Big data analytics and business intelligence: A comparison

BI vs. big data analytics

The emergence of BI software in the 1990s marked a shift from static reports to a systematic data analysis process delivering aggregated data and KPIs to business decision-makers. BI systems are typically built on top of data warehouses that store large volumes of historical business data optimized for analytical queries. These platforms essentially provide a structured model of the business to analyze.

To better support this mode of analysis, more user-friendly BI dashboards and interactive analytics applications were created. Their availability led to the development of self-service BI environments where business users run queries and analyze data independently, instead of relying on skilled BI professionals.

Today, BI encompasses a wide array of technologies and practices for collecting, storing, analyzing and visualizing structured data from different source systems. For example, AI-driven natural language interfaces that enable users to ask questions in plain English are now common. Built-in AI functionality also suggests follow-up questions, generates data visualizations and summarizes BI findings. However, BI's goal remains the same: to provide actionable insights and support business decision-making across all levels of an organization.

The subsequent emergence of big data analytics further expanded the possibilities for generating useful data insights. Descriptions of big data often focus on its scale and complexity, but it's about more than that. As first noted in 2001 by Doug Laney, then an analyst at Meta Group Inc., big data is typically characterized by the following three key elements, known as the 3 V's:

• Volume. Big data commonly involves data sets far larger than traditional data processing tools can handle.
• Velocity. Data is often generated and updated in real or near real time.
• Variety. Big data encompasses diverse data types, including mixed pools of structured, unstructured and semistructured data.

Different lists have since been created that extend these characteristics to include others -- most commonly veracity and value for a total of 5 V's. But the original three remain the most pertinent ones.

Because of big data's various attributes, it creates more complex data processing demands -- and offers more expansive analytics opportunities -- than BI data does. Big data analytics extracts insights that conventional BI methods either can't deliver at scale or might not discover at all, including hidden patterns in data sets, correlations between data elements and data trends over time. It's most often conducted by skilled data scientists using advanced tools and statistical techniques.

Key differences between BI and big data analytics

BI and big data analytics differ significantly in terms of data architecture, data processing methods and their analytical focus, as explained below.

Data architecture and data processing

BI applications primarily work with structured data sets pulled from internal sources -- such as transactional systems, SQL databases and spreadsheets -- and then organized in tables and stored in a data warehouse for querying and analysis. Query languages are used to produce summaries, reports and data visualizations. SQL is the most prominent one; others include Microsoft's Data Analysis Expressions, or DAX, language and Multidimensional Expressions, an older language commonly known as MDX that Microsoft also created.

In contrast, big data analytics often handles vast amounts of data in diverse formats from various data sources -- not only internal IT systems but also sensors, emails, images, external databases, social media and more. Some of these data sets require advanced processing techniques to generate insights. Others involve processing at such a scale that it requires distributing data analysis jobs across multiple servers and consolidating the results. The raw processed data is commonly stored in a data lake, where it can be analyzed as is or filtered and prepared for use in different analytics applications.

This level of processing is accomplished with a combination of big data technologies, machine learning algorithms and scripting languages such as Python and R. AI also now helps -- and it's blurring the boundary between processing structured and unstructured data. Large language models read documents, parse emails, interpret images and summarize conversations without requiring elaborate data extraction pipelines. Nonetheless, big data processing is still more complicated than the standard extract, transform and load procedures used to pull BI data into a data warehouse.

The frequency of data updates is another difference between BI and big data analytics. BI data summarizing historical performance is typically updated on a periodic basis: daily, weekly or monthly, for example. Big data analytics systems process and update data more frequently, including in real time.

Analytical focus

BI primarily focuses on descriptive and diagnostic analytics, which examine what has happened in the past and why to inform current business decisions. It also helps organizations monitor KPIs and identify operational improvements. As a result, corporate executives and business managers regularly use BI for strategic and tactical decision-making.

Big data analytics also involves such analysis, usually at a larger scale. However, it's most often used for forward-looking purposes, primarily focused on predictive analytics that forecasts future business scenarios and trends. Prescriptive analytics, which provides guidance on how to achieve a desired business outcome, is also done with big data. Common big data analytics applications include predicting market trends, enhancing customer experience and driving product innovation.

AI also blurs the distinction in analytical focus by enabling business users to perform some predictive analytics tasks that previously required data science skills. However, this area of AI-enabled analytics is still developing. For now, reliably interpreting big data analytics results still requires a sound understanding of data science principles.

How do BI and big data analytics benefit organizations?

Because BI and big data analytics serve different purposes and employ different technical methods, they provide distinct business benefits, which are outlined separately below.

BI: Structured approach to data analysis

Think of BI as a model-driven approach to data analysis that's often aligned with business processes. BI applications are built on predefined business models that capture a shared understanding of how an organization operates and are structured to mirror existing business workflows. The resulting insights relate to standardized KPIs and other metrics that can be monitored across the organization.

This structured analysis approach maps directly to strategic objectives, which enables the following:

• Consistent tracking of key business initiatives.
• Simplified management of corporate governance and compliance processes.
• Easier identification of bottlenecks and inefficiencies in business processes.
• Operational performance measurements against established benchmarks.
• Decision support for continuous improvement or quality management efforts.

Big data analytics: Exploratory data analysis approach

In contrast to BI's structured nature, big data analytics takes a more exploratory approach. Big data analytics applications are typically driven not by existing business models, but by the data as it's generated and collected. For example, they can discover patterns in raw data that reflect unforeseen changes in the market, customer behavior or a company's operations.

Data scientists use techniques such as hypothesis testing and predictive modeling to identify, test and analyze these data patterns or other data discoveries. The generated insights help identify business risks and opportunities as well as the need for new processes, enabling companies to respond faster to changing business conditions.

The table below summarizes the separate high-level benefits of BI and big data analytics for businesses.

How BI and big data can be integrated

The most effective organizations employ both analytics approaches. They use BI to help execute business strategies and optimize operational tactics, and big data analytics to drive strategic evolution, adapt to new developments and operationalize business innovation.

The two processes can also be integrated. Big data technologies can extract structure from unstructured data sources, enabling BI tools to work with the data. In addition, big data analytics results can be delivered as tables for use in BI tools. For example, a table containing predicted customer lifetime value scores could be embedded in a customer service dashboard.

Similarly, data scientists might integrate BI data into big data analytics workflows if their analysis work requires well-structured data. In industries such as financial services and healthcare, a data warehouse might contain customer or patient data carefully structured for regulatory compliance. A data scientist could use this cleansed and conformed data set to ensure the insights generated by an analytics application are actionable under the applicable regulatory framework.

A data lakehouse is a newer platform that supports these types of integrations. As the name suggests, it combines key features of data lakes and data warehouses, providing a single platform for both BI and big data analytics applications -- and, increasingly, for AI ones.

Examples of BI and big data analytics applications

The following table includes examples of applications for BI and big data analytics to show how they contrast with -- and complement -- each other.

AI: A possible third analytics approach

AI introduces analytical capabilities that differ from the ones BI and big data analytics provide. AI systems can work with data at any scale, from a single spreadsheet to massive data sets distributed across multiple systems. While BI answers questions through structured queries and big data analytics surfaces insights through statistical techniques, AI reasons about data through a form of analytical dialogue.

For example, a business analyst investigating a revenue decline might ask an AI tool a series of questions: what sales were last quarter by region, which behavior patterns predict customer churn, why the company's best customers are leaving and how to address that issue. The AI tool synthesizes information from multiple sources, draws inferences, proposes hypotheses and explains its reasoning in a conversational manner.

The analyst then iterates with the tool, refining questions, challenging interpretations and building toward a deeper understanding. AI can also suggest issues worth investigating or explain anomalies that statistical methods discover but can't interpret. Working with it feels more like collaboration than the traditional use of analytics tools.

Currently, AI primarily functions as a conversational layer to make BI and big data analytics applications more accessible and productive. However, progress in agentic AI development is creating the potential to embed AI agents in operational workflows and enable them to analyze data and take actions autonomously. Doing so would add a third analytics approach for data leaders and their teams to manage.

Editor's note: This article was updated in January 2026 for timeliness and to add new information.

Donald Farmer is a data strategist with 30-plus years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups.