Google Sheets vs. IBM SPSS Modeler

Google Sheets is well suited in two main areas: is free to use and you don't need to buy a license to use it, comparing to the most direct competitors ; collaboration is in my opinion the best advantage, with multiple people working together and seeing others working in real time. It's less appropriate in low connectivity environments (offline capabilities)

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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• Collaborative planning : In the initial phase of project, Team leads and architects create a permission matrix along with the naming convention simultaneously, seeing who is editing / adding the details in real-time.
• Cost tracking : We use this tool to track cloud resource usage monthly costs, so that we can analyse it and send out comms for high cost based resources. By storing cost data here, it's easy for use to store data of last couple of years.
• Flexible documentation : For change logging of different scenarios we would need different / ad-hoc columns to be added on the fly, which makes using this tool much simpler then reputed third party tools.

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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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• Ease of use - I collaborate with a lot of folks on these and a lot of them have trouble using it.
• Would love more live features; for example, being able to have cells react to other inputs (checkboxes, etc) with reactive formatting
• More cross-usability with other Google products; it's hard to think of drawbacks honestly, this is a pretty seamless software.

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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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I am not involved in the purchase/selection process, but my organization is a Google shop, and Sheets meets most of our spreadsheet needs and works seamlessly with our other tools. I don't anticipate our switching anytime soon.

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It can easily handle most uses and functions. It is only for very large datasets or advanced analysis that it either lacks the proper functions or performance begins to slow. Most employees who continue to use competitors' products do so out of preference, familiarity with the user interface, or other surface-level reasons.

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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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Like most Google products, Google Sheets rarely has outages or slowness, and when it does, connection is always momentarily restored. I can't recall a time when I've been unable to access Google Sheets but able to access other sites just fine. That said, errors aren't uncommon when handling large data volume. You know what they say about using spreadsheets as databases, but sometimes it's just the most convenient option, especially for smaller or one-off projects, and not being able to store large amounts of data hampers our ability to move quickly with scrappy prototypes or full solutions. It would be great if we could better integrate our data manipulation (Apps Script) with big data in the sheet.

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Again, Google Sheets is no exception to Google's general high speed and reliability, but load times can be slow for larger amounts of data. I've used Sheets with Zapier and have used the Python API, and speed has never been an issue.

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I have never contacted Google Sheets support, but Google Sheets makes it very easy to report an issue or suggest a feature from Sheets itself (Help > Help Sheets improve), and I've had mostly good experiences with support for other Google products.

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

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I have found that I can do almost everything I could have done in Microsoft Excel faster and easier in Google Sheets. We recommend Google Sheets in 99.9% of our use cases and feel it meets the needs of our workers very well. I am sure there are other spreadsheet creation programs out there, but because we are already in the Google environment, adopting Google Sheets in very easy.

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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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I'm not involved with the purchase, but I assume everything goes smoothly and that the pricing structure is predictable and reasonable. We do not get surprise fees.

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Google Sheets works very well with multiple users. It's convenient to see in real-time who is collaborating in a sheet, down to the specific cell that they're viewing/editing. Linking Sheets across departments is convenient with the IMPORTRANGE function.

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• Don't need to pay for windows 365 license as it is free
• Has a positive impact since I am not cursing excel for annoying problems(I don't want the new Pivot table format, I want to use Classic and I don't want to expand/collapse arrows. "x$#%")
• [Haven't] looked at return on investment on work, but has "simplified" for basic and medium spreadsheets.

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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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