The Evolution of Business Intelligence
Business Intelligence Is Becoming More Interactive
Traditional business intelligence provides a reliable foundation for reporting and decision-making. Data models, consistent metrics, and dashboards make trends visible and comparable.
Business Intelligence 2.0 expands on this foundation. Business units can explore data more independently, adjust their analytical approach during the evaluation process, and ask questions in natural language. Self-service BI, data exploration, conversational analytics, and AI-powered features are thus fundamentally changing the way people work with data.
What is Business Intelligence 2.0?
Business Intelligence 2.0 refers to the evolution of traditional business intelligence toward a more self-directed, exploratory, and AI-powered approach to data analysis. data models, key performance indicators, reports, and dashboards remain the reliable foundation.
The term is not used uniformly everywhere. It does not refer to a single product, but rather to a new form of analysis: business units can examine data more independently, address follow-up questions, and integrate new insights into their workflows.
| Category | Traditional BI | Business Intelligence 2.0 |
|---|---|---|
| Focus | Providing reliable answers to recurring questions | Exploring new questions and relationships |
| Analysis Approach | Predefined reports, views, and drill-downs | Flexible, sequential analysis steps |
| Interaction | Filters, metrics, and pre-built visualizations | Self-service, natural language, and AI support |
| Role of Business Units | Using reports and formulating requirements | Exploring data independently within clear rules |
| Result | Report, dashboard, or standardized metric | Evaluation, visualization, and documented analysis process |
| Foundation | Data models, key metrics, and permissions | The same foundation, extended for exploratory and AI-powered use |
What Role Does AI Play in Business Intelligence 2.0?
AI-powered data analysis can interpret questions in natural language, support appropriate evaluations, suggest visualizations, and summarize results. This particularly facilitates analytical pathways that emerge from interim results.
- Translating analytical questions into queries
- Suggesting appropriate visualizations
- Highlight notable trends
- Summarizing results for different user groups
Analytics agents supplement these functions with multi-step analyses and the controlled use of additional tools. The Data Insights page shows how HMS develops and integrates such applications.
The data foundation is key
AI enhances a robust BI landscape but does not replace a sound business and technical foundation. Reliable results require approved data, understandable data models, consistent metrics, and regulated access. Results with business or regulatory implications must also undergo business-side validation.
When BI 2.0 Becomes Relevant
For Which Companies Is Business Intelligence 2.0 Useful?
Business Intelligence 2.0 is particularly relevant when a solid BI foundation is in place, but business units regularly have to address new follow-up questions. Typical signs include frequent ad hoc queries, manual exports, and a growing need for self-service or natural-language analysis.
It makes less sense to jump right in if key metrics are inconsistent, data ownership is unclear, or access permissions aren’t reliably managed. In such cases, the BI and data infrastructure should be improved first.
Typical Application Areas
Understanding analysis needs and user groups
Determine which decisions need to be supported, where existing reports fall short, and what follow-up questions regularly arise. In doing so, consider who will be using the analyses and what level of subject matter expertise and data literacy is available.
Prioritize use cases
Select a use case that is relevant to the subject matter and clearly defined. The data, responsible roles, and expected results should be specific enough to allow for verification of the use case's value and quality.
Check the database and semantics
Evaluate data quality, timeliness, and source. Additionally, verify that metrics, relationships, units, and technical terms are clearly defined and appropriate for the use case.
Define target architecture, access policies, and governance
Define how data sources, semantic models, analytics interfaces, and AI functions interact. Establish binding rules for roles, permissions, quality checks, documentation, and business approvals.
Test the analytical method under controlled conditions
Test self-service BI, data exploration, conversational analytics, or AI-powered support using realistic questions and data. Review answers, queries, and visualizations from a technical perspective and make targeted improvements to the chosen solution.
Integrate the application and strengthen data literacy
Integrate the application into existing data, IT, and work processes. Empower users to work with data, key metrics, and analytical functions. This also includes critically evaluating results and understanding the capabilities and limitations of AI-powered analytics.
Organizing operations and further development
Define monitoring, support, and responsibilities for production operations. Regularly review data models, key metrics, access, and analysis functions, and gradually integrate additional use cases.
In addition to tool selection, the implementation process also involves domain-specific semantics, data ownership, and subsequent operations.
- An AI assistant cannot reliably resolve inconsistent metrics.
- Increased self-service requires clear responsibilities and understandable data models.
- A successful prototype is not yet an integrated and operational application.
- Automated results require appropriate validation and approval rules.
No. Reports and dashboards remain useful when questions and information needs recur. BI 2.0 supplements them with flexible analysis methods for new or changing questions.
Not necessarily. Many existing BI platforms can be enhanced with self-service, data exploration, or AI-powered features. Whether an add-on, a custom solution, or a new architecture makes sense depends on data models, user groups, integration requirements, and governance.
BI 2.0 is particularly relevant when a robust BI foundation is already in place and business units regularly address new follow-up questions. If key metrics are inconsistent, data quality is low, or data ownership is unclear, the foundation should be improved first.
Self-service BI enables business units to analyze data themselves within approved models. Data exploration refers to an open-ended analytical process in which new questions and analytical steps emerge from previous results.
Reliability depends on data quality, data models, metric definitions, and access permissions. Results with business or regulatory implications must be reviewed by subject matter experts.
The process begins with a clearly defined need for analysis. Next, the data foundation, key metrics, user groups, access permissions, and technical interoperability are reviewed. A prioritized use case can then be tested in a controlled manner and gradually rolled out into production.
From Classification to Implementation
Practical Development of Data Insights
Business Intelligence 2.0 describes how reporting and analysis are becoming more interactive. On the Data Insights page, you’ll learn how HMS evaluates existing BI and analytics landscapes, develops suitable applications, and integrates them into data models, access permissions, and operational processes.
