformation, KNIME Analytics Platform saves precious time in data analysis processes. It supports various data formats like JSON and XML, further enhancing its versatility.
Furthermore, the KNIME community boasts hundreds of add-on modules that provide existing solutions for similar tasks, making it easier to tackle complex projects. With support for both simple and complex analytics, including AI algorithms, the platform caters to diverse analytical needs across industries. For marketing purposes, KNIME Analytics Platform excels at crunching large sets of data by facilitating data manipulation, report creation, and running prediction models. Its efficacy has earned it a reputation as a best-of-breed analytics platform that can drive real business value from data.
Many users commend KNIME Analytics Platform for its shallow learning curve, enabling immediate efficiencies in management reporting. The platform integrates seamlessly with other open-source libraries, empowering users to leverage advanced analytics and AI capabilities. It serves as a bridge between multiple data sources, facilitating data cleansing and transformation. Consequently, KNIME Analytics Platform is widely used across organizations for ETL, data integration, advanced analytics, and customer segmentation.
Notably, KNIME Analytics Platform has emerged as a cost-effective alternative to Alteryx software for many functionalities. Its applications extend beyond data analysis and into internal audit, where it helps identify exceptions in data, generate reports, and prepare management dashboards. With its data science capabilities, KNIME Analytics Platform assists in investigating big data issues and automating processes.
The platform's drag and drop interface and visual management of software code allow users to quickly test concepts and build prototypes of data pipelines, machine learning solutions, and data apps. Fast access to and blending of data from various sources, including databases, APIs, and flat files, is made possible by KNIME Analytics Platform. The wide range of pre-built nodes covering machine learning algorithms, combined with Python integration and shared components, ensures that users have the tools they need to fill any gaps in their workflows.
As organizations strive to go beyond spreadsheets and traditional BI systems, KNIME Analytics Platform fills the gap by providing professional-level data processing and data science capabilities to anyone. It not only offers standalone solutions but also provides collaboration and automation features through the server solution, allowing users to automate tasks and make data apps accessible to anyone within the organization. Whether it's building data science pipelines, automating tasks, or creating self-service analytics platforms, KNIME Analytics Platform proves to be a versatile tool that meets various business needs.
From NLP-related tasks like information retrieval to addressing customer segmentation challenges in marketing departments, KNIME Analytics Platform has become an indispensable tool for organizations across domains. Its powerful capabilities for data transformation make it a robust choice for meeting various data transformation needs within organizations.
The ease of use and power of KNIME Analytics Platform have garnered praise from users who appreciate its ability to automate simple processes or develop complex solutions involving machine learning and data science. With its deep integration with other open-source libraries and its ability to handle large datasets effectively, KNIME Analytics Platform empowers users to drive innovation and extract valuable insights from their data.
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KNIME Analytics Platform Reviews
3 Reviews
Mid-sized Companies (51-1,000 employees)
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Nordax Bank uses the KNIME Analytics Platform to build risk and marketing models.
Pros
Machine learning models
Great support and user examples
Format that allows users to build very flexible workstreams
Cons
An optimization module that allows users to define constraints
Likelihood to Recommend
Well-suited: 1. Machine learning tasks such as credit score and marketing response models 2. Integration with Python, R, and H2O offers great flexibility for users of different backgrounds to collaborate. 3. Share workstreams.
Less Appropriate: 1. Plot capabilities could in my mind be improved. The flexibility Tableau offers would be nice to also have in the KNIME Analytics Platform.
We primarily use KNIME Analytics Platform as our go-to integration and
ETL tool. The platform's makes it easy for our team to connect to diverse data sources, extract relevant information, and transform it into a
format that suits our analytical needs.
We also use it as a reporting tool, creating data applications that various departments can consult and use on a daily basis.
Pros
Seamless Integration with API, DBs, Tabular files
Robust ETL capabilities using or it's No code/Low code nodes
Automatize workflows
Unify ETL, ML and Reporting in the same framework
It's Open Source and has a strong community
Cons
Reporting, the reporting is lacking a lot in terms of customization, is really basic
Integration with Microsoft services
A SaaS option
Likelihood to Recommend
Knime is suited for several scenarios:
ETL and Data Science Use Case scenarios for non technical people.
Data Science Democratization process, as with their new Server option called Business Hub it allows to create several teams within an organization where you can share components, WF, reports...
Automation of excel processes/reports that require a lot of time and manual interaction
Knime is less appropiate for:
Reporting capabilities, it's better to connect a reporting tool to it, Knime allows it.
We use KNIME for three overlapping use cases. (1) With its drag and drop interface and visual management of software code it is a great tool for quick testing of concepts and building prototypes of data pipelines, machine learning solutions and data apps. With KNIME Analytics Platform, it is very fast to access and blend data from various sources including databases, APIs and flat files. KNIME's pre-built nodes cover a range of machine learning algorithms and associated procedures and where they fall short, its Python integration and shared components are likely to cover the gap. (2) As a free and accessible, but yet extremely powerful data tool KNIME Analytics Platform brings professional-level data processing and data science into the hands of anyone who wants to develop data skills beyond spreadsheets and BI systems. As the central analytics team, we can promote the tool to everyone whether they eventually became a user or not, without incremental cost. (3) The commercial product, KNIME Server/Business Hub enables turning the solutions developed in (1) and (2) into automated jobs and data apps accessible to anyone in the organisation.
Pros
Easy access to powerful data wrangling capabilities to business users and citizen data scientists
Simple management of complex analytical processes and user interfaces due to the visual workflow approach
Straight forward integration with Python for additional capabilities
Data Apps (KNIME Server/Business Hub) have the potential of moving self-service analytics and collaboration between business teams from creating and sharing BI dashboards into real applications with complex backends and rich user inputs
Cons
The visualisation nodes that KNIME Analytics Platform offers out-of-the-box lack variety and configuration options to optimise their usability and looks for different use cases. However, the JavaScriptView and PythonView nodes together with the ability of using CSS styling should in principle provide boundless opportunities but are not necessarily accessible for those looking for a No Code/Low Code approach (also, the JavaScript nodes would benefit from similar package management approach to the Python integration). There are some user-driven developments and component nodes available on the KNIME Hub that improve the basic visualisation functionalities, but perhaps this is an area the KNIME team could also focus on with new nodes and components. One way of boosting development could be competitions for the user community focusing on visualisation approaches.
Similarly, and related to the visualisation capabilities, the capabilities for creating Data Apps could be improved. More refined and intuitive user interaction within component views would require additional functionality. It would also be important to have more overall control of the app display and be able to create apps that do not follow the generic flow with standard [Next] and [Close] buttons, to disable the showing of the progress bar (which sometimes weirdly moves backwards rather than forwards) and to generate apps that can use the whole screen with fully customisable backgrounds. The objective should be to enable developing apps that the end-users will find intuitive and familiar based on their experience of mobile and other apps rather than expect users to adapt to certain idiosyncrasies of KNIME Apps.
Likelihood to Recommend
KNIME Analytics Platform is a great productivity enhancing tool for any knowledge worker who wants to replace spreadsheets and VLOOKUPs in managing and blending data with systematic, repeatable procedures. KNIME Analytics Platform enables team managers and others who cannot perform development work and maintain coding skills on a daily basis due other responsibilities to quickly test their ideas and build prototypes. KNIME Analytics Platform is a good way to manage complex solutions even for seasoned coders due to the visual view of the workflow logic. And even if heavy lifting was performed by Python nodes instead of native KNIME nodes, the workflow view enables a citizen data scientist or even a business user to run and troubleshoot workflows independently. KNIME Integrated Deployment is a very innovative way for developing and deploying production workflows. There could be some weaknesses in relation to development work, at least in the soon legacy KNIME Server environment, where it is not possible to collaborate in the way of Git, but multiple team members could be working on the same workflow and deploy updates without knowing of each other.
VU
Verified User
Manager in Research & Development (51-200 employees)
