Dataiku vs. InetSoft Style Intelligence

Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.

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It's great for the accumulation and assimilation of various data formats to be channeled into a single end-point, whether that be a table or a report. It reminds me so much of SPLUNK in that regard. The application is also very easy to use and has been configured in such a way that it is appealing and productive for both novice and expert users. I was able to pick up the basis after a solitary day's training. We've mainly used it to create bespoke dashboards and visuals, thereby allowing for effective report writing. This was a major consideration from Senior Management before the application was purchased. There haven't been any real drawbacks with the software thus far.

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• The intuitiveness of this tool is very good.
• Click or Code - If you are a coder, you can code. If you are a manager, you can wrangle with data with visuals
• The way you can control things, the set of APIs gives a lot of flexibility to a developer.

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• Easy to use with a very user-friendly interface, making onboarding with the tool a lot easier.
• Able to mash up/integrate data from different sources and file types which helps a lot with consolidation.
• Good customer service/support that makes solving problems/issues a lot faster.

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• End product deployment.

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• I think the user interface is in need of an upgrade, as its starting to show it's age a little. It looks like it needs a rehaul in the department.
• Hopefully, the company can grow and then have more frequent interactions with their customers.
• I am slightly wary that support may not have the same proficiency at the very highest level, as my company reaches the expert user level.

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As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.

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The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.

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Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.

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InetSoft is a more cost-effective option that offers the same functionality, features, and usability. It is a great option, especially for SMEs. It also has top-notch customer service/support which is a key factor in selecting any agency or tool. I recommend using InetSoft especially for small businesses

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• Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
• Platform also ease tracking of data processing workflow, unlike Excel.
• Build-in data visualizations covers many use cases with minimal customization; time saver.

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• Reduction in excessive space for data storage repositories
• Faster time to analyse data and metadata. Quicker to reach objective analysis
• Conclusive informational reporting

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