Microsoft Power BI vs. OpenAI API Platform

Has significantly improved collation of data and visualisation especially with business across Europe. Has given me the ability to see the Site availability at the click of a button to see which Site is in the "money" and seize opportunities based on Market data

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For smaller organizations that run lean and would like to get to deploy a solution quickly. This is a solution that is easy and quick to develop. It has a good amount of customization. However, for advanced customization this might not be a good solution. I suggest experimenting with OpenAI API and then if the experimentation is successful then it is a good idea to optimize and try other LLM models.

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• Options for data source connections are immense. Not just which sources, but your options for *how* the data is brought in.
• Constant updates (this is both good and bad at times).
• User friendliness. I can get the data connections set up and draft some quick visuals, then release to the target audience and let them expand on it how they want to.

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• The developer experience is top notch. Their SDKs are super easy to use
• Organization and project billing separation. You know where everything was consumed.
• Playground. The playground is super useful to prototype without writing a single line of code

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• It would be easier for users could Microsoft Power BI and Excel used the same programming languages.
• Would like to see the online version of Microsoft Power BI be as powerful as the desktop version.
• Publishing a Microsoft Power BI file online and then having to save the file is somewhat redundant.
• Would like to export each page or chart as an image.

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• Restrictions are sometimes too strong

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Microsoft Power BI is an excellent and scalable tool. It has a learning curve, but once you get past that, the sky is the limit and you can build from the most simple to the most complex dashboards. I have built everything from simple reports with only a few data points to complex reports with many pages and advanced filtering.

Automating reporting has reduced manual data processing by 50-70%, freeing up analysts for higher-value tasks. A finance team that previously spent 20+ hours per week on Excel-based reports now does it in minutes with Microsoft Power BI's automated Real-time dashboards have shortened decision cycles by 30-40%, enabling leadership to react quickly to sales trends, operational bottlenecks, and customer behavior.

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Easy to setup, develop and deploy. The payload for the API is simple and has all the inputs required for simple projects. There are a good number of options of LLM models to optimize for speed, cost or quality of the answers. A larger token input might improve the overall usability.

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It is a fantastic tool, you can do almost everything related with data and reports, it is a perfect substitutive of Power Point and Excel with a high evolution and flexibility, and also it is very friendly and easy to share. I think all companies should have Power BI (or other BI tool) in their software package and if they are in the MS Suite, for sure Power BI should be the one due to all the benefits of the MS ecosystem.

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It was integrated with our erp easily and was accessible on cloud.

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Microsoft Power BI is free. If I didn't want to create a custom platform (i.e. my organization insisted on an existing platform that I *had* to use), I'd use Microsoft Power BI. For any start-up or SMB, I'd just use Claude & Grok to build it quickly, also for free. Would not pay for Tableau or Sigma anymore. Not worth it at all.

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Anthropic is only the best for coding and its really really expensive. So, if you're not making a coding app, I would stay away from it. On the other hand, Gemini models are dirt cheap but come with a bit of performance limitations, so i would use it for big volume non sofisticated use cases. The OpenAI API platform excels at providing best in class performance models, at not outrageous anthropic-like pricing.

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• Power BI usage reduced the effort of analytical reports creation by about 80%
• Empowered all the level of employee to be more vigilant of the data and business insights, gained the profit of 8% overall.
• AI-powered predictive analytics improved forecasting accuracy by 17%, that topped the overall sales.

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• Big question about functionality

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