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
6 Reviews
Professional, Scientific, and Technical ServicesInformation Technology & Services3Management Consulting2Research1
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.
The KNIME Analytics Platform provides a comprehensive set of tools for addressing the data manipulation and data science issues we encounter. Internally we use it for training new data scientists, building awareness of the data science workflow and data manipulation with non-technical staff. We also use it on our own data projects. The no-code environment allows us to focus on the methodology and intent of analyses with novice users without them encountering errors in syntax as they would if they were learning to code at the same time. However, the R and Python nodes allow experienced data scientists to work in their preferred language as well as allowing us to scaffold the learning of new data scientists in those languages when it becomes advantageous. We find non-technical clients will engage with the visual node structure much more than code, which helps us get to a solution more quickly. We can deliver stand-alone solutions to clients with confidence that we are not tying them into an expensive vendor relationship. Clients value that they can give access to all of their users at no licencing cost. Where collaboration and automation is required, KNIME Analytics Platform offer an extremely competitive server solution.
Pros
Connectivity to an array of data sources and joining the data
Rapid prototyping across data science use cases
Making data science explainable to non-experts
Democratising data - KNIME Analytics Platform allows everyone access to powerful analysis techniques
Providing simple access to powerful external data science tools such as H2O and hyperscalers
Cons
The previous UI of KNIME Analytics Platform provided easy access to a wide range of examples which is an extremely valuable resource for understanding how to break down a problem in KNIME Analytics Platform and provide accelerated delivery for similar use cases. Access to these resources doesn't seem possible at the moment in the new UI, but I believe it is being actively worked on. The examples are still available in the platform, but presently you need to switch back to the old UI.
Likelihood to Recommend
KNIME Analytics Platform is excellent for people who are finding Excel frustrating, this can be due to errors creeping in due to manual changes or simply that there are too many calculations which causes the system to slow down and crash. This is especially true for regular reporting where a KNIME Analytics Platform workflow can pull in the most recent data, process it and provide the necessary output in one click. I find KNIME Analytics Platform especially useful when talking with audiences who are intimidated by code. KNIME Analytics Platform allows us to discuss exactly how data is processed and an analysis takes place at an abstracted level where non-technical users are happy to think and communicate which is often essential when they are subject matter experts whom you need for guidance. For experienced programmers KNIME Analytics Platform is a double-edged sword. Often programmers wish to write their own code because they are more efficient working that way and are constrained by having to think and implement work in nodes. However, those constraints forcing development in a "KNIME way" are useful when working in teams and for maintenance compared to some programmers' idiosyncratic styles.
One of our clients has KNIME as a Data Wrangling and Data Science tool for internal data and process automation. KNIME is surprisingly easy to use and very powerful. You can go from automating the a process that reads data from a database and wrights it into a Excel file to a much more complex solution involving Machine Learning and complex Data Science solutions. I'm extremely happy with KNIME and how it bridges the gap between developers and the business users.
Pros
Data transformation
Data conversion
Data Wrangling
Workflow
Cons
Interpreting Excel files and translating it to CSV format
Nodes that do multiple transformations at the same time
Likelihood to Recommend
ETL, TLE and Data Science. KNIME competes toe to toe with other tools like Alteryx. In fact it's more flexible and easier to use. You can be a BA with basic skills in SQL and programing or a senior developer, KNIME will help you develop a easy to understand solution that will be easy to maintain
VU
Verified User
Consultant in Engineering (Information Technology & Services company, 10,001+ employees)
KNIME is used as a bridge piece of software that connects multiple, disparate data sources into a single data pipeline for further analysis downstream. Some level of transformation is done in the processing, mainly for data cleansing, but most of that is left to custom code further on in the pipeline.
Pros
Connection to multiple data sources.
Unified interface for data and cleansing.
Cross platform interoperability.
Cons
Cumbersome UI.
Slow to load.
Memory/CPU hog.
Likelihood to Recommend
KNIME is well suited for the data analyst that has multiple disparate data sources and needs to unify them, with a price point that is lower than some other enterprise packages. It's less well suited for smaller data pipelines or pipelines where a ton of custom coding and modification needs to be made.
I have used KNIME for advanced data analytics and experiments in the AI (machine learning) area. I have also used this platform for running client data analysis in sourcing and sales areas, including running of prediction models. This is a framework that allows you to start with simple tasks and gradually increase the analysis complexity. After going repeatedly through several data sources with tons of data, the painful part has always been preparing and transforming the raw data for analysis. This can be automated and the data acquisition model can be saved and run repeatedly, saving a lot of time. Data cleansing and blending of tables is easy here. It also supports formats as JSON, XML, a quite frequent format nowadays.
Above all the platform and community is wide with hundreds of add-on modules. Frequently, someone has already solved a similar task as you. Before trying to model anything from scratch, it is a good idea to skim through modules and hopefully you can find a good one to use. And finally, it supports simple as well as complex analytics, including AI algorithms.
Pros
Great UX interface, easy connection of data sources, good handling of the analytical model, easy to modify.
It provides good level of control of what happens with your data in each step.
Great tool from data preprocessing, from analysis to visualization.
Great community and a lot of modules to reuse.
Supports machine learning - it is easy to configure and run.
It is Open Source!
If you are familiar with Python, you can use this easy programming language to add additional functions to your analytical model.
Cons
Automation - e.g. RapidMiner Studio provides a Turbo Prep function, where one can get to working on models more quickly (RapidMiner is not open source though)
KNIME does not replace a regular reporting tool - it is not meant to. However, if I have already spent some time developing a data acquisition and analytical model, it would be nice to be able to deploy, for example, a monitoring or reporting module that would process data autonomously and react accordingly.
Likelihood to Recommend
If you are searching for a tool with a low total cost of ownership (TCO), that is easy to understand, and that comprises many prepared modules, KNIME is great. The tool is very intuitive with a lot of examples to learn. You can find a bit better tools, like RapidMiner Studio, but this is a paid, commercial solution. Yes, you can get a free RapidMiner license to process up to 50,000 lines of data, but this is not sufficient for serious work. Most of my use cases today require a bigger license, so KNIME is an attractive alternative price-wise.
We use KNIME Analytics for our client (one of the Big Four). The use of this platform is for NLP related tasks. Specifically for Information Retrieval. It is used by a division within the organisation.
Pros
Text processing is easily performed by the various extensions within this platform
Integrates multiple languages like Python, R , Java etc. all in one place
Also provides many options for text parsing like CoreNLP, OpenNLP
Cons
Documentation is poor
The developers are mostly not native English speakers therefore their verbiage is sometimes ambiguous in the given examples
Likelihood to Recommend
It is good for data ingestion given various formats. Thereafter more time can be dedicated to data analysis and other downstream tasks.
VU
Verified User
Engineer in Information Technology (Information Technology and Services company, 1001-5000 employees)