IBM InfoSphere Information Server vs. MonkeyLearn

Information Server is extremely useful to replace manual developments that require a lot of coding effort. It significantly increases the productivity of the initial development and the future maintenance of the processes since it has a visual development environment with self-documentation.

I have used MonkeyLearn to analyze texts and extract information, that has been a tough task for me because I have to do it manually, but this software has made it easy for me, it is very easy to use and very intuitive, I can use it for extracting information from emails, chats, forums, websites, etc. I would definitely recommend using this platform to others.

Incentivized

• IIS best for ETL ,not ELT , and many and diffrent source systems.
• It also can process big data , unstuctured data
• It is not only DWH , you can use infosphere for analys and see the bigger architecture of your OLTP systems

Incentivized

• I like how MonkeyLearn can track customer feedback for us, it helps us get more data about customer needs, it makes it easier for us to analyze customer feedback and act on it in the future.
• Easily build and train a machine learning model to tag and classify your text.

Incentivized

• I would be nice to have a new web development environment for DataStage.
• Connectivity Packs such as Pack for SAP Application are a little pricey.
• It is confusing for new developers the possibility of developing jobs using different execution engines such as Parallel or Server.

Incentivized

• Sometimes the segregation is generic based on the email content and needs more examples to learn.
• Financial category is not easily segregated/tagged.

Incentivized

• Scale of implementation
• IBM techsupport

Incentivized

DataStage is more robust and stable than ODI The ability to perform complex transformations or implement business rules is much more developed in DS

Text analysis tools. Thanks to its more than 60 native integrations in the platforms, they make it possible to import your data sets. Furthermore, they also make it easy to export your data sets to other programs. Built-in extensions make their flexible platforms. Some of these integrations include word processing, detection, and web mining.

Incentivized

• Productivity of the development of integration processes.
• Better documentation and governance.
• Reduce training costs of various technologies.

• Extract custom data within texts.
• Their labeling process is fast and efficient.
• The Studio version of the tool is an excellent option for a complete analysis solution.