Hex.tech vs. IBM Watson Studio on Cloud Pak for Data vs. Plotly Dash

Hex.tech is an excellent tool for a product intelligence or product analytics team that wants to enable widespread use of available data for decision-making. It requires some SQL experience in many cases but makes it possible to go far without complex querying skills.

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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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Applicable for data visualization across disciplines. I have used it for data from buildings, building occupancy, public health, and statistics. It is a useful tool to use for big data. It has nice templates and a number of interesting visualization types. If you are familiar with R and python it is easy to use.

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• Powerful interactive notebook environment
• Balances the need of code and non-code users
• Provides free and easy sharing to viewers of analyses across the company
• Responds promptly to support requests and needs
• High quality native charting capabilities

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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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• Powerful visualization options.
• Ability to create in-browser interactive visualization apps.
• Ability to create hosted apps.
• Allows you to develop web-based reporting applications without requiring web application development expertise.

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• Limited support for R notebooks
• Limited sharing outside of the app (e.g., no slack integration)
• Some Python knowledge needed for highest levels of aggregations

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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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• Would be good if Dashboard Engine was included in the Enterprise VPC plan
• Would love to see ready made fintech apps

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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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Hex.tech is the easiest to use, most enjoyable experience, most powerful environment, and most cost scalable of all the above.

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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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• Increased visibility into analyses with free membership tier across company
• Increased capacity for self-service analytics among R&D stakeholders (e.g., product managers)
• Improved productivity of data science team with ease of use

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• 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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• A no-cost option as it is open sourced.

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