IBM SPSS Modeler vs. Powerslide

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.

Incentivized

It has allowed us as an organization to clearly deliver our numbers to our collaborators, but above all to the heads of our teams and company. Not being entirely professional, it is easy to use for someone who does not understand the function of these numbers or hard data.

Incentivized

• 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.

Incentivized

• Collaborative fractures helped a lot.
• Dashboards are really unique.

• 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.

Incentivized

• Increase the variety of fonts
• Make templates less serious or business

Incentivized

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

Incentivized

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.

Incentivized

Powerslide stands out for having templates that help you easily organize hard data and more delicate information such as numbers and statistics. By having many ways to present and edit it in one place, it makes this a page with variety and empathy with its user.

Incentivized

• 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.

Incentivized

• Our collaborators do not get bored in meetings and even if it is a lot of information, seeing it in an interesting design makes them pay attention and like to be informed of the numbers they manage to obtain with their work.

Incentivized