Limitations of Traditional Business Intelligence (BI)

First, what exactly is business intelligence (BI)? Here is how several business intelligence software platform vendors describe business intelligence as it is currently instantiated today:

Per IBM (IBM Cognos Analytics): Business intelligence (BI) is a set of technological processes for collecting, managing and analyzing organizational data to yield insights that inform business strategies and operations.

Per Microsoft (Microsoft PowerBI): Business intelligence (BI) uncovers insights for making strategic decisions. Business intelligence tools analyze historical and current data and present findings in intuitive visual formats.

Per Salesforce (Tableau): Business intelligence combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations make more data-driven decisions. In practice, you know you’ve got modern business intelligence when you have a comprehensive view of your organization’s data and use that data to drive change, eliminate inefficiencies, and quickly adapt to market or supply changes. Modern BI solutions prioritize flexible self-service analysis, governed data on trusted platforms, empowered business users, and speed to insight.

Per Google (Looker for BI): Business intelligence (BI) is the process of using the power of people and technologies to collect and analyze data to be used by organizations in their strategic and daily decision-making processes.

Per Qlik (Qlik BI Platform): Business intelligence (BI) refers to the use of technology, tools, and processes to collect, analyze, and present business data for informed decision-making. It transforms raw data into meaningful insights, helping you to quickly identify trends, resolve problems, and grow revenue. Modern BI tools leverage artificial intelligence (AI) and machine learning to automate analysis, unearth hidden patterns, and predict future trends, empowering you to make informed decisions faster.

Here is my synthesis of a definition of traditional business intelligence:  Business Intelligence (BI) is a technology-driven process that analyzes historical and current data to help organizations make informed, data-driven decisions. It transforms raw data into actionable insights through tools like dashboards, reports, and data visualization, covering areas such as customer behavior, sales, and operational efficiency. BI is answers questions about "What happened?" (past and present)

Note that BI is not the same as FP&A (Financial Planning and Analysis).  FP&A tends to be future oriented. "What will  happen?" (future oriented)

Here is the 2025 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms:

How does traditional BI software work?

  1. Information is collected by multiple separate software applications and put into relational databases.
  2. The information from separate databases is combined into one "data warehouse". ETL processes are used to extract, transform and sometimes "clean" data, and then load the data into a data warehouse.
  3. Data analysis and reporting is done from the single data warehouse using OLAP based "cubes" of data; dynamic pivot tables.
OK, now for the limitations of traditional BI:
  • No Global Standard for BI: There is no global open industry standard for business intelligence tools. There are some semi de facto standards and industry norms; but no standards.
  • Rigid Data Models: BI relies on predefined schemas, cubes, and joins. Any change in business logic, hierarchy, or metric definition requires redesign. The redesign tends to have to be done by a knowledgeable technical person, not a business analyst.  This rigidity makes BI slow to adapt to evolving business meaning.
  • Siloed Cubes and Data Stores: Each data cube in BI is its own little world. Cross cube analysis requires that dimensions be manually aligned, hierarchies aligned, granularity be aligned, and terminology used by each cube to be aligned.  This results in duplication and inconsistencies.
  • Weak Semantic Understanding: BI tools and platforms understand structure, not meaning. Things such as "Customer,” “Product,” “Revenue,” “Period” are just column names, not concepts. Because of this, BI cannot reason over relationships, BI tools are not aware of semantic conflicts, meaning cannot be reused across systems unless humans make manual adjustments.
  • Contextual Understanding Limited: BI answers "what happened", not "why" or "how" things relate. Because of this, relationships cannot be inferred, traversing relationships is not possible without manual intervention, business rules are not clear, and inconsistencies in meaning cannot be detected by BI tools.
  • Local Orientation: Just like BI cubes are their own little world; a BI platform is also its own little local world and cannot be connected to other public or private systems of information. Traditional BI systems store data in local context only. Traditional BI does not store information.
  • Table Centric: Traditional BI is fundamentally table‑centric or you can think of this as document centric.  BI does not understand the information in the table, it only understands the structure of each individual table in each individual cube of data.  Because of this, traditional BI is poor at representing networks, hierarchies, multi-dimensional relationships, recursive structures. Artificial intelligence can struggle at understanding those tables.
  • High Dependence on Human Interpretation: Traditional BI tools have a hard time interpreting the data in BI cubes.  Because of this, business analysts must interpret what the data means; how things relate to one another, what the data means, how hierarchies roll up, how to reconcile inconsistencies.  This required human intervention creates bottlenecks and increases risk.
  • Limited Understanding: Traditional BI tools and platforms are powerful for certain types of reporting, but very limited in terms of understanding exactly what it is reporting.re weaknesses stem from one root cause.
  • Limited Understanding of Mathematical Relations: Traditional BI platforms understand what a "roll up" is in many cases, usually because it created the roll up relation using its OLAP capabilities.  But traditional BI does not understand what a "roll forward" is, what a "variance" is (i.e. difference between "actual" and "budget"), what a "restatement" is (difference between an originally stated number, adjustments to that number, and a restated number which corrects for an error), or arbitrary mathematical relationships (i.e. arithmetic).  There is no ability to express these mathematical relationships using traditional BI.
  • Read Only: Traditional BI is read only.  You cannot write information, you cannot edit information, you cannot correct mistakes, you cannot delete information.
  • Cannot Share Rules: Business rules are not sharable between cubes of information (remember, each cube is an a separate world) and there is no standard approach for representing business rules.
  • Forced to Use OLAP: Traditional BI forces the use of OLAP.
  • Poor at Handling Text: Traditional BI is fundamentally bad at handling text because it was never designed for it text, it was designed for handling numbers.
  • Cannot Exchange Cube Models or Cubes: Traditional BI does not really enable the ability to exchange cube models or cubes between systems.
  • Multidimensional Model is Limited: The traditional BI platform multidimensional model is not only not standard (there are best practices, Kimball Dimensional Modeling Techniques); the multidimensional model itself is rather limited. For example, you cannot do things like model "negated dimensions" (exclude members from a cube) or construct hierarchies of members.
  • Cubes not Compostable:  With traditional BI, each cube is its own universe unless you manually enforce conformance. This is one of the biggest limitations compared to knowledge‑graph type approaches.
  • Focus on Data: Traditional BI tends to be focused on data and data products, not information, information products, and knowledge products.
It is highly unlikely that traditional BI will be replaced any time soon.  But it is certainly very possible that new types of products might be developed.  Here is my vision.


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