The Dataiku platform unifies data work from analytics to Generative AI. It supports enterprise analytics with visual, cloud-based tooling for data preparation, visualization, and workflow automation.
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IBM watsonx.governance
Score 8.7 out of 10
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The more AI is embedded into daily workflows, the more proactive governance is required to drive responsible, ethical decisions across the business. Watsonx.governance is used to direct, manage, and monitor an organization’s AI activities, and employs software automation to strengthen the user's ability to mitigate risk, manage regulatory requirements and address ethical concerns without the excessive costs of switching data science platforms—even for models developed using third-party tools.
With its smooth integrations with different AI models and strong compliance tools, IBM watsonx.governance leads in comprehensive data governance. IBM watsonx.governance provides a well-balanced combination of governance, compliance, and integration capabilities in contrast to Da…
Dataiku is an awesome tool for data scientists. It really makes our lives easier. It is also really good for non technical users to see and follow along with the process. I do think that people can fall into the trap of using it without any knowledge at all because so much is automated, but I dont think that is the fault of Dataiku.
We have been able to make the right decisions based on performance metrics. Data assets across the enterprise have experienced significant growth from comprehensive audits that drive quality growth. The platform has filtered out poorly analyzed data from the workflow chain and introduced stable control mechanisms that meet compliance policies.
The integrated windows of frontend and backend in web applications make it cumbersome for the developer.
When dealing with multiple data flows, it becomes really confusing, though they have introduced a feature (Zones) to cater to this issue.
Bundling, exporting, and importing projects sometimes create issues related to code environment. If the code environment is not available, at least the schema of the flow we should be able to import should be.
The user experience is very good. Everything feels intuitive and "flows" (sorry excuse the pun) so nicely, and the customization level is also appropriate to the tool. Even as a newer data scientist, it felt easy to use and the explanations/tutorials were very good. The documentation is also at a good level
Research data can be handled and governed more effectively to save time and minimize errors. Practical learning helps students become more marketable to employers by giving them practical experience with industry-standard tools. Updates content on AI governance in courses to make them more appealing to students. Lowers the time needed to manually check for biases, increasing the validity of research findings.
The open source user community is friendly, helpful, and responsive, at times even outdoing commercial software vendors. Documentation is also top notch, and usually resolves issues without the need for human interactions. Great product design, with a focus on user experience, also makes platform use intuitive, thus reducing the need for explicit support.
Anaconda is mainly used by professional data scientists who have profound knowledge of Python coding, mainly used for building some new algorithm block or some optimization, then the module will be integrated into the Dataiku pipeline/workflow. While Dataiku can be used by even other kinds of users.
With its smooth integrations with different AI models and strong compliance tools, IBM watsonx.governance leads in comprehensive data governance. IBM watsonx.governance provides a well-balanced combination of governance, compliance, and integration capabilities in contrast to Dataiku, which concentrates more on data science workflows, and Holistic AI, which stresses AI ethics and risk management. That was my choice because of its robust integration features and comprehensive approach.
It has massively cut down the time our compliance teams spent on preparing compliance packs for EU emissions report. We're talking 4 weeks of manual tracing and spreadsheet validations to just under 3 days now!
IBM watsonx.governance flags anomalies in shipping data 2 weeks earlier than our older system, saving us thousands by renegotiating contracts before spot prices rise