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.
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.
KNIME Analytics Platform is the perfect tool for data loading, transformation, and analytics. The flexibility with Python and R scripting is incredible and replaces many manual data tasks. Every manual data transformation frequently done in Excel should be transformed into a KNIME Analytics Platform flow. In most cases, there is no need for manual steps in data analytics when using KNIME Analytics Platform. The available documentation and training are perfect for starters. The low-code-no-code approach gives unexperienced users a great chance to step up in the data analytics game. KNIME Analytics Platform is an everyday tool in my work life.
Pros
visual data flow creation
huge number of built-in nodes and function
very supportive community
webinars about use-cases and new functions
Cons
report design (a modern BIRT)
Likelihood to Recommend
KNIME Analytics Platform Analytics is always the right tool for repetitive manual work (copy-paste data, use Excel formulas to transform data, export data, and create charts and reports). Creating a KNIME Analytics Platform Flow does not just remove the chance for errors but also provides documentation of the data transformation (the visual flow). All common data types and data sources are supported out of the box.
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.
Internal Audit needs to identify the exceptions in the data to address the risks. These risks could be coming from IT or Business. So, we collect data from various systems on daily basis and analyze the data to discover the exceptions and email them to the respective auditors for tracking and closing them. Almost 90 workflows are automated to run on daily basis. Beyond this, weekly, monthly and quarterly reports are generated and shared with the auditors. KNIME Analytics Platform is also used to prepare management dashboards to understand the risk trends. We also use KNIME Analytics Platform to conduct Data Science activities and identify the trends in the data. In many investigation cases, we need to relate data and identify the issues. We use KNIME Analytics Platform for this activity as well.
Pros
Extraction, Transformation and Loading
Integration with Python
Loading millions of records for analysis
Connectivity with Databases
Job Scheduling
Cons
Managing Date and Time functionality
Compatibility between Sever and AP
Likelihood to Recommend
When you have data in tabular form and it needs to be analyzed, KNIME Analytics Platform is a best fit. If fact many times, my team goes to KNIME Analytics Platform to read the data from table rather than going to a DB tool such as SQL Developer because it is easy to manage and it also avoids fear of changing data at the source system unintentionally. Once the data is retrieved, play around with the data and simply close it. The team do not need to write many varieties of queries to extract the data. Data Apps can be improved, and it is considered one of the critical items. Similarly, there is no easy way to encrypt data and store in the database.
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)
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
As Head of Analytics for Eviden (an Atos company), I have built a strong relationship with KNIME as I wholeheartedly believe in the democratisation of best of breed analytics platforms, of which KNIME is one, that can significantly drive organisations to real business value from their data. It has a shallow learning curve and can drive immediate efficiencies in a short space of time, particularly in the management reporting space for those that are report producers. As the KNIME Analytics Platform is open source, it integrates with other open source libraries and can accelerate organisations to delve into advanced analytics and AI in areas of prediction for example. There isn't a better time than today to unleash this platform across your user base and reap the value of enhanced quality of insights in parallel to increasing data literacy.
Pros
Visual programming as oppose to scripting encourages data analysts to reap deeper insights from their data
Large community contribution in extending the KNIME Analytics Platform into other areas of analytics, e.g. Text Analytics, Predictive Analytics, ML, etc.
Open source with periodic updates ensures it is equipped to deal with the most sophisticated data analytics use case
Cons
User interface has recently been improved to align with good practice on UX
Likelihood to Recommend
The KNIME Analytics Platform can cater for anyone who has a role in analysing data. I am in the process of delivering a series of knowledge shares that will compliment our team of business consultants outside of our Insight Practice to take confidence that their analysis of data can benefit from a) automation of standard client reports, b) deeper insights into the data they are analysing.
The more advanced use of KNIME will continue to be demonstrated to our clients in the areas of a) data wrangling and automation and b) data science.
In our organization we need to frequently query and transform data for various deliverables. KNIME provides a robust set of capabilities to meet all of our data transformation needs.
Pros
KNIME is amazing at data transformation. KNIME contains every node imaginable to transform data in whichever way you need it. It is also a very stable program, reliable, and scales well when it process's large datasets. We reviewed numerous other programs in our organization before going with KNIME, and there were really no other programs that performed to the degree KNIME does. KNIME was a clear winner for us.
Cons
On the DB Query Reader node, it would be helpful if it had a graphical query building and editing interface, like KNIME's competitor platform has. It's not a deal breaker for our organization though as we develop the SQL in other application before importing into KNIME.
Likelihood to Recommend
KNIME is great for simple to complex data transformations. I would recommend it if these types of data transformations are needed. Though if there is very simple data transformation that could be completed with just SQL alone, I would recommended using just that to a colleague.
KNIME Analytics Platform is the central data processing system we use in our consulting work with financial institutions. We have found the flexiaibity, integration with other systems such as SQLite, Python, etc. to be a great advantage. The self documenting nature of the GUI is awesome and eliminates a task no one wants to do (documentation). Access to AI machine learning tools is another bonus we are also exploiting to a great advantage.
Pros
Summarize instrument level financial data with relevant statistics
Map transactions from core extracts to groups of like transactions using rule engines
Machine learning using random forests and other techniques to analyze data and identify correlations for use in predictive models
Fill out sampling data from averages.
Cons
The Excel reader node doesn't always reset. Sometimes the node has to be rebuilt or reconfigured to truely reset the node. This can trip you up if you're not aware of it.
Basic filtering in table view. Sure you just add a filter node, but it would be cool if the data tables worked more like tables in Excel where you can filter as well as sort.
Likelihood to Recommend
KNIME Analytics Platform has vastly improved our effectiveness when working with large data sets. The self documenting GUI allows analysts to focus on what they are trying to accomplish, not complex code syntax. If we were to use traditional tools, like SQL, work would take much longer and it would be more difficult to collaborate both internally and with clients. Since KNIME Analytics Platform is database oriented, some spreadsheet functions are not supported, which is as it should be. For small data sets we often use Excel vlookup and pivot tables in place of KNIME Analytics Platform. If VBA code is requried, we go to KNIME Analytics Platform as we find VBA to be unstable in Excel.
