AI data analysis vs traditional BI platforms: Where we’re headed

Someone on Reddit asked a great question: "Why do business users ask for dashboards that they never open?"

Data analysts complain that they spend tons of time making reports for one-off questions that business users might look at once (if they're lucky). Or, they follow up last week's request with a related but slightly different request. This leads analysts to feel like they're taken for granted or that their work isn't valuable.

Business users, meanwhile, keep making requests! They feel they can't find the answers to the specific questions they have in the company's forest of BI reports.

This clash highlights how, in the current debate between traditional BI and AI data analysis, business users and data pros have a common enemy: mountains of useless reports. 

Even in businesses where traditional BI is still “working,” we hear users increasingly saying they need a new tool. Not a replacement for what traditional BI does. Rather,  a complement that enables fast, highly customized data exploration and ad hoc analysis. 

This is the role that AI data analysis plays in data-driven companies. In this article, we’ll take a closer look at how traditional BI and AI data analysis differ, and how you can use AI data analysis to fill in the gaps left by traditional BI while also reducing Dashboard Creep.

Traditional BI vs. AI data analysis

Traditional BI has been with us for a while - and for good reason.

In the traditional business intelligence model, data engineers create data pipelines that take data from disparate data sources across the enterprise, clean and transform it, and combine it into a format suitable for a given use case. Data analysts and power users can then mine this data to produce reports containing actionable insights.

Traditional BI excels in:

Stable, recurring reporting. Executives, managers, and other decision-makers rely on traditional reporting to track KPIs that are critical to the business. This provides reliable data for identifying new opportunities or making course corrections through data-driven decision-making.

Of course, this assumes that nothing changes in the underlying data. Sadly, reality is constantly changing - and so does the underlying data. This requires engineers and analysts to keep monitoring and fixing data pipelines so that reports don't break. So long as you continue making this investment, everything works smoothly.

Semantic layer. BI introduced the notion of a semantic layer, a set of centralized and predefined metrics that everyone across the business can access. This prevents different business units, for example, from using conflicting definitions of key concepts such as "revenue."

Visual dashboards for quick insights. It's one thing to see a pattern in a table of 100 numbers. It's another thing entirely to see a line in a chart move drastically up or precipitously down. Visual dashboards provide a fast, intuitive way for business users to understand how their business is performing without needing to learn SQL or Python.

Many power users can even use BI tools such as Tableau and Power BI to create their own reports from minted datasets. This provides a modicum of flexibility in generating new insights from existing high-quality data.

By contrast, in the AI-driven data analysis model, anyone - data engineers, analysts, or business users - can gather and instantly analyze data from multiple sources using, not SQL, but natural language queries. Users can take data from any source - data warehouses, but also Excel spreadsheets, Google Sheets, CSV files, etc. - and ask a Large Language Model (LLM) to combine, transform, clean, analyze, and visualize it via a series of prompts through AI-driven workflows.

AI data analysis excels at automating data preparation, identifying unique patterns in data, and generating real-time insights. This provides two major benefits:

• For business users, it provides a simple, powerful interface to ask ad hoc questions, reducing the load on the data team
• For data professionals, it's a fast method of performing exploratory data analysis, enabling them to dig into the data and explore more complex questions much faster than they would with traditional BI platforms.

Where traditional BI breaks down

Why do we need this? Because BI has limits.

To be fair, this is less a matter of BI "breaking down." Rather, it's a matter of using traditional BI tools for cases that fall outside their purview. These include:

Intuitive self-service

BI was the only game in town when you couldn't ask questions directly of your data. But with the advent of LLMs and natural language processing, we now have a better, more intuitive interface for answering data-related questions.

Executives still need highly accurate and up-to-date dashboards and metrics produced the old-fashioned way. However, the rest of the business can get the data they need from natural language queries. This frees up resources to focus on those reports that are essential to the business, rather than spending so much time on dashboards that no one reads twice.

Slow iteration cycles

A huge downside of traditional BI reports is the long development cycles required. Dashboards can take anywhere from hours to weeks to build, depending on the complexity of the underlying data. Many times, by the time the report is done, the question that sparked it is no longer relevant.

By contrast, AI data analysis can provide immediate answers to pressing questions in hours, often minutes. This makes it a valuable tool for generating insights in a more immediate, lighter-weight manner. If a given query is used frequently and needs to be tightened up and made more widely available, it can be promoted to a traditional dashboard.

Information discovery

Most companies that have an extensive BI investment are drowning in dashboards. It can be hard to find the ones that are relevant to your current question. AI data analysis means you never have to go digging for the right report - you can always generate it fresh.

What native AI data analysis tools can do

The benefit of asking complex business questions in plain English is so apparent that businesses are rushing to add "AI" to everything. Traditional BI platforms are no exception. Many are bolting on a natural language interface to their existing solutions.

The problem, is that these solutions often require a well-defined semantic layer in place to produce useful results. In other words, they require all of the underlying architecture of the traditional BI stack - Airflow for data orchestration, dbt for data transformation, CI/CD-style data pipelines, and visualization tools. They need perfect data and don't operate well when data isn't properly sanitized and prepped.

In other words, "bolted-on AI data analysis" does nothing to address the clear problems of traditional BI.

AI data analysis tools are built from the ground up to solve a different set of issues. They're ideal for supplementing traditional BI in mid- to large-size organizations. They're even mature enough to substitute for traditional BI in small companies and lean teams.

AI data analysis tools supplement BI by:

• Enabling easy self-service, thus offloading requests from the data teams. Our own customers have reported eliminating up to 50 data requests a month by using AI tooling instead of traditional BI.
• Providing human-in-the-loop code generation. Good AI data analysis tools aren't black boxes. They display all of the SQL and Python code they used to generate a solution, producing an editable notebook that data experts can validate and modify. Studies have shown how such AI code-generation tools can boost developer productivity by 55%. That makes AI data analysis a useful tool for business users and data developers alike.
• Facilitating data exploration. AI tools can automatically perform basic EDA tasks, such as generating ordinal and categorical summaries of data, performing various types of statistical analysis such as univariate and multivariate analysis, anomaly detection in datasets, and identifying correlations in unstructured data. You get this for free with AI data analysis without writing a single line of code.

Popular AI data analysis tools and their use cases

The proliferation of "AI everywhere" makes it harder to select a good AI data analysis tool. As I mentioned above, many "AI data tools" aren't AI-native - they're simply natural language interfaces to traditionally managed datasets.

That means most tools will only work within the constraints of your current pre-built data pipelines. While there's some value here, it's not where the real magic happens.

Another issue is that not all AI data analysis tools work well outside of a demo. Many don't utilize enough business context or sufficient memory feedback loops to learn over time, which is key to output accuracy. AI-powered tools also vary significantly in their functionality, pricing, and scalability.

Here are some of the more popular AI tools on the market and what they can - and can't - do.

Fabi.ai

We're not the only game in town, of course, and you should feel free to test out claims against others on the market. But we're proud of what we've built with Fabi.ai.

We've engineered Fabi.ai from the ground up as a native AI tool for data exploration. With Fabi.ai, you can generate data-driven solutions 10x faster than coding SQL and Python from scratch.

Fabi.ai hosts all work in Smartbooks, living notebooks that users can verify and edit. This means that business users, data analysts, and data engineers can collaborate on new data projects using a combination of natural language, SQL, and Python - whatever they're most comfortable with.

Data experts can generate boilerplate code in minutes using machine learning algorithms. Business users and non-technical users can ask cutting-edge questions without needing to learn how to code using AI-powered natural language interfaces. Everyone wins.

Fabi.ai also supports automated workflows for repetitive tasks, enabling teams to automate report generation, automate data processing, and optimize decision-making processes. This includes capabilities for predictive analytics, forecasting future trends, creating real-time dashboards that update automatically as new data arrives, and building custom workflows for specific business needs.

Multiple customers have used Fabi either to supplement or replace traditional BI systems:

• Hologram saw a 94% reduction in analyst delivery time
• Parasail eliminated 90% of its BI dashboards, reducing the time it took to create initial versions of new dashboards from months to just 4-5 hours
• Lula Commerce cut 30 hours of repetitive data work every week by using Fabi.ai

Fabi.ai is a solid solution for teams looking to move fast on exploratory analysis, or for small companies that need enterprise BI capabilities without an enterprise BI investment. The platform connects to common data source,s including data warehouses like Snowflake and BigQuery, CRM systems, ERP systems, and social media platforms.

Julius

Julius is designed with simplicity and accessibility in mind. Like Fabi.ai, it supports generating Python, parsing data from uploaded files, and providing basic AI data analysis at an affordable price point.

In our view, Julius doesn't cover the same ground as Fabi.ai. For example, it doesn't support version control or integration with enterprise data warehouses and Google Sheets. It might work for you if your use cases are more limited or you don't need a company-wide solution.

Wisdom

Unlike Julius (and like Fabi.ai), Wisdom can incorporate common business data sources such as Snowflake, PostgreSQL, and Google BigQuery. It works by organizing data into business domains and using these domains to generate interactive Stories, which combine visualizations and text.

LLMs (Claude, ChatGPT, etc.)

All AI data analysis solutions make use of the major LLMs on today's market. You can opt to use these AI models directly instead, feeding them your data and prompting them to perform common EDA tasks, find hidden patterns in data, analyze customer behavior, or detect customer churn risks.

The benefit of this approach is that it's one less tool to license and manage. The downside is that you don't get the data integration, API management, and advanced learning capabilities that come from using a purpose-built tool. In particular, you'll either have to upload all your data as files or develop your own custom data warehouse integrations. 

Why legacy BI is on its way out

It's too early to say that AI data analysis can replace legacy BI in all circumstances. We're not quite there yet. For now, traditional BI tools cover ground in some large-enterprise scenarios that AI can't match, particularly when it comes to static dashboards for tracking historical data and past performance.

But that's changing - rapidly.

Legacy BI will eventually die. It's built on an old paradigm that has, to date, shown itself unable to change. The bottlenecks created by manual analysis and the need for data scientists and specialized data engineering skills make traditional BI systems increasingly untenable for modern businesses.

AI-driven analytics is pointing the way towards the future - a world of text-to-dashboards BI, managed under a unified platform where you can perform both complex EDA and produce executive-facing reports. Rather than spending months building out a semantic layer, you'll be able to auto-generate one based on Slack conversations - without having to spend months getting a PhD in an enterprise BI tool.

Using an AI data analysis tool today means you can address gaps that legacy BI tools just can't. In the near future, you'll be able to use the same tools to manage reporting across your business, at the speed of your business.

Prepare for the future today - create a free Fabi.ai account now and start asking questions of your data in under five minutes.