At a glance
- Data queries via natural language instead of technical query languages.
- AI and Natural Language Processing (NLP) translate questions into structured database queries.
- Answers are output as text, table, or visualization.
- Significantly lowers the barrier to entry for data-based decisions.
- Can be integrated into BI systems, dashboards, and enterprise applications.
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Explanation and classification
Conversational Analytics is a sub-discipline of Augmented Analytics and is used in BI systems that enable dialog-oriented interaction with data. Instead of configuring reports manually or formulating database queries, a user simply asks a question – for example "How did revenue develop in the second quarter compared with the previous year?" – and receives an immediate, contextually prepared answer.
The term combines two developments: the increasing maturity of NLP models (Natural Language Processing) and the maturity level of modern BI platforms. Conversational Analytics is therefore not a standalone product but an interaction layer placed on top of existing data infrastructures.
In the corporate context, this development is significant because it removes the traditional boundary between technical database specialists and business users. Controllers, sales managers, or managing directors can access data directly without going through IT or BI teams.
„In our monthly review, we pose our variance questions directly in everyday language and immediately receive the drivers as a waterfall. This noticeably shortens our discussions, because we already have the answer during the meeting.“Head of Controlling
Industrial company
How does Conversational Analytics work?
Conversational Analytics is based on several technical layers that must interact seamlessly:
- Natural Language Processing (NLP): NLP models analyze the user's input, identify entities (for example "revenue," "Q2," "Germany"), understand temporal references and comparison operators, and derive a structured query from them.
- Semantic Layer / Data Semantics: A central data model – often referred to as a semantic layer – ensures that terms from everyday language are mapped correctly to database fields, KPIs, and dimensions. Without this layer, NLP systems produce faulty or meaningless queries.
- Query Generation: From the interpreted request, a formal query is automatically generated – in SQL, MDX, or a platform-specific query language – and executed against the data model.
- Natural Language Generation (NLG): The result is not only output as a table or chart but can also be translated into comprehensible prose by NLG components: "Revenue in Germany rose by 12 percent in the second quarter compared with the same quarter of the previous year."
- Context Memory / Dialog Capability: Advanced systems maintain the conversational context, so that follow-up questions such as "And how was that in Austria?" are interpreted correctly without having to specify the context again.
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What does Conversational Analytics mean in a corporate context?
- Problem 1 – IT and reporting bottleneck: In many companies, analysis requests end up as tickets with the IT team or central reporting units. The waiting time until a response often amounts to days. Conversational Analytics enables ad hoc evaluations in seconds – without involving IT.
- Problem 2 – Low BI adoption: Many BI systems fail due to a lack of use. Complex user interfaces, training effort, and a lack of intuitiveness lead business users to avoid reports. A natural-language interface significantly lowers the barrier.
- Problem 3 – Time-critical decisions: Controlling and management need answers not the next morning, but often immediately in the meeting. Conversational Analytics delivers answers faster than ever before.
Conversational Analytics vs. conventional BI: a comparison
| Criterion | Traditional BI | Conversational Analytics |
|---|---|---|
| Entry barrier | High (training, technical knowledge) | Low (natural language) |
| Query method | SQL, MDX, graphical filters | Free text, voice input |
| Response time | Minutes to days (depending on IT involvement) | Seconds |
| Target group | BI specialists, power users | All employees |
| Flexibility | High for preconfigured reports | High for ad hoc questions |
| Context understanding | None | Dialog-capable, context-sensitive |
| Error risk | Low (with good data models) | Medium (depending on NLP quality and semantic layer) |
| Integration complexity | Established | Depends on system maturity |
What are common mistakes and how do you avoid them?
What are typical areas of application and use cases?
Where would you like to use Conversational Analytics?
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Practical example: Conversational Analytics with Bissantz
Scenario: variance analysis in the monthly controlling review
A mid-sized industrial company uses DeltaMaster as its central BI platform for planning, reporting, and analysis. In the monthly review meeting, the CFO asks: "Why is gross profit in the South region 8 percent below plan?"
Without Conversational Analytics, this process would require several steps: the controller opens DeltaMaster, navigates to the relevant report, filters on the South region, compares planned and actual values at the product level, and manually identifies the drivers of the variance. This takes several minutes – even for experienced users.
With a natural-language interface that sits on top of the DeltaMaster data model, the system responds immediately: it identifies that two product groups deviate disproportionately, names the price effect as the dominant factor, and provides the variance components as a waterfall chart.
In DeltaMaster, Bissantz & Company pursues the approach that analyses must not be slowed down by technical complexity. The consistent use of the OLAP data model as the basis for queries – combined with automatically generated analyses and comments – ensures that Conversational Analytics is possible cleanly and reliably on valid data. In this way, Bissantz delivers reliable analysis results and recommendations for action fully based on language as well.
Business intelligence with AI functions Our software uses integrated AI functions for data integration, analysis, planning, and reporting. This turns your data into a clear basis for decisions across the entire company. You see faster what is truly relevant and can make business decisions more efficiently and with greater confidence. AI in business intelligence
| Function | Benefit |
|---|---|
| OLAP data model as the basis for queries | ensures reliable analyses based on valid data |
| Automatically generated analyses | identify variances and drivers without manual navigation |
| Automatic commentary | delivers language-based explanations and recommended actions |
| Waterfall chart for variance analysis | presents variance components clearly |
| Integrated AI functions | support data integration, analysis, planning, and reporting |
„Our business departments access the data model directly, without a detour through us, and formulate their analyses in language. Because DeltaMaster is built on a clean OLAP model, we get reliable results instead of faulty ad-hoc queries.“Head of IT
Food producer
Frequently asked questions
Conversational Analytics explained simply – how can I picture it?
Imagine typing a question into a chat window: "How much revenue did we make last month?" The system understands this question like a human, finds the relevant data, and answers with a number, a chart, or a short sentence – without you needing to know where the data is stored or how to query it technically.
What distinguishes Conversational Analytics from a simple chatbot?
A conventional chatbot answers predefined questions from a script. Conversational Analytics, on the other hand, understands open, variable questions and dynamically accesses real company data in doing so. The answers are not prefabricated but are calculated in real time from the data model.
Do I need a new BI platform for Conversational Analytics?
Not necessarily. Many Conversational Analytics solutions sit as a layer on top of existing BI systems – provided a clean semantic layer is in place. What matters is the quality of the data model, not just the choice of platform.
How secure are natural-language data queries with regard to data protection and access rights?
Professional solutions adopt the existing role concept of the BI platform. Through Conversational Analytics, a user sees only the data they are otherwise authorized to access. Nevertheless, the authorization concept should be explicitly reviewed and documented for the conversational layer.
How does Bissantz support Conversational Analytics?
Bissantz provides a structured, semantically rich OLAP data model that forms the basic prerequisite for reliable natural-language queries. Automatically generated texts and comments in DeltaMaster explain variances and suggest options for action without an analyst having to manually evaluate data. Bissantz also supports companies in an advisory capacity with such AI use cases.
Is Conversational Analytics also suitable for smaller companies?
Yes, provided a structured data base is in place. The barrier to entry drops with the increasing availability of cloud-based NLP services and pre-integrated BI connectors. Smaller companies benefit in particular from being able to save on dedicated reporting resources, while business users access data independently.
Summary
Conversational Analytics makes data analysis accessible without requiring technical prior knowledge. Through natural-language queries, business users, controllers, and executives receive valid answers from the company data model in seconds – directly within the workflow and without going through IT or reporting teams. The decisive success factor is not the AI interface itself but the quality of the underlying semantic layer and data model. With its structured OLAP model and automatically generated analysis texts, DeltaMaster from Bissantz & Company delivers Conversational Analytics functions for efficient business intelligence.
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