KNIME Analytics Platform Reviews and Ratings

Use Cases and Deployment Scope

Nordax Bank uses the KNIME Analytics Platform to build risk and marketing models.

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.

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.

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.

Use Cases and Deployment Scope

<div>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.

</div><div>We also use it as a reporting tool, creating data applications that various departments can consult and use on a daily basis.</div>

Likelihood to Recommend

<div>Knime is suited for several scenarios:</div><div><ul><li>ETL and Data Science Use Case scenarios for non technical people.</li><li>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...</li><li>Automation of excel processes/reports that require a lot of time and manual interaction</li></ul><div>

</div><div>Knime is less appropiate for:</div><div><ul><li>Reporting capabilities, it's better to connect a reporting tool to it, Knime allows it.</li><li>Productionizing DS/ML models

</li></ul></div></div>

Use Cases and Deployment Scope

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.

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.

Use Cases and Deployment Scope

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.

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.

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.

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.

Use Cases and Deployment Scope

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.

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

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.

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.

Use Cases and Deployment Scope

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.

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.

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.

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.