KNIME blended With R skills Is a great GUI Based Analytics & Mining Tool, Specially for Advanced Statistical Usage
Updated March 25, 2019
KNIME blended With R skills Is a great GUI Based Analytics & Mining Tool, Specially for Advanced Statistical Usage
Score 9 out of 10
Vetted Review
Verified User
Overall Satisfaction with KNIME Analytics Platform
We use KNIME due to its high-value predictive analytics and its ability to find patterns as a data mining tool. Its risk analytics are used in our department for the development of new models and model validation using time series for low default portfolios. Primarily for creating univariate and multivariate analysis and finding statistical significance of variables, and further correlations with a blend of statistical procedures in the banking industry.
- For non-programming based functional users, it's a blessing as it doesn't require coding, programming skills to perform data mining. The full desktop version of KNIME is free and open source, with no limit to data.
- Connect to Open source: It also offers excellent integration with a wide range of other open source software such as Python, R, Spark, and even ImageJ for image analysis.
- Great Integration of functionalities: We never move data between applications/platforms to complete the project. Raw data is easily ingested in the tool, processed, can be performed statistics, summarised and exported to various formats.
- Visualization can be improved further though it has been better with new versions, with a lot of scope available. However, connectivity to Tableau somehow overcomes this. Also, skilled resources are difficult to find for KNIME, due to other solutions having better penetration.
- Knowledge of R/Python is required to fully use the statistical analysis (rather than just data mining). Also, memory usage is a problematic issue sometimes.
- Not enough domain usage experience can be shared between KNIME users as well.
- It is suited for data mining or machine learning work but If we're looking for advanced stat methods such as mixed effects linear/logistics models, that needs to be run through an R node.
- Thinking of our peers with an advanced visualization techniques requirement, it is a lagging product.
We need to use SAS/STAT package within SAS to use the advanced statistical functions, but KNIME has inbuilt libraries for the same. Also, the integration with Open source (Python, R, Java codes) allows better scalability & more availability of skilled resources to work upon.