Services
Data & AI Governance
Gain, shape, and retain control over your data and AI assets.
Data Management
Develop your capabilities for the systematic, scalable planning, acquisition, distribution, and management of your data assets and algorithms.
Data & AI Regulation Management
Bring clarity to the jungle of data and AI regulations—and translate complex requirements into a unified framework with fit-for-purpose controls for your organization.
Data Strategy
Develop data-strategy capability across all levels—transformational, transactional, operational, and conceptual—and put an end to the perpetual call for “a new data strategy.”
Data Architecture
Bring order to data chaos—structurally and organizationally. Create a modern, holistic view of your information and data.
Data & AI Platforms
Create sound decision foundations for your data platforms: from analysis and design through to a technology-agnostic selection process for scalable, governance-ready data-management and AI architectures.
People at the Center -
Change Management in Data Organizations
Anchor change where it needs to take effect: in the mindsets, processes, and structures of your data organization. Engage your stakeholders, cultivate new roles and skills, and provide direction and impact through a clear, co-creative process.
Our Competencies and Capabilities
FOR DATA-DRIVEN PROJECTS AND ORGNIZATIONS
Design, build, transform and grow your data organization towards effectiveness and performance. Optimize and integrate data processes into your general business, trim for measureable scalability. Don’t let experts tell you it doesn’t work.
Build strong data teams and integrate new data capabilities into the existing organizational set-up.
Identify, evaluate, plan and implement appropriate methods,procedures and technical tools to professionalize and scale your data organization.
Understanding and addressing gaps in data excellence. Transform the non-specific buzzword “Data Literacy” into effective learning.
Data & AI Governance
- Gain, shape, and maintain systematic control over your data- and AI-driven assets, capabilities, technologies, and rights.
- Establish and embed both business and technical accountability for your data and AI portfolios — and reinforce these responsibilities with effective governance instruments.
- Consolidate and operationalize diverse regulatory requirements for data and AI so that your standard processes remain scalable and exceptional cases can be handled quickly and consistently. Do not allow exceptions to dominate your day-to-day operations.
- Maintain sovereignty over your data and AI applications even when making them available within external data ecosystems.
- Create transparency and control across your data- and AI-related risk landscape.
- Ensure that data and AI requirements are governed systematically across business units and domains, and supported by efficient data and AI management structures.
Data Management
- Design, expansion, or realignment of your data management operating strategy and its implementation.
- Development, transformation, and scaling of operational capabilities for systematic and manageable data management within the organization and across partner networks.
- Structured requirements engineering in data management for business, process, and technological requirements.
- Identification, evaluation, piloting, and integration of suitable data management tools and methods in on-premise and cloud environments.
Data & AI Regulation Management
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Analysis of existing application architectures and development of target scenarios for your future data and AI platform — in the cloud, hybrid, or on-premise.
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Execution of a professional, transparent, and audit-proof selection process for suitable technologies in the areas of data management and data governance.
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Systematic requirements management in accordance with IREB for the elicitation of technical, organizational, and business requirements — including with regard to AI capabilities.
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Design, comparison, and evaluation of future-ready architecture scenarios that address both classic data management requirements and AI-specific use cases (e.g. training data, model training, inference, MLOps).
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Architectural assessment and further development of existing strategies in the field of tension between data lakes, data lakehouses, data warehouses, and AI platforms.
Data Architecture
- Create and maintain your data landscape map.
- Establish clear mappings between your data, your strategic target capabilities, and your business objectives.
- Derive, align, and introduce (business) data models at the conceptual and logical levels.
- Establish active data architecture management and integrate it operationally with adjacent corporate and/or IT architecture domains, such as Enterprise Data Architecture.
- Foster data architecture competencies among your employees and qualify your data experts to act as effective support for your digital development teams.
Data Strategy
- Define your data strategy. But be careful — this inflated term is neither clearly nor structurally defined. We analyzed 12 publicly available data strategies, and each of them produced a different result.
- Create alignment around your data strategy dimensions: transformative, transactional, operational, or conceptual — “a bit of everything” does not work.
- First, carefully articulate, collect, and structure the core questions of your intended data strategy.
- Create clear boundaries and transparency regarding expectations for future strategy artefacts and establish a continuous data strategy process.
- Define your strategic cornerstones, establish a sustainable connection between business and data strategy, and secure the capabilities required for operationalization.
- Use modern methods and approaches from design thinking for fast and iterative implementation. In this context, we work with selected partners (e.g., Datentreiber).
Data- & AI Platforms
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Analysis of existing application architectures and development of target scenarios for your future data and AI platform — in the cloud, hybrid, or on-premise.
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Execution of a professional, transparent, and audit-proof selection process for suitable technologies in the areas of data management and data governance.
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Systematic requirements management in accordance with IREB for the elicitation of technical, organizational, and business requirements — including with regard to AI capabilities.
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Design, comparison, and evaluation of future-ready architecture scenarios that address both classic data management requirements and AI-specific use cases (e.g. training data, model training, inference, MLOps).
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Architectural assessment and further development of existing strategies in the field of tension between data lakes, data lakehouses, data warehouses, and AI platforms.
People at the Center - Change Management in Data Organizations
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Develop your individual change roadmap based on the iDIGMA Data Excellence Change Compass, which provides orientation for both stakeholders and those responsible.
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Systematically align individual measures in communication, qualification, and participation, and avoid parallel structures.
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Mobilize your stakeholders (from business units, IT, data management, governance, and legal) as well as the new owners of data-related roles to establish a shared understanding and active participation.
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Bring data stewardship to life — through clear responsibilities, meaningful enablement, and lived collaboration.
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Measure the success of your transformation through structured feedback and continuously assess the development of your data organization’s productivity.
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