Anaconda vs Qlik Sense

• Clear install story. There are a lot of ways to install python. There's only one way to install anaconda. This makes teaching and standardizing much easier.
• Batteries included. It's easy to install things in python, but anaconda ships with most of what you need out of the box. This helps with standardization and reproducibility.
• Good integrations with Jupyter and other visual tools. Jupyter is really convenient when learning various python packages. Anaconda makes these tools easy to launch and to use.

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• Running consistently without errors which gives users confidence in the system and its numbers
• Easy to configure and customise to adopt to manyfold sources (facebook, google analytics, filemaker, SAGE, various Excel sheets)
• Easy to navigate within the table and graphs (drill down, slice & dice, filter, sort) and easy to export results.
• Geoanalytics is cool
• Very customizable user and role based security

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• Although some other users mentioned the installation is "simple", we did encounter some challenge in a highly controlled environment (due to security reasons).
• Jupyter Notebook is extremely slow when the client/server side of the network's speed/bandwidth is not balanced.
• Bootstrapping Anaconda takes too long, sometimes I even started doubting it would respond any more.
• If there are extra python packages you need but are not by default installed by Anaconda, then some efforts will be required to figure out how to put them in the right place.

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• Visualizations have sometimes worked slightly differently between releases requiring the need for some re-work when upgrading. Most of these have been appearance only but are still annoying.
• Enterprise product should come with the ability to email common reports. People still want/need this but don't need all the functionality of the Nprinting product. Basic email functionality should not need to be a separate purchase. Perhaps give the basic functionality with the product to give people exposure to it would actually increase the take rate on Nprinting.

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• We can get any new employee set-up on Python for Machine learning in minutes, without any assistance from IT. That's real $ savings.
• We started to experiment with Machine Learning a lot more, which leads to creating new projects which can have a tremendous impact on the business.

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• Positive - we have been able to dramatically reduce customer churn rate with increased business visibility.
• Positive - reduced reporting complexity by pushing/flagging employees for specific needs, thus reducing the need for best-guess reporting.
• Positive - overall ROI has been more than measurable from a simple complexity reduction standpoint. No more creating custom reports on a monthly basis. Once a report or dashboard has been created, that report becomes a standard for us and can be easily modified for future use.
• Positive - we've been able to reduce head-count for report generation.

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