#1 most frequent
United States of America
50.6%43 installations of 85
What is KNIME Analytics Platform?
KNIME enables users to analyze, upskill, and scale data science without any coding. The platform that lets users blend, transform, model and visualize data, deploy and monitor analytical models, and share insights organization-wide with data apps and services.
Categories & Use Cases
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Screenshot of the KNIME Modern UI. This is the the new user interface for the KNIME Analytics Platform that is available with improved look and feel as the default interface, from KNIME Analytics Platform version 5.1.0 release.
Top Performing Features
Extend Existing Data Sources
Use R or Python to create custom connectors for any APIs or databases
Category average: 8.9
Connect to Multiple Data Sources
Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion
Category average: 8.8
Data Transformations
Use visual tools for standard transformations
Category average: 9.1
Areas for Improvement
Built-in Processors
Library of processors for data quality checks
Category average: 9
Security, Governance, and Cost Controls
Built-in controls to mitigate compliance and audit risk with user activity tracking
Category average: 8.6
Self-Service Model Delivery
Multiple model delivery modes to comply with existing workflows
Category average: 8.3
Who Buys & Uses KNIME Analytics Platform
#2 most frequent
Germany
11.8%10 installations of 85
#3 most frequent
Brazil
8.2%7 installations of 85
An incredibly comprehensive and powerful data analytics platform that is somehow free
Use Cases and Deployment Scope
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.
Return on Investment
- KNIME Analytics Platform is free and our investment in training time has been paid back many times over when using the software for rapid prototyping and implementing our own analysis
- On one occasion we deployed KNIME Analytics Platform and Python with a client. Due to their secure environment, it was prohibitively expensive in time and capital to add Python packages to account for additional requirements as the project progressed and we switched entirely to KNIME Analytics Platform due it its comprehensive set of features. Without KNIME Analytics Platform the project would have been halted and resulted in a loss of >£40k in revenue.
Alternatives Considered
Alteryx, RapidMiner, Dataiku DSS, IBM SPSS Statistics and Anaconda
Other Software Used
Microsoft Power BI
Usability
KNIME Analytics Platform Makes Life Easier and More Fun
Use Cases and Deployment Scope
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.
Return on Investment
- Signficantly reduced training time for new anaysts. This is due to the self documenting nature of the GUI.
- Improved turnaround time on standardized client work.
- Reduced time spent in development since the focus can be on what is being done, not the syntax of the code.
Alternatives Considered
SQLite, Microsoft Excel and Microsoft Access
Other Software Used
Microsoft Office 365, HubSpot CRM, ClickUp, Google Drive, Adobe Acrobat
The Swiss army knife for data jobs
Use Cases and Deployment Scope
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)
Return on Investment
- I have no numbers, but I know how much time I save after I created a flow and rerun it without manual inputs and steps needed
Other Software Used
Microsoft Power BI, Tableau Desktop
KNIME Analytics Platform is for everyone, with little to no experience needed!
Use Cases and Deployment Scope
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.
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
Return on Investment
- High ROI
- Time savings through report automation
- the visual programming capability allows for wider team contribution from SMEs as they understand the construct of the analysis. Inclusivity from this perspective is key.
Other Software Used
Microsoft Excel, Microsoft Visual Studio Code, Microsoft Power BI
KNIME Analytics Platform User Review
Use Cases and Deployment Scope
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
Other Software Used
IBM SPSS Statistics, Tableau Desktop
Return on Investment
- Significantly reduced time from modeling to production
- Increased sales with more accurate response models
- Shallow learning curve and free access to web-based tutorials
