Semantic Data Modeling Workshop – Technical Information Modeling & Operationalization for Analytics, AI and Business Decisions
- Clarity: common data model for business & IT
- 1 day: operational information model (technical)
- Responsibility: RACI, lineage, quality targets
- Implementation: quick wins & roadmap with KPIs
- Knowledge management: centrally documented and reusable
What you get from the Data Modeling Workshop
Our data modeling workshop offers you clear benefits and practical results – beyond buzzwords and hype:
- Results-oriented instead of theoretical: We deliver tangible artifacts (models, definitions, RACI, lineage, action backlog) instead of abstract discussions.
- Over 20 years of project experience: data, analytics, governance and development - best practices that work in companies.
- Scalable thinking: models, processes and responsibilities designed so that further use cases and domains can be added.
- Strategy connection secured: What we develop fits the goal, budget, architecture and governance - seamlessly connectable for implementation.
Contents and procedure of the Data Modeling Workshop
Module 01
Goal, scope, stakeholders & process
Result: Coordinated framework with clear objectives, responsibilities and complete process/info view
- Kick-off: Clarify objectives, scope, roles (owner/steward/process) and KPIs
- Specify prepared use case; outline process, record pain points and information requirements/contexts for each step
Module 02
Governance & data flow
Result: Binding responsibilities and transparent data flows/system allocation
- Responsibilities per information context (data owner/steward/producer/consumer)
- Data lineage and system mapping: sources, destinations, storage locations, access points, interfaces
- Clarify compliance & responsibilities; document risks/dependencies
Module 03
Data quality & implementation
Result: Actionable roadmap with measurable quality targets and prioritized measures for direct transfer into everyday life
- Define quality dimensions (completeness, accuracy, timeliness, consistency); prioritize minimum requirements (thresholds) for each critical attribute
- Define monitoring approach, testing concept and corrective measures
- Action backlog with quick wins; roadmap (roles, schedule, KPIs, dependencies)
Module 04
Information modeling (conceptual/logical/technical)
Result: Conceptual/logical information model with clear definitions and relations
- Structured definition of business objects with classes, attributes and relationships
- Specify naming conventions, definitions and example values; derive cardinalities and integrity rules
- Reporting/analytics comparison (key figures, views, drill-downs)
For whom is the Data Modeling Workshop suitable?
This workshop is aimed at roles and teams who want to use data as a lever for better decisions and reliable implementation – from the specialist domain to IT.
- Business, data and analytics teams that establish overarching data management and governance
- Departments with a specific use case (e.g. sales, service, finance/controlling, operations)
- IT/Architecture, BI/Business Intelligence and Data Management, converting models into systems
- Managers who want to enable data-driven use cases and AI in specific areas
Packages for different levels of experience (examples – each package is individually tailored to your needs)
- Goal: Start quickly and with low risk, create common understanding, develop initial model structure
- Sample content: Clarify use case and scope, capture process and information needs, basic information modeling (objects/attributes/relationships), establish common vocabulary
- Result: Compact conceptual/logical model with definitions, initial RACI assignment and quick wins backlog for immediate implementation
- Goal: delve deeper into governance, lineage and data quality; ensure connectivity to architecture and analytics
- Sample content: Detailed model refinement (cardinalities, integrity rules), RACI per object/attribute, lineage and system mapping, data quality targets with monitoring approach, roadmap with KPIs
- Result: Reliable information model with clear responsibilities, measurable quality targets and implementable roadmap, connectable to DWH/MDM/BI and AI use cases
What you can do immediately after the Data Modeling Workshop
Companies need a clearly defined information model so that data can be uniformly understood and utilized along a specific use case. To achieve this, we translate the process into precise business objects, attributes and relationships. At the same time, we create clear responsibilities for maintenance, quality and decisions by clearly assigning data responsibilities (data owner/steward, RACI). This reduces frictional losses between the business department and IT.
On this basis, you receive immediately usable artefacts and a clear basis for decision-making in order to operationalize your data foundation and start follow-up projects in a targeted manner.
- Transparency regarding data collection, origin and system location eliminates interface and responsibility ambiguities and accelerates downstream implementations.
- Measurable data quality targets for each critical attribute enable targeted improvement, risk minimization and the verifiable success of the use case.
- The result is a sustainable information model based on use cases as a foundation for governance, analytics/reporting and scaling to other domains.
- A common vocabulary between business and IT shortens discussions, avoids misinterpretations and speeds up decisions
- Prioritized quick wins and a pragmatic roadmap enable immediate operationalization in day-to-day business.
- Semantic data models form the basis for AI systems to process and further develop company-specific processes and concepts beyond generic "knowledge".
Data Modeling Workshop: Group size, location & format, costs
To ensure that the effect, tempo and organization match, we coordinate the framework and depth with you.
- Group size: Up to 12 people (minimum 5 participants); larger groups on request
- Location & format: On site at your premises, remote or hybrid; on request in our office in Munich
- Costs: € 490 per person (plus travel expenses) or flat rate according to expenditure; on request
- Additional services: Optional - follow-up workshops, business case consolidation, roadmap refinement, support with implementation and services
About Ventum
With over 20 years of consulting experience, we combine in-depth expertise in the introduction of digital innovations such as artificial intelligence with tried-and-tested methods.
Use case-driven modeling
We start with the business objective - this creates models that accelerate decision-making and implementation.
Bridge between business & IT
Common vocabulary, clear responsibilities (RACI) and documented lineage reduce friction and misinterpretations.
Scaling secured
Information models and measures are compatible with other domains, analytics and AI development.
Governance that works
Practical policies, responsibilities and data quality targets - tailored to the operational reality of your company.
Over 20 years of experience
In-depth expertise in data and new technologies as well as company-wide implementation - best practices that work.
Hands-on instead of theory
Structured analysis, modeling exercises and concrete artifacts (models, backlogs) as output - no slide marathon.
Our references and projects in AI and data
Your experts in the Data Modeling Workshop
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- Results-oriented: tangible artifacts (models, definitions, responsibilities, lineage, action backlog) instead of theory.
- Over 20 years of project experience: data, analytics, governance, development - tried and tested best practices.
- Scalable thinking: models, processes and responsibilities can be connected to other use cases and domains.
- Strategy connection secured: in line with target, budget, architecture and governance - seamlessly into implementation.
TISAX and ISO certification for the Munich office only
Your message
FAQ - Frequently asked questions about the Data Modeling Workshop
No. We agree the depth and terminology in advance so that business, analytics and IT can model productively together. A brief preliminary discussion clarifies the objectives, use case, team structure and existing artifacts (e.g. process documentation, reports).
Ideally, the focus is on a prioritized case with clear benefits (e.g. reporting gap, KPI definition, AI project). If not, we start with a compact discovery: goals, scope, stakeholders, benefit levers – this is how we ensure relevance and focus.
A conceptual/logical information model with definitions, RACI per object/attribute, lineage and system mapping, prioritized data quality targets and an action backlog including quick wins and roadmap.
Yes, AI requires clear object definitions, reliable attributes, quality and responsibilities. The information model provides the structure to use AI in specific areas in a targeted manner and to scale use cases safely.
For each critical attribute, we define target values (e.g. completeness, accuracy, timeliness, consistency), prioritize according to risk/impact and derive a pragmatic monitoring approach with corrective measures – measurable and auditable.
Yes, in two half-days remotely (Teams/Zoom) with a concept board and shared templates. Onsite is recommended for fundamental questions because discussions and clarifications are quicker. We adapt to your situation.
On request, we offer review, implementation (policies, roles, standards), scaling to other domains and accompanying coaching. The goal is a living model that creates lasting value.