Understanding business intelligence

The term, business intelligence, itself has become somewhat opaque through years of industry use. Rather than describing a single, unified concept, business intelligence (BI) represents a portfolio of capabilities that work together to achieve a specific outcome: enabling people to know facts about their business using structured data.

The core components of business intelligence

Modern business intelligence tools typically perform three primary functions. First, they handle modeling, defining the semantic concepts behind structured data such as metrics, dimensions, and relationships between tables. This modeling layer creates a shared understanding of what the data means and how different pieces relate to each other.

Second, BI tools facilitate exploratory data analysis, the iterative process of investigating data to uncover useful insights. This work tends to be highly iterative and unpredictable, requiring flexibility and speed as analysts follow threads of inquiry wherever they lead.

Third, BI tools handle presentation, aggregating multiple data artifacts into cohesive narratives that can be shared across an organization. This presentation layer includes governance features like permission models that control who can see what information.

The relationship between these components has evolved considerably over time. Pre-cloud business intelligence tools often included their own data processing engines, competing on speed and local performance. With the shift to cloud data warehouses, this changed fundamentally. Local data processing became unnecessary, and BI tools adapted by focusing on their core strengths: helping people explore and understand data that lives in centralized cloud platforms.

The lifecycle of business intelligence artifacts

Understanding how BI artifacts mature helps clarify why organizations structure their data work the way they do. Analysis typically progresses through distinct phases, each with different requirements for governance and quality.

Exploratory analysis represents the starting point. When faced with a business question, data practitioners develop low-fidelity sketches to investigate possible answers. Most of this work gets discarded, so expectations around code quality and governance remain minimal. The priority is iteration speed and flexibility.

As certain exploratory work yields genuine insights, it transitions into personal reporting. The analysis has answered a question important enough to revisit later, but it's not yet ready for broader consumption. Some BI tools provide dedicated spaces for this personal work, areas where individuals can save and refine their analyses without formal review.

The requirements change dramatically when reporting becomes shared. Once a report reaches another person, governance becomes essential. The creator understands the context and limitations of their analysis, but others simply expect it to be correct. At this stage, the BI tool's governance layer determines access, and best practices around auditability and change tracking become necessary.

Finally, some shared reporting reaches production status. When artifacts support critical business processes, get accessed frequently by many users, or require agreed-upon service levels, they need to be owned and operated like any production data asset.

This progression creates what might be called a "production line" for business intelligence. The most valuable BI tools act as conveyor belts through these stages, making it straightforward to add capabilities (governance, dynamic filters, authentication) as work matures. This only functions smoothly when the entire process happens within the BI tool, starting from initial exploration.

The data foundation beneath business intelligence

Business intelligence tools sit atop a foundation of data transformation work. The ELT (Extract, Load, Transform) pattern has become standard in cloud environments: data gets extracted from source systems, loaded into a data warehouse, and then transformed for analytical use.

This approach provides significant advantages over older ETL patterns where transformation happened before loading. With ELT, raw source data remains available in the warehouse, allowing teams to iterate on transformation logic after the fact. Transformations become idempotent; running them multiple times on the same source data produces identical results. This makes it possible to recreate historical transformations, trace data model dependencies, and test changes before deploying them.

The structure of transformed data matters enormously for business intelligence. Dimensional modeling (organizing data into fact and dimension tables) has long been a standard approach. Facts represent actions or events (orders, payments, logins), while dimensions describe the entities involved (customers, products, locations). Whether to keep these tables separate or join them into wider tables depends on factors like the capabilities of BI tools, the SQL proficiency of end users, and the specific needs of different business departments.

Semantic layers and metric definitions

As organizations mature their data practices, they increasingly recognize the importance of semantic layers: centralized definitions of business metrics that ensure consistency across different tools and analyses. When "revenue" or "active users" means the same thing everywhere, trust in data increases and debates about whose numbers are correct decrease.

Semantic definitions become particularly valuable as they connect to the broader context of the data platform. Knowing not just what a metric means, but also how fresh the underlying data is, what quality tests it passes, and how it relates to other metrics, makes the difference between numbers people trust and numbers they question.

The rise of open standards for semantic definitions, like the Open Semantic Interchange, reflects growing recognition that metric definitions need to work across different tools and platforms. Organizations want to define metrics once and use them everywhere: in BI dashboards, in AI-powered analytics, in operational systems.

Challenges in business intelligence

Despite decades of development, business intelligence continues to present challenges. The ambiguity inherent in data modeling means teams must make judgment calls about how to structure their data. What should be a fact versus a dimension? How wide should tables be? These decisions depend on understanding both the data itself and what stakeholders need from it.

BI tool capabilities constrain what's possible. Some tools struggle with joins, making separated fact and dimension tables painful to work with. Others have performance limitations that make very wide tables impractical. The choice of BI tool shapes how data teams structure their work.

Storage and compute costs create tradeoffs. While cloud storage has become inexpensive, compute remains costly. Keeping tables separate means more joins and higher compute costs. Combining tables into wide, pre-joined structures reduces compute but can make the data less flexible.

Governance and security add complexity, particularly as data gets shared more broadly. Row-level security, column masking, and access controls all need to be implemented and maintained. As the number of data consumers grows, managing these permissions becomes increasingly challenging.

The emerging role of AI in business intelligence

Recent advances in large language models have made conversational analytics more practical.

AI excels particularly at exploratory data analysis, the iterative process of investigating data to find insights. When supplied with appropriate context about data structures, semantic definitions, and business logic, AI can write analytical code faster than humans. This doesn't replace the analytical thinking and business judgment that data practitioners provide, but it accelerates the mechanical work of writing queries and generating visualizations.

The key to making AI useful for business intelligence lies in providing context. AI needs to understand what data exists, what it means, how trustworthy it is, and how different pieces relate to each other. This context comes from the metadata generated by transformation tools, the semantic definitions in metric layers, and the documentation that describes business logic.

Protocols like the Model Context Protocol (MCP) are emerging to standardize how AI systems access this context. Rather than building point-to-point integrations between every AI tool and every data source, these protocols create a common interface that any AI system can use to understand available data.

Best practices for business intelligence

Successful business intelligence requires attention to several key areas. Documentation matters enormously, both for human users and for AI systems that might help with analysis. Clear descriptions of what data represents, how it's calculated, and what limitations it has make the difference between data people trust and data they question.

Data quality testing should be comprehensive and automated. Tests for uniqueness, completeness, and referential integrity catch problems before they reach end users. Custom tests for business-specific rules ensure data meets organizational standards.

For larger organizations, modular data architecture helps maintain quality at scale. Breaking monolithic data projects into smaller, domain-specific pieces with clear ownership and contracts between them makes it easier to evolve data products without breaking downstream dependencies.

Iterative improvement based on usage patterns helps BI systems stay relevant. Monitoring what questions users ask, what data they access, and where they encounter problems reveals opportunities to improve documentation, add new data sources, or restructure existing models.

Version control and change management for transformation code enables teams to track changes, roll back problems, and understand how data definitions have evolved over time. Treating analytics code with the same rigor as software code (including code review, testing, and deployment processes) reduces errors and increases confidence.

The path forward

Business intelligence continues to evolve as technology changes and organizational needs grow. The shift from on-premise to cloud infrastructure fundamentally altered how BI tools work. The emergence of AI is creating another shift, particularly in how people explore and interact with data.

What remains constant is the need for reliable, well-governed data that people can trust and understand. The specific tools and techniques may change, but the fundamental challenge (turning raw data into insights that drive better decisions) persists. Organizations that invest in solid data foundations, clear semantic definitions, and robust governance will be best positioned to take advantage of new capabilities as they emerge.

Frequently asked questions

What is business intelligence?

Business intelligence refers to the collection of processes, technologies, and practices used to transform raw data into meaningful insights that inform business decisions. It encompasses the extraction of data from various sources, its transformation into usable formats, and its presentation to stakeholders who need to understand what's happening in their organization. Rather than describing a single concept, business intelligence represents a portfolio of capabilities that work together to enable people to know facts about their business using structured data.

What are the key components of a business intelligence system?

Modern business intelligence tools typically perform three primary functions. First, they handle modeling, defining the semantic concepts behind structured data such as metrics, dimensions, and relationships between tables. Second, they facilitate exploratory data analysis, the iterative process of investigating data to uncover useful insights. Third, they handle presentation, aggregating multiple data artifacts into cohesive narratives that can be shared across an organization, including governance features like permission models that control who can see what information.

How is generative AI being applied in business intelligence tools to enable natural-language querying and generate actionable insights?

AI is beginning to reshape how people interact with business intelligence by making conversational analytics more practical, where users can ask questions in natural language and get accurate answers. AI excels particularly at exploratory data analysis by writing analytical code faster than humans when supplied with appropriate context about data structures, semantic definitions, and business logic. The key to making AI useful lies in providing context through metadata from transformation tools, semantic definitions in metric layers, and documentation that describes business logic. Emerging protocols like the Model Context Protocol create standardized interfaces for AI systems to access and understand available data.