Share your experience using OpenText Intelligent Classification

I primarily use KNIME for ETL, extracting data from different sources. I extract data from endpoints of Drupal created for me by developers, then transfer this data into Oracle. After extracting, I create a model in Oracle with ETL, which is used by Power BI. Following this, I create a star schema of the data.

KNIME is simple and allows for fast project development due to its reusability. I appreciate the ability to make improvements or modifications in existing workflows. Although I have not yet used the forecasting and customer profiling features, I find them promising. 

Another effective feature is the ability to use GET request objects to retrieve data from websites or APIs. This makes iterative steps easy to manage. It is more elastic and modern compared to SAP Data Services, allowing node creation and regrouping components or steps for reuse in different projects.

I have seen the potential to interact with Python, which is currently a bit limited. I am interested in the newer version, 5.4, when it becomes available. The machine learning and profileration aspects are fascinating and align with my academic background in statistics.

I have been working with KNIME for almost five years now.

Occasionally, when using the GET object, there might be issues due to the velocity of the lines or the IT system of the commission. Overall, stability is not a significant concern.

I have not encountered any scalability limitations with KNIME at the moment.

I contacted their technical support around five times. While they cannot always provide immediate answers, they are generally efficient and simplify tasks, especially in the initial phase of learning KNIME.

Neutral

I also worked with Power BI and BusinessObjects and have experience with typical data services access.

The initial setup was straightforward, taking between half an hour and an hour depending on the data entity.

I use SAP Data Services as well, but I find KNIME more elastic and modern.

I am impressed by the modularity and reusability in KNIME, especially the ability to make small adjustments to object configurations. I am interested in its interaction with Python and machine learning aspects. Also, I recommend KNIME to others as I face difficulty finding reasons not to. My overall rating for KNIME is between nine and ten.

On-premises

I use SAS Analytics in my work to analyze data, such as selecting credits for traditional securitization. Recently, I used SAS Analytics for the project roster from Santander, selecting and evaluating credits, and conducting historical research on the reception of contracts to issue securitization. I don't use machine learning models but focus on analytics processes and report creation using macros.

The most valuable aspect of SAS Analytics is the ability to automate work using macros without heavily relying on technology teams. By leveraging macros, I can create functions that operate independently, streamlining workflows efficiently. This capability enhances productivity by reducing the need for manual intervention and technical support.

SAS Analytics could improve in areas such as handling stress tests and workloads, as well as predicting financial movements, specifically profit and loss movements.

I have 12 years of experience with SAS Analytics.

I have used Orange before, which I found had better visualization capabilities. With Orange, everything is visible and understandable, whereas SAS Analytics requires more programming.

The setup for SAS Analytics is complex, particularly when using the enterprise guide. However, combining SAS Analytics with Python simplifies the process since Python acts as a co-pilot to manage data and models.

On a scale of one to ten, SAS Analytics is about nine in terms of expense.

I evaluated Orange, which offers superior visualization, but SAS Analytics involves more programming.

Overall, I would recommend SAS Analytics due to its ease of understanding. However, for those less inclined towards programming, Orange might be more suitable as a starting point. I would rate SAS Analytics an 8 for sure.