Introduction to Business Data Analysis
Every business, big or small, has a wealth of valuable data that can inform impactful decisions. But to extract insights, there’s usually a good deal of manual work that needs to be done on raw data, either by semitechnical users (such as founders and product leaders), or dedicated – and expensive – data specialists. Either way, to produce real value, information has to be collected, shepherded, altered, and drawn from dozens of spreadsheets and different business platforms: the organisation’s CRM, its martech stack, e-commerce system, and website data, to name a few common examples. Clearly, that’s a time-consuming process, and the outcomes can be old news, rather than up-to-the-minute insights.
Introducing Vibe Analytics
The ideal business solution would be querying real-time data using natural language (vs writing code in SQL or Python), with smart systems working in the background to correlate and parse different data sources and formats. This is vibe analysis, where users can simply ask questions in plain language and let AI do the heavy lifting. Instead of manual data-wrestling and business users spending hours uncovering insights hidden deep in datasets, they get results fast — in text, graphics, summaries, and, where needed, detailed breakdowns.
The Importance of Fast and Accurate Data Analysis
Fast and accurate data analysis is important to every organisation, but for many, real-time insights are crucial. In the agricultural sector, for example, Lumo uses Fabi.ai’s platform to manage large fleets of IoT devices, collecting telemetry data continuously and adjusting its systems based on collated, normalised, and parsed information. Using vibe analysis, Lumo sees device performance immediately, as well as trends that develop over time. It pulls in weather data, and correlates the device fleet’s performance metrics with environmental factors.
Getting Under the Hood
Sceptics of AI’s abilities often point to vibe-coding as an example of where things can go wrong, raising concerns about quality control and the “black box” nature of AI-driven analysis. Many users want visibility into how results are generated, with the option to inspect logic, tweak queries, or adjust API calls to ensure accuracy. When done well, vibe analytics addresses these concerns by combining transparency with rigour. Natural language inputs and modular build methods make it accessible to semitechnical users (such as founders and product leaders), while the underlying systems meet the accuracy and reliability standards expected by technical teams.
Fabi: A Generative BI Platform
Designed specifically for both data experts and semitechnical data users, Fabi is a generative BI platform that brings vibe analysis done right to life. The code it produces can be hidden away entirely, or shown verbatim and edited in place, giving semitechnical users a chance to understand how the analysis works under the hood, while allowing technical teams to verify and fine-tune the system’s output. Data flows from an organisation’s systems (the platform mediates connections) or is uploaded. The resultant actionable insights can be pushed/scheduled to email, slack, google sheets, displayed in graphics, text, or a mixture of both.
How Fabi Works
Co-founder and CEO of Fabi, Marc Dupuis, describes how many organisations start using the analysis platform by testing workflows and queries on sample data before progressing to real-world analysis. As users delve into data troves and test their work, they can check its veracity, often in collaboration with someone more technically astute, thanks to the platform’s open, transparent view of Smartbooks to show what’s happening under the hood. It works the other way, too: semitechnical data users can confirm that the data being processed is relevant and accurate.
Typical Workflows
Typical workflows include real-time KPI dashboards; natural-language Q&A over operational and product data; correlation analyses (for example, device performance against weather conditions); cohort and trend exploration; A/B test readouts and experiment summaries; and scheduled, shareable reports that mix text, graphics, summaries, and detailed breakdowns. These collaborative workflows are designed to be efficient and intuitive, so, whether working collectively or solo, users can unlock insights from even the most complex data arrangements.
Conclusion
In conclusion, vibe analytics is a powerful tool that can help businesses make data-driven decisions quickly and accurately. With the help of AI, users can ask questions in plain language and get results fast, without having to manually wrestle with data. Fabi is a generative BI platform that brings vibe analysis to life, providing transparency, rigour, and accuracy. Whether you’re a data expert or a semitechnical user, Fabi can help you unlock insights from your data and make informed decisions.
FAQs
Q: What is vibe analytics?
A: Vibe analytics is a way of analysing data using natural language, with smart systems working in the background to correlate and parse different data sources and formats.
Q: How does Fabi work?
A: Fabi is a generative BI platform that brings vibe analysis to life, providing transparency, rigour, and accuracy. It allows users to ask questions in plain language and get results fast, without having to manually wrestle with data.
Q: What are the benefits of using vibe analytics?
A: The benefits of using vibe analytics include fast and accurate data analysis, real-time insights, and the ability to unlock insights from complex data arrangements.
Q: Is Fabi suitable for both data experts and semitechnical users?
A: Yes, Fabi is designed specifically for both data experts and semitechnical data users, providing a platform that is accessible and intuitive for all users.
Q: Can I trust the output of Fabi?
A: Yes, Fabi provides transparency and rigour, allowing users to inspect logic, tweak queries, or adjust API calls to ensure accuracy. The platform also meets the accuracy and reliability standards expected by technical teams.
