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Data Governance

Data governance is a comprehensive system of policies, processes, roles, and responsibilities

Meaning in detail Contact us now

Purpose and delimitation

The main objectives are to ensure data quality, the Minimize risks (compliance, GDPR) and the creation of a trustworthy data basis (”Single Source of Truth“). Governance defines the Strategy and rules (that “What and why”), while that data management is the operational implementation of these rules.

Contrary to the analogy of the “data police,” data governance creates the framework that first Self-service usage And the Data democratization secure and trustworthy. It determines who can use which data in which situations (e.g. roles Data Owner and Data Steward).

Further information

‍For a detailed definition, comprehensive benefits, core elements, and implementation steps, read our detailed Data Governance Guide.

Abstrakte Form eines Pfades

Data governance is not a one-off initiative...

... but a continuous process.

Abstrakte Form eines Pfades

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Data Governance
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Hub & Spoke Model and Data Roll Out

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Hub & Spoke Model and Data Roll Out

Program developed for organization, architecture, culture; mission & vision for data organization

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MediaPrint reinvents itself in terms of data technology

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Which services fit to
Data Governance
?

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Data Governance

Guaranteeing Data Quality, Clear Roles & Compliance.

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Process & Cultural Development

Establish a Data-Driven Culture and efficient Processes.

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Organization Development

Clear Roles and Structure for sustainable Data Ownership.

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Data Audit

Objective Transparency: Your Data Maturity and Risks at a glance.

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Data Strategy

Your Roadmap for Data-Driven Growth and Business Goals.

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Interim Mandates

Bridge Capacity Gaps with expert Interim Data Managers.

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Tool Comparison & Tool Selection

Neutral Tool Comparison: Avoid Misinvestment & maximize ROI.

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Business Intelligence & Reporting

Consistent KPIs & Acceptance: Dashboards for better Decisions.

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Data Activation

Data in Action: Activate Customers through Personalization.

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Data Engineering

Data Pipelines: Transforming Raw Data into Business Value.

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Infrastructure Setup

Modern Data Architecture: The Foundation for Scalable Solutions.

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