IBM SPSS Modeler vs. Cortana (discontinued)

Fast NLP analytics are very easy in SPSS Modeler because there is a built-in interface for classifying concepts and themes and several pre-built models to match the incoming text source. The visualizations all match and help present NLP information without substantial coding, typically required for word clouds and such. SPSS Modeler is good at attaining results faster in general, and the visual nature of the code makes a good tool to have in the data science team's repository. For younger data scientists, and those just interested, it is a good tool to allow for exploring data science techniques.

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It's easy for anyone who is expecting some simple AI problems like fetching the keywords, understanding the intent, language translation, etc. to be solved from an existing database and all they need is to connect to their APIs via a subscription model. But for complex use cases, there is still room for improvement like customization of underlying AI models for a specific use case like identifying some unique identifiers with respect to industry.

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• Combine text and data
• Provide facilities for all phases of the data mining process.
• Use a node and stream paradigm to easily and quickly create models.

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• Being hosted in Azure solves a massive hosting problem
• The language understanding system has the ability to revolutionize many vertical markets
• Integrating with Cortana Analytics was really simple due to easy to understand documentation

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• Has very old style graphs, with lots of limitations.
• Some advanced statistical functions cannot be done through the menu.
• The data connectivity is not that extensive.
• It's an expensive tool.

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• More partnerships with colleges and schools to increase the workforce with technical knowledge (increase local workforce)
• Have more online training and documentation in other languages
• Have affordable prices for students

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The ability to do predictive modeling, text analytics for both structured & unstructured data, decision management, optimization, and support for various data sources

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The online support board is helpful and the free add ons are incredibly appreciated.

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When it comes to investigation and descriptive we have found SPSS Statistics to be the tool of choice, but when it comes to projects with large and several datasets SPSS Modeler has been picked from our customers.

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IBM Watson Assistant has been early into this market and has improved a lot over time compared to Azure AI Cortana. More documentation related to the services. But Ease of integration Azure AI ranks over IBM Watson Assistant. And again in terms of services offered under the ecosystem, Azure AI precedes IBM Watson Assitant.

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• Positive - Ease of decision making and reduction in product life cycle time.
• Positive - Gives entirely new perspective with the help of right team. Helps expanding the portfolio.
• Negative - Needs to have good understanding about mathematical modelling, of which talent is rare and expensive. Hence, increase the costs for R&D and manpower.

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• Difficult to ascertain the ROI as we are a software house who have developed a module in our application using Cortana. However for companies that use our software I would say the use of sentiment analysis in our application could free up at least 1 full time resource to be used elsewhere in their organisation.

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Independently Invited by TrustRadius

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. More Info