When the Dashboard Leaves the Next Question Unanswered
Reports and dashboards reliably answer recurring questions. In day-to-day work, however, a result often gives rise to new questions.
Predefined reporting and analysis paths aren’t always sufficient for this. Creating new reports takes time. Manual exports can take analyses out of the controlled BI environment.
Business units can progressively explore and compare data and formulate follow-up questions based on the results.
Interactive Data Analysis
Data Insights enhances existing BI environments with interactive analyses powered by AI agents. Business units can progressively narrow down and compare data and derive new questions directly from the results.
This turns a business question into a traceable analysis process that can be explored step by step.
Reports, dashboards, and self-service analytics remain the right approach for recurring information needs. Data Insights complements this foundation in cases where business units want to explore new questions and continue their analyses flexibly.
This does not replace business intelligence; rather, it expands it to include interactive and AI-powered forms of analysis.
Learn more about Business Intelligence 2.0
Our Services
Further Developing BI and Analytics Environments
Within Data Insights HMS combines traditional business intelligence, interactive data analysis, and agentic analytics capabilities. We further develop data models, reporting, and analytics applications, and integrate analytics agents into existing data and process environments.
What Sets Reliable Analyses Apart
The key lies in the interplay of technical, subject-matter, and organizational rules.
- Controlled Data Foundation
Approved data, domain definitions, and consistent metrics form the foundation. - Governed Access
Roles and permissions control which data and analysis functions may be used. - Traceable analysis steps
Relevant data sources, filters, and queries are documented in accordance with requirements. - Domain Expertise
AI supports the analysis. Evaluation and decision-making remain the responsibility of the relevant departments.
1. Determine the Analysis Needs
We examine which questions business units need to answer, where existing reports reach their limits, and which results are required.
2. Assess the Data Foundation
We review data quality, key metrics, data models, access permissions, and technical interoperability.
3. Define the Architecture
We design the interplay between data access, semantics, the analytics interface, AI capabilities, and governance.
4. Develop the Application
We develop reports, custom analytics applications, conversational analytics, and specialized analytics agents.
5. Validate Results and Quality
We test queries, results, and access controls. Quality procedures and relevant evaluation steps are documented in accordance with the requirements.
6. Prepare for Operations
We integrate the application into the existing system landscape and take monitoring, support, and further development into account.
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Why HMS
Bringing Together BI, Analytics, and AI Engineering
Data insights require more than just a new analytics interface. Data models, software architecture, AI capabilities, and business requirements must all align.
HMS brings together:
- Business Intelligence and Reporting
- Data Science and Analytics
- Software Engineering and Integration
- Governance and Quality Assurance
- Deployment and Production Operations
We provide consulting services independently of individual technology providers. We make decisions based on the specific use case, the existing system landscape, and long-term maintainability and operability.
No. Existing data models, dashboards, and BI platforms can often be reused. We assess which components are viable and where additions would be beneficial.
Conversational analytics is suitable when users want to ask questions about a governed dataset using natural language. The quality of the responses depends largely on the data model, domain-specific semantics, and permissions.
A chatbot primarily engages in dialogue and answers questions. Analytics agents also access approved data models and analysis tools, ask follow-up questions, and support sequential analysis steps.
Agentic Analytics goes beyond individual interactive queries. Analytics agents can plan multi-step analysis tasks, use approved tools, and leverage interim results for further analysis steps. The scope and level of autonomy are defined for each specific use case.
Yes. However, these two types of data require different approaches. Structured analytical data can be supplemented in a targeted manner with documents, texts, or other unstructured content.
Data sources, permissions, queries, filters, and relevant analysis steps are logged as required. The specific artifacts to be versioned are determined during the design phase.
