KNIME Analytics Platform

It is well suited for organizations having day to day advanced statistical procedures requirements. We use ANOVA, multivariate regression using time series modeling and several calibrations in our models for periodic change due to agile macro-sensitive economic forecasts.

Perfect for training of non-expert users. It is well suited for any kind of analytics endeavor. It is appropriate for many information automation tasks.

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.

Good support from the user community, including recipes and templates.

1. Clean the big data and data transformation for data mapping and visualization purposes
2. Perform predictive analytics
3. Perform statistical modelling and analysis
4. It is not good for planning purposes
5. Not good for visualization and explain the business leaders about logic
6. customer segmentation, information retrieval and advanced analytic
7. Can perform risk analysis

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.

KNIME Analytics Platform is easy to install on any Windows, Mac or Linux machine. The KNIME Server product that is currently being replaced by the KNIME Business Hub comes as multiple layers of software and it took us some time to set up the system right for stability. This was made harder by KNIME staff's deeper expertise in setting up the Server in Linux rather than Windows environment. The KNIME Business Hub promises to have a simpler architecture, although currently there is no visibility of a Windows version of the product.

The community hub at https://hub.knime.com/ is a great way to learn through example of other users and utilise the good work of others. Whenever specific questions and problems arise, the community and the KNIME staff are quick to respond queries on the community forum at https://forum.knime.com/. As subscribers to the paid KNIME Server product we also benefit from dedicated user support. So far, the only issue has been KNIME staff's seemingly stronger expertise of the Linux environment rather than Windows VMs that are favoured by our organisation. The Server product will be replaced by the Business Hub product within the next 1-2 years and, at least currently, only Linux option is available, causing some concerns within our IT department.

KNIME Analytics Platform offers a great tradeoff between intuitiveness and simplicity of the user interface and almost limitless flexibility. There are tools that are even easier to adopt by someone new to analytics, but none that would provide the scalability of KNIME when the user skills and application complexity grows.

Integration with other tools is a great strength of KNIME. It connects to most databases and big data platforms and integrates Python well into the overall development. But from the perspective of an individual analyst or developer even more important can be the ability to output the results of a KNIME workflow into BI tools such as Tableau or Power BI. It is often the case that the data in the databases or source files isn't ideally laid out for such analyst tools and business users often lack the skills and tools to prepare datasets independently. KNIME takes the same approach to Tableau Prep in its visual workflow philosophy but has a much richer functionality for data processing and can extend to applications of ML/AI. For that reason, KNIME is the perfect companion to Tableau Desktop. Power Query and Power BI can appear more familiar than KNIME to those with a primarily spreadsheet backgrounds, but their limits become felt quite quickly when datasets grow in size. KNIME also takes the user through more professional data processing concepts such as joins and hence creates transferable skills.