How Good Is AI-Driven Business Analysis (Really?) 

You know big tech changes are coming when they make it into the mainstream. And in 2023, we all had the opportunity to witness it first-hand. Suddenly, news channels were talking about Artificial Intelligence, this bot that seemed to "do it all", and how it would revolutionize the way we do...everything.  

Some images of Skynet-infused ends of the world were conjured, some fear was mongered, and yet, overall, a lot of enthusiasm was poured in.  

Almost two years into the beauty and the madness, and everything seems to have come a long way. All in all, the market is settling down: there's less hype and more realism on what and how AI can do for businesses. But more importantly, AI tools have now started to niche down -- and AI-driven business analysis is definitely one of the most interesting spaces to keep your eyes on.  

Good AI-driven business analysis tools can save you hours of work, make your processes more efficient, set your team free from mundane tasks, improve communication and collaboration, and, ultimately, help everyone grow -- or 

How come? And how good is AI-driven data analysis, realistically speaking?  

Here's what you need to know.    

What Do We Want AI to Do? 

AI comes as a massive promise for pretty much anyone, in every industry. From chatbots to robot maids, a future worthy of The Jetsons feels right within grasp, especially with how fast AI has been developing over the past few years.  

Wearing rose-colored glasses and gearing up for flying cars serves no one, though, and we need to be realistic about what we want AI to do next. And when it comes to that, it all essentially boils down to:  

• Data Democratization: Our aim is to empower non-technical users to navigate data effortlessly, allowing for natural language interactions and data-driven decisions without deep technical expertise. 

• Ease of Use & Accessibility: It's essential for AI tools to be user-friendly for all users, not just experts, ensuring intuitive navigation across diverse user environments. 

• Advanced Analyses: We look forward to AI managing intricate analyses that generally demand extensive manual effort and expertise, streamlining processes that previously posed significant challenges. 

• Speed & Collaboration: Immediate insight retrieval is crucial, replacing the lag of static reports with instant, shareable, and editable data insights, facilitating real-time collaboration. 

• Customization & Flexibility: AI tools must cater to industry-specific needs, allowing for adaptation in styling, branding, and report formatting to meet varied organizational requirements. 

• Reliability & Trust: Trust in AI-generated results is paramount. Assurance in the accuracy and reliability of insights is essential to utilize findings effectively and to foster trust among users. 

• Security & Privacy: Expectations for AI-driven tools include robust data security, enforcing stringent access controls, and preventing unauthorized AI training on sensitive information. 

What AI Can Do for Data Analysts Now 

Let's get this out of the way: Artificial Intelligence can do a lot of things for data analysts, and it can do it very well. For example, AI can successfully assist with:  

• Data Collection & Connection: AI can assist in seamlessly integrating both structured and unstructured data. This includes the use of generative AI to establish vector databases that intelligently connect data points. While direct input of database credentials remains a task for the user, AI's potential shines in building comprehensive agents for expansive data collection from online sources. 

• Data Cleaning & Preparation: Once connected to a data source, AI can analyze and optimize data structures, conducting semantic evaluations to determine relevant data fields. It can autonomously perform iterative cleaning processes, transforming disordered data into a polished, unified format ready for analysis. 

• Modeling: Utilizing semantics, AI aids in identifying key performance metrics and offers insights on data aggregation. It automates the creation of formulas for calculated measures, such as profit margins, and deduces relationships and hierarchies, enhancing the modeling phase with structured outputs and smart prompting. 

• Exploratory Data Analysis (EDA): AI elevates EDA by executing custom analytical algorithms, facilitating reliable and consistent results. It selects appropriate statistical techniques, offering capabilities like specialized financial analyses based on the dataset's context. 

• Visual Presentation: AI selects and produces relevant visualizations from analytical results, crafting coherent dashboards. Leveraging intuitive visualization tools enhances the final presentations, making them interactive and appealing. 

• Interpretation of Results & Decision-Making: AI supports interpretation by generating narrative summaries and suggesting actionable strategies. It enriches data insights by accessing external knowledge bases, providing relevant contextual information and correlations. 

• Monitoring: AI monitors live data feeds, issuing alerts for significant changes. Through personalization and fine-tuning, it adapts to stakeholder-specific needs, providing customized updates and notifications. 

In an economic environment where doing more with less has grown to be crucial, every minute you save your team from does make a major difference. So even the most basic assistance with tasks like the ones mentioned above can save tens, maybe hundreds of hours of work every month.  

SO... Are AI Tools Good Now, Then? 

There's a lot AI does very well, as mentioned before.  

But where most data analysis Artificial Intelligence tools fall short is being too general. Let's look at what we have:  

ChatGPT 

ChatGPT is the incontestable leader on the AI market, and it is very much capable of handling a wide range of data from different domains like sports or medical records, thanks to its Advanced Data Analysis mode. Its versatility in performing surface-level general analysis makes it a powerful initial tool for various fields of activity.  

However, its lack of specialization can become apparent when tasked with more complex, industry or domain-specific analysis. Users often need to invest significant effort to draw meaningful insights. The process could typically include uploading a file, such as an Excel spreadsheet, and posing a question, to which the AI writes Python code to analyze data and generate insights and charts.  

Yet, the resulting charts often lack flexibility. Moreover, customization is difficult beyond basic modifications like changing colors. Plus, ChatGPT may not always accurately interpret its own results, providing general descriptions instead of specific insights. Users must often dig deeper into the generated code to understand and rectify visuals or computations that do not meet expectations. 

All in all, ChatGPT can help with a lot of things, but doesn't really deliver on the promise when it comes to data analysis. It could, but it's not quite helpful if you have to re-prompt it, work with your data, and try to understand how it works. 

PowerBI's Copilot  

Unlike ChatGPT, Copilot addresses the specialization issue many people have with ChatGPT. Its integration within specific Power BI functionalities allows it to perform specialized tasks efficiently.  

However, this approach creates a fragmented system with multiple Copilots across the application, each limited to particular functions. For instance, where one Copilot might assist in writing DAX formulas and preparing models, another can aid with querying dashboard visuals, and another one can help with data exploration, using Python or SQL. The challenge is linking these components into a cohesive workflow, demanding a substantial level of expertise to navigate each stage effectively. Additionally, Power BI's enterprise-level cost and accessibility create barriers for small businesses. 

There's something else on the market 

At Zebra BI, we looked at the market for data analsyis and visualization, understood the shortcomings, and took on the challenge: we created the world's first AI autopilot that creates fully interactive business dashboards directly from your data sources. 

Zebra AI was born out of more than one decade of experience in data analysis, to really lift the burden off your shoulders in terms of how you create dashboards and draw insights from your data.  

What makes it unique?  

• Specialization: Zebra AI is tailor-made for business users who need smart, precise insights rather than general descriptions. 

• Integration: Zebra BI's long-standing integration within Microsoft Excel and Power BI allows for a cohesive experience with no extra costs or barriers. 

• Smart Automation: With its advanced algorithms, Zebra AI can go beyond pre-programmed responses to understand and answer queries like a human analyst would do. We've "fed" the machine with actual financial and economy-related analysis, so it understands how to recognize and interpret financial data.  

• User-Friendly Experience: Zebra AI was designed with business users in mind. Its simple, intuitive interface allows for easy navigation and customization of results, from visuals to deep-dive analysis. 

• Customization: Zebra AI dashboards and reports can be very easily customized for your company brand, allowing you to create a unified identity across the entire organization.  

• Collaboration: Working with your peers on the same Zebra AI story is as easy as copy-pasting a link. Everyone can pitch in, ask their own questions, and draw insights, so you can all work together, instead of in siloes.  

• Interactivity: Each report generated by Zebra AI is completely interactive. You can zoom in on any data point, dig deeper, and explore the "why" behind a certain trend.  

• Cost-effective: Zebra AI offers multiple pricing options, so you can choose what works best for your budget and business needs. In fact, the first pricing tier costs exactly $0, and it allows you to test the entire platform and all its features.  

You might think we're biased -- but you can try it all for free and see for yourself. Here's what Danone's Financial Team said about it, for example:  

“Our experience with Zebra AI has been fantastic. The tool’s capabilities have grown significantly since we started the proof of concept, and the support from the team has been outstanding. Compared to Microsoft’s Copilot for Power BI, our experience has been MUCH better with Zebra AI.” 

(Instead of a) Conclusion 

Some see AI as a threat on critical thinking and innovation -- but Artificial Intelligence tools aren't meant to do the work for you. They're meant to make it easier and free your team's time so they can focus on complex decision-making, innovative solutions, and growing the business. Using AI for more than just "surface-level" tasks can provide a competitive edge, as well as free up resources for critical activities that require human input, expertise, and creativity.  

That's what Zebra AI aims to do, too -- to create a complementary tool for data analysis that creates value, not competition. With its advanced algorithms and customizable features, it can save time and effort while producing accurate insights and tailored reports for your business needs.  

 Most AI tools are flawed -- not because the technology itself is, but because they aim for the "General", rather than the "Specialized".  

Our goal was and continues to be the exact opposite: Zebra AI is created by analysts, for analysts. And that makes a world of difference.  

Try it -- and let us know what you think! 

...And if you want to learn more about AI in Business Intelligence...

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