We guide companies from developing a data strategy to operational deployment. Data applications are industrialized and made scalable. Our core tool is our industry-specific data landscapes, which provide a structured view of processes, responsibilities, and data sources.
Our 4Q approach assesses digital maturity across four quadrants: value creation, technology management, governance, and organization. Based on this, we derive a tailored data landscape that serves as a business-oriented compass, highlighting potential opportunities.
Using the data map, we define application scenarios with technologies such as advanced analytics, AI, machine learning or classic data integration. In this way, we combine technological feasibility with strategic benefits and create a scalable portfolio of data products.
Together with our customers, we go through the Design, Develop and Deploy phases. In design, we develop solution architectures and governance models, in develop, we implement agile, and in deploy, we sustainably integrate the products into the organization. This creates a continuous value-added process that enables data-driven innovations.
A Chief Data and Analytics Officer (CDAO) takes a central leadership role in driving data-driven transformation. They develop the data strategy, manage data and analytics products, establish governance structures, and enable capabilities across all business units.
We provide experienced CDAO expertise flexibly as an external service, tailored and immediately deployable. Using proven methods, role models, and templates, we strengthen internal capabilities, promote data and analytics literacy, and embed data-driven decision-making sustainably within the organization.
This approach lays the foundation for efficient, scalable data processes, clear responsibilities, and technologically well-integrated solutions that enable sustainable value creation.
Our experienced engineers implement projects efficiently. Modern data pipelines, automated quality checks, AI-driven analytics, and close integration with governance ensure that data products are business-relevant.
Data Platform Engineering: We build and operate modular, hybrid data platforms (on-premise & cloud) with Infrastructure-as-Code, self-service components, and role-based access.
Data Product Engineering: We develop interoperable, reusable data and analytics products, leverage data contracts and APIs, and optimize Data Laklehouse and Warehouse architecture.
Data Quality and Observability: We implement automated quality checks, monitor data flows in real time, detect anomalies early, and ensure end-to-end discoverability through data and analytics governance.
We deliver stable, high-quality data and analytics products that meet governance requirements, respond agilely to new challenges, and generate long-term business value.
Our data and analytics governance solutions are practical, modular, and scalable. Intelligent, automated agents create a living framework that ensures trust, transparency, and control in the data and analytics environment.
The governance framework is based on seven principles – from security and data quality to discoverability and strategic use – and provides ready-to-use templates for specific requirements such as AI strategy and governance, automation and robotic governance, or multi-cloud governance.
Our governance solutions integrate seamlessly with data warehouses, data lakes, and SaaS tools such as Databricks, Snowflake, or Microsoft Fabric. They provide real-time control for risk, compliance, and audits. Roles like product owners, data stewards, and compliance officers work closely together to ensure sustainable and scalable governance.
Standardised types for data products along source, aggregation and consumption.
Templates and methods for planning, handover, role clarification and performance monitoring.
Transfer of MVPs to stable operating mode with documentation, monitoring and scaling.
Metrics, logs, and dashboards to monitor and troubleshoot the platform.
Structured end-to-end process from idea to industrialization of a data product.
Mandatory approval points for quality assurance and implementation management.
Systematic recording, evaluation, and prioritization of data requirements.
Timely and professionally structured overview of planned product developments.
Automated, declarative security rules (such as Kyverno) for compliance and protection.
Structured handling of changes to existing data products.
Role-based access control at platform and data product levels.
Automated setups and templates (e.g. copier, helmet) for rapid deployment of components.
Define and deploy infrastructure via code (e.g. Terraform, Helm) for automation and reusability.
Central services such as logging, monitoring, security, which are used by all data products.
Mandatory requirements for naming, structure, versioning and governance compliance.
Technical discipline for building, operating and developing the data platform as a platform-as-a-service.
Reusable standards for connecting data sources — including batch, streaming and hybrid variants.
Technical and organizational process for creating, maintaining and decommissioning data products.
Structured templates for modeling data products, data flows, and system analyses.
First implementation of a use case that follows the principles of data mesh and product architecture.
End-to-end responsibility for developing and maintaining scalable, quality-assured data products.
Semantically described metadata and machine-readable contracts for discoverability, observability, and governance.
Standards for naming, tagging, PII anonymization, code structure, and team collaboration.
Definition and modular template for a data & analytics product, consisting of components such as Inport, Outport, Runtime, Storage, Metadata, Orchestration, and more
Automated testing, validation, and anomaly detection to ensure consistent data quality.
A suite of data & analytics products that are connected to provide specific functionality and create business value.
Distributed governance responsibility across platform, domains, and key roles.
Automated policy compliance using metadata, SLOs, data lineage, and logs.
Agent process for real-time monitoring of governance principles in data products.
Functionally defined organizational unit with full responsibility for associated data products.
Use of data in accordance with Purpose Alignment & Limitation, with clear purpose
Agreement between Data & Analytics product owner and consumer on data consumption, e.g. SLAs, pricing, frequency.
Focus on data quality (DQA) across the entire life cycle.
Monitorability through monitoring, lineage, anomaly detection, and dashboards.
Cross-system usability through API standards, semantics and decoupling.
Discoverability and documentation through metadata, catalogs and standardized descriptions.
Committee for the evaluation, approval and further development of architectural decisions.
The seven principles (Secure, Trustworthy, Discoverable, Observable, Transparent, Interoperable, Purpose-Driven) as a normative basis.
Traceability and documentation of processes, flows and responsibilities.
Includes platform and data security, including access control, encryption, and data sovereignty.
Documented architectural decision including context, alternatives, evaluation and decision.
Architectural component for anchoring governance principles via contracts, sidecars and regulations.
Exchange platform for the development of methods, standards and know-how.
Understanding of technology, platform logic, and infrastructure processes — necessary for business roles to work effectively with data-based solutions.
Technical design, implementation and operation of the data & analytics platform — including infrastructure-as-code, observability, security and automation.
Defines and implements security mechanisms to meet security & compliance requirements in the platform and products.
Ensures the availability, performance and scalability of the platform and the operated data & analytics products during the operating phase.
Generalized architecture template to ensure consistency and reusability.
Responsible for the development, maintenance and development of data & analytics products — including data pipelines, data models, APIs, business logic and ML components.
A stateful data microservice with defined inports/outports, which processes and makes data available. Must be discoverable, credible, secure, and interoperable
Technical architecture of the data platform including infrastructure, security components, shared services and blueprints.
Technical and organizational characteristics of a data product or family, including blueprints and shared services.
Creates dashboards, reports, and self-service analytics solutions for business use of data & analytics products.
An architectural paradigm in which data responsibility is organized in domains in a decentralized manner.
Responsible for the data-driven business capacity of a domain. Acts as a bridge between business requirements and the domain's data & analytics product strategy.
Responsible for the bridge between infrastructure/platform and data & analytics products. Ensures clean decoupling and coordinates requirements with the capabilities of platform technologies.
The person or team that consumes Data & Analytics products to make decisions or develop new products.
Responsible for the implementation, maintenance and development of a data & analytics product. Translates requirements into specific backlogs and coordinates the engineering team.
Provision of technical platforms with standardized services (e.g. infrastructure, data services) by IT. Includes operations (SRE) and service management.
Responsibility for the lifecycle, quality, and business value of data & analytics products. Combines product thinking with data strategy and governance.
Leads the Practice of Governance, defines strategies, standards and processes for sustainable and automated governance.
Ability to understand, interpret and use data responsibly — at all levels of an organization.
Technically responsible for developing and operating sidecars and automated governance rules at runtime.
Operational responsibility for planning, managing and complying with roadmaps and resources of one or more data & analytics products.
The central leadership role for data-driven transformation in the organization. Responsible for data strategy, data products, data governance, and enablement — across all domains.
An organizational model that establishes data-driven management structures, roles and processes in companies — without the initial need for a fully staffed Chief Data Officer. The CDAoaaS provides methods, role models and templates.
Technical disciplines (e.g. architecture, governance, platform) that act as a bridge between business and IT. Practices are centers of excellence for data literacy, method development, and role training.
Understanding business processes, value streams and operational requirements — a prerequisite for data-based solutions with real impact.
Industrialization of MVP use cases and implementation of further use cases on the roadmap, industrialization of data & analytics platform, productization of governance analytics and operating model (TOM).
1. Computational data governance, 2. High Profile Engineering, 3. Data management using methods, 4. CDAO as a Service, 5. Value creation with data & analytics products
Industrialized operating model, demand management, enablement, and scaling.
MVP use case implementation, platform construction, governance analytics and operating model prototype.
Strategic positioning, use case prioritization, vision & stakeholder alignment.
Data strategy, MVP definition for selected use cases, data & analytics architecture blueprints, governance & operating model (TOM) for the organization.
Structured recording of key data objects and flows for an industry. Used to derive data and analytics products and to guide strategic data architecture and use case development.
Discover, Design, Develop, Deploy — methodological framework for all projects.
A model based on Simon Sinek to clarify vision, strategy and implementation: Why (purpose), how (procedure), what (services). The 'why' is at the center of action. Basis for positioning the Navique portfolio.
The role Navique sees itself in: Navigator for data-based strategies in an age of artificial intelligence. With consulting and a structured service portfolio, we help companies effectively use data and create value.
Four quadrants that must be in balance to build a data-driven organization: value creation, technology management, frameworks (governance, compliance, architecture), organization and enablement.
Modular offering model for data-driven transformation, structured along the Discover, Design, Develop and Deploy phases.
The active attitude of understanding data as a strategic asset and anchoring data-based decisions at all levels of the organization.
The goal of an organization to build up data, analytics and technology expertise internally in order to create value independently and confidently with data and analytics — without permanent external dependency.
