IBM InfoSphere Information Server vs. IBM Watson Studio on Cloud Pak for Data

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.

It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.

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• 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

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• Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc.
• SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly.
• Enforced best-practices set up POCs for deployment in production with a minimum of re-work.
• Estimator validation lets data scientists test and prove different models.

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Independently Invited by TrustRadius

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• 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.

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Independently Invited by TrustRadius

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• The cost is steep and so only companies with resources can afford it
• It will be nice to have Chinese versions so that Chinese engineers can also use it easily
• It takes a while to learn how to input different kinds of skin defects for detection

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• Scale of implementation
• IBM techsupport

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because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)

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The UI flawlessly merges this offering by providing a neat, minimal, responsive interface

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From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected

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Never had slow response even on our very busy network

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I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path

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The trainers on the job are very smart with solutions and very able in teaching

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The Platform is very handy and suggests further steps according my previous interests

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It surprised us with unpredictable case of use and brand new points of view

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

The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.

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It helped us in getting from 0 to DSX without getting lost

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

• Could instantly show data driven insights to drive 20% incremental revenue over existing results
• Still don't have a real use case for unstructured data like twitter feed
• Some of the insights around user actions have driven new projects to automate mundane tasks

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Invited by TrustRadius (TR) on Vendor's Behalf

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info