A comparison of traditional BI vs exploratory data analysis solutions like Fabi.ai with practical examples.

The ability to extract actionable insights from vast amounts of information is crucial. Yet, while the business intelligence market is estimated at $23B in the US alone, 97% of the gathered data will never be explored.

Traditional Business Intelligence (BI) tools have long been the go-to solution for key metrics and source-of-truth reporting. However, they often fall short when it comes to ad hoc analysis, exploratory data investigation, and advanced techniques like machine learning for churn prediction, forecasting, and A/B testing. This gap has given rise to a new breed of exploratory data analysis (EDA) platforms that offer greater flexibility and power.

In this article, we'll delve into the strengths and limitations of both BI and EDA platforms, helping you understand when to leverage each for maximum impact. We'll also give you an overview of Fabi.ai and break down why–and when–we may be the best option to suit your data needs. 

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The different types of analytics platforms and analyses

Before diving in, let's explore the various types of data analytics platforms and the analyses data practitioners typically conduct.

Data analytics platforms generally fall into two broad categories:

1. Business Intelligence (BI) platforms: Tools designed to create and distribute standardized reports and dashboards across an organization.
2. Exploratory Data Analysis (EDA) platforms: Tools that enable in-depth, ad-hoc analysis of data to uncover insights and patterns.

EDA platforms can be further divided into two subtypes: 

1. Traditional analytics platforms: Established tools that require more technical expertise.
2. Agile analytics platforms: Modern tools that emphasize flexibility and ease of use.

There are also lots of more specific applications that offer analytics points solutions such as Amplitude and Posthog for product analytics or Salesforce and Google Analytics for marketing. However, we'll focus on the broader categories in this article.

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Business intelligence (BI) platforms: Definitions, use cases, and popular tools 

Business Intelligence platforms are software tools designed to collect, process, and present data in a way that enables organizations to make data-driven decisions. They typically offer features for data visualization, reporting, and dashboard creation. 

Consider a company's revenue reporting. BI platforms ensure that everyone across the organization views the exact same revenue figures, promoting alignment and consistency. This principle applies to various metrics such as churn rates, net dollar retention, and marketing pipeline performance.

Pros of BI tools: 

• They provide a single source of truth for key metrics
• They offer standardized reporting across an organization
• They enable non-technical users to access and analyze data
• They facilitate data governance and security

Cons of BI tools: 

• They can be inflexible for ad-hoc or complex analyses
• They often require significant setup and maintenance
• They typically have more complex user experience and require stakeholders to log in to the platform to consume the dashboards

Common BI solutions include: 

1. Tableau: Known for its powerful data visualization capabilities
2. Looker: Excels in data modeling and embedded analytics
3. Power BI: Integrates well with other Microsoft products
4. Sigma Computing: Offers a spreadsheet-like interface for cloud data

BI platforms are essential for standardizing key metrics and reports across an organization, but may lack flexibility for more advanced or exploratory analyses.

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EDA platforms: Definitions, use cases, and popular tools 

Exploratory Data Analysis platforms are tools that allow data practitioners to investigate datasets, discover patterns, test hypotheses, and perform ad-hoc analyses. They typically offer more flexibility and analytical power compared to BI platforms.

When conducting A/B test analysis, pricing optimization, or churn prediction, data scientists often turn to EDA platforms. These tools allow for deeper, more strategic analyses that go beyond standard reporting.

Pros of EDA solutions: 

• They offer greater flexibility for complex analyses
• They support a wide range of statistical and machine learning techniques
• They allow for rapid iteration and experimentation
• They can handle diverse data types and sources

Cons of EDA solutions: 

• They often require more technical skills to use effectively
• They may lack the standardization and governance features of BI platforms

Common EDA solutions you’ll see most often: 

1. Traditional Analytics Platforms:
   • Jupyter Notebooks: Popular for data science and machine learning tasks
   • RStudio: Favored for statistical analysis and data visualization
2. Agile Analytics Platforms:
   • Fabi.ai: Combines SQL, Python, and AI for efficient data analysis
   • Databricks: Offers integrated notebooks for big data analytics
   • Mode: Provides a collaborative environment for SQL, Python, and R analysis

EDA platforms provide the flexibility and analytical power needed for complex, ad-hoc analyses, but may require more technical expertise than BI tools. Agile analytics platforms aim to bridge the gap between traditional EDA tools and BI platforms, offering both power and accessibility.

Let’s get into some specific types of reports and analyses and which tools to use for each.

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Overview of various types of analyses

Beyond “source-of-truth” dashboards and EDA, there five different types of analyses to consider depending on where you are in your data journey and what kind of metrics you’re looking to report against: 

1. Historical reporting: Analysis of past data to provide standardized, accurate metrics across an organization. Examples: Revenue trends, churn rates, funnel conversions, executive dashboards with KPIs.
2. Predictive analyses: Using historical data and complex modeling techniques to forecast future trends and outcomes. Examples: Sales forecasts, churn prediction, lead scoring.
3. Correlative analysis: Exploration of relationships between different data points to identify influential factors in business outcomes. Examples: Analyzing how marketing spend affects revenue, or how product features impact user engagement.
4. Sentiment analysis: Evaluating the emotional content of text data using Natural Language Processing techniques. Examples: Analyzing customer reviews, social media posts, or survey responses.
5. Ad hoc analysis: Flexible, on-demand data exploration and report creation to address specific business questions or hypotheses. Examples: Investigating a sudden drop in user engagement, or analyzing the impact of a new product feature.

Let’s take a look at the best tool suited to each analysis type. 

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When to use BI vs EDA platforms 

Choosing the right data analysis tool is crucial for effectively leveraging your organization's data. Business Intelligence (BI) platforms excel at providing standardized, consistent reporting across an organization, while Exploratory Data Analysis (EDA) platforms offer more flexibility for complex, ad-hoc analyses. Within EDA platforms, traditional tools cater to users with strong technical skills, while agile platforms aim to balance power with ease of use. Understanding the strengths of each platform type is key to selecting the right tool for your specific analysis needs.

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Historical reporting 

Verdict: BI tools are best here if you already have your data centralized and have established core metrics. If neither is true, you may be better off with an agile EDA platform that offers greater flexibility. Traditional EDA tools might be overkill for this type of analysis.

Every business needs to be aligned on certain key metrics: revenue, retention, churn, sales funnel conversion etc. These types of metrics have a few points in common:

1. They must be accurate and everyone must look at the same numbers
2. These numbers tend to be historical looking, not forward looking, so in general they can be answered with some SQL or spreadsheets

For these types of reports, it’s important to go with a solution that allows the data team to set strict guardrails and define metrics rigorously. These kinds of definitions are typically managed in a semantic layer (if you’re new to this concept, simply think of this as a dictionary that records how metrics are defined within your organization). 

A good BI solution will either have their own integrated semantic layer or connect to a third-party semantic layer (such as dbt or Cube), where the data team can define metrics in a version-controlled environment. This is where traditional BI shines with solutions like Looker, Tableau, PowerBI or newer solutions like Sigma Computing, Omni or Hashoard.

It's worth noting that nearly all traditional BI solutions work best when all your data is centralized into a single data warehouse. So if you haven't done that yet, you either need to plan on doing so or you may want to start building reports with an agile EDA platform before you build out your complete solution. Agile EDA platforms are a great way to experiment with reports and metrics before doing all the hard data engineering work you would need to do in order to surface metrics in BI.

A word of caution: If you haven't yet set up your BI solution, it's generally good to do some planning or talk to peers before jumping in. Once you've started defining your business metrics in one place, it can be hard and costly to migrate to a different solution later.

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Predictive analyses

Verdict: Predictive analyses require complex data modeling, which is best supported with EDA platforms. Traditional EDA tools like Jupyter notebooks excel here, but agile EDA platforms can make these analyses more accessible to a wider range of users.

Profit-driving insights generally come from your ability to predict where things are going. However, predictions can be complex and require advanced data analysis solutions which may involve things like machine learning and statistical analysis.

For example, if you want to forecast sales based on historical trends and patterns you may want to use a time series analysis model. If your data has no historical patterns (stationary), you may want to use an exponential smoothing model such as an Autoregressive Integrated Moving Average (ARIMA) model, or if your data has seasonal patterns (non-stationary), a Holt-Winters model. Or perhaps you want to predict customer churn in which case you may want to use a supervised learning model or a survival analysis model (we touch on that in detail in a previous article).

Even without a PhD in machine learning, Python and R offer plenty of pre-built packages and libraries that can get you up and running quickly for any of these types of predictions. Traditional EDA tools like Jupyter notebooks are powerful for these tasks, but require strong coding skills. Agile EDA platforms like Fabi.ai aim to make these analyses more accessible by providing seamless integration of SQL, Python, and AI, allowing you to have your first model running in minutes.

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Correlative analysis

Verdict: Correlative analyses require complex data modeling, which is cumbersome to set up within the rigid structure of most BI tools. Both traditional and agile EDA platforms excel here, with agile platforms offering a more user-friendly approach. 

Figuring out correlations in your data to understand what levers you can adjust to move the business is fundamental. You and your stakeholders likely have hypotheses, but it’s important to validate those. This type of analysis requires a lot of exploratory data analysis. 

You may need to slice and dice the data a number of different ways until you land on a nugget. AI is perfectly suited for this, allowing you to quickly iterate through hypotheses, or even doing things like clustering or to see if there are hidden patterns in your data. You may also want to tap into advanced statistical and machine learning algorithms like Pearson's product-moment coefficient and correlation matrices.

This type of work tends to be very ad hoc in nature, so it's not well suited for traditional BI, which can require a lot of overhead to set up and is rigid in its ability to do advanced data analysis. In contrast, both traditional and agile EDA platforms give you the flexibility and power for advanced analysis. Traditional EDA tools offer the most flexibility but require strong coding skills. Agile EDA platforms aim to provide similar capabilities with less technical overhead, often integrating SQL, Python, R, and AI in a more user-friendly interface.

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Sentiment analysis

Verdict: Sentiment analysis requires NLP models and advanced data science skills. While traditional EDA platforms offer the most flexibility, agile EDA platforms can make these advanced analyses more accessible and cost-effective for a wider range of users. 

Sentiment analysis is a unique sub-category of data analysis which requires Natural Language Processing (NLP). 

For example, if you want to gather positive or negative sentiments from a customer survey, or group reviews by sentiment, you will need to use an NLP model. Doing this effectively necessitates some form of machine learning model. If you want to do some simple classification based on sentiment, you could consider using Support Vector Machines (SVMs) or random forecasts leveraging the Scikit-learn Python library. Or if you want to summarize the text, you could use PyTorch to build a transformer.

A lot of these models can be quite complex and require advanced machine learning and data science skills, making traditional EDA platforms like Jupyter notebooks a popular choice for experts. However, Large Language Models (LLMs), which most new AI solutions are based on, are custom transformer models that have already been trained and deployed. Agile EDA platforms that integrate these tools, such as OpenAI's ChatGPT or Anthropic's Claude, can make this kind of analysis much more accessible with little to no data science experience required.

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Ad hoc analysis

Verdict: Ad hoc analysis often requires many different types of complex analysis and real-time collaboration with stakeholders. While both traditional and agile EDA platforms excel here, agile platforms often provide better support for collaboration and quick iterations.

We touched on the need for data centralization above, but it’s worth exploring a bit further. Traditional BI requires all your data to be centralized and somewhat cleaned. This is generally considered best practice, and there are quite a few data engineering and data warehousing tools at your disposal to do this. 

However, sometimes you want to explore your data or prototype your reports before you do all that heavy lifting. Even if these types of reports don't require any sort of advanced machine learning or data science, you might be better off doing this in an EDA platform for a few different reasons:

1. You can merge data from different sources before you do any sort of data engineering
2. You can quickly iterate with the help of SQL, Python and AI to validate the value of the report before you spend hours, or even days and weeks building it out
3. You can collaborate in real time with your peers and stakeholders, which is typically not well supported in traditional BI

While traditional EDA platforms offer great flexibility for these tasks, agile EDA platforms often provide better support for collaboration and quick iterations, making them a strong choice for ad hoc analyses.

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When and why to choose Fabi.ai for your data needs 

While business intelligence tools are powerful and necessary for standardized reporting in data-driven organizations, they often fall short when it comes to agile, exploratory data analysis. 

On the other hand, traditional EDA tools like Jupyter notebooks offer great flexibility but can be challenging for less technical users and lack built-in collaboration features. This is where Fabi.ai, an agile data analysis platform, shines. 

Here’s a breakdown of why Fabi.ai is uniquely qualified to address the various types of analyses we've discussed:

• Historical reporting: While BI tools excel here, Fabi.ai offers greater flexibility for exploratory analysis and report prototyping. Its seamless integration of SQL, Python, and AI enables effortless multi-source data analysis, perfect for testing hypotheses before committing to a rigid BI structure.
• Predictive analyses: Fabi.ai's AI-powered assistance, including GPT-4 for instant code generation and error correction, makes complex predictive modeling more accessible and efficient. This dramatically reduces time spent on debugging and boilerplate code, allowing you to focus on strategic insights.
• Correlative analysis: Fabi.ai's flexible environment and rapid prototyping capabilities allow for quick iteration and hypothesis testing, essential for uncovering meaningful correlations. The platform's ability to easily integrate and analyze data from multiple sources with in-memory joins eliminates the need for complex ETL processes.
• Sentiment analysis: Fabi.ai's AI integration makes advanced NLP techniques more accessible, even for users without extensive data science experience. The user-friendly interface bridges the gap between data scientists and business stakeholders, making sophisticated analyses more widely available.
• Ad hoc analysis: Fabi.ai's real-time collaboration features, including inline commenting and version history, make it ideal for addressing unexpected analytical needs. The platform's scalability ensures it can handle everything from quick ad-hoc analyses to complex, multi-stage data pipelines.

By choosing Fabi.ai, you're not just selecting a tool – you're adopting a more efficient, collaborative, and insightful approach to data analysis. Whether you're a data scientist looking to streamline your workflow, an analyst seeking to uncover deeper insights, or a business stakeholder wanting to make data-driven decisions, Fabi.ai can help you turn your data into actionable intelligence.

Ready to transform your data analysis workflow? Explore how Fabi.ai can address your specific use cases and drive your business forward in today's data-driven landscape.