Data governance

Structure, manage and secure data as a strategic asset

Data governance: regain control over your critical data

Data is now central to operational performance, regulatory compliance and innovation. It is key and is a prerequisite for all AI projects.

However, without a clear framework, it remains fragmented, unreliable and difficult to use.

At Netsystem, we support companies in implementing pragmatic, usage- and results-oriented data governance that is aligned with their business, IT and regulatory challenges.

Our goal: to make data a measurable value lever, rather than an organisational constraint.

A widely shared observation

In many organisations, we see:

  • Unclear responsibilities for data,
  • Heterogeneous quality and usage rules,
  • ERP, data or AI projects weakened by unreliable data,
  • Increased exposure to regulatory risks (GDPR, AI Act),
  • Difficulties in effectively managing the business.

Without structured governance, data becomes a risk factor rather than a competitive advantage.

Why implement data governance?

Effective data governance enables you to:

  • Clarify roles and responsibilities (business, IT, data),
  • Sustainably improve data quality and reliability,
  • Secure usage and strengthen compliance,
  • Make management and decision-making more reliable,
  • Create a solid foundation for MDM, ERP, BI, AI and IT M&A projects.

It is a strategic prerequisite for any data-driven organisation.

Our approach at Netsystem

We view data governance as a concrete, progressive process tailored to your level of maturity.

Our approach is:

  • Pragmatic: focused on real business uses,
  • Progressive: deployed in controlled stages,
  • Cross-functional: integrating IT, data, cybersecurity and compliance,
  • Results-oriented: with quickly measurable gains.

We prioritise balance between organisation, processes and tools, without unnecessary complexity.

Information/quote for a data governance project

Our Data Governance offering

A business-first approach, gradual deployment by data domain, and controlled integration into your existing IT system.

Diagnostic data & governance

  • Mapping critical data and flows,
  • Analysing business practices and pain points,
  • Assessing data and organisational maturity,
  • Identifying operational and regulatory risks.
01

Definition of the governance framework

  • Governance model (Data Owner, Data Steward, committees),
  • Data quality, security and usage rules,
  • Management and lifecycle policies,
  • GDPR, cybersecurity and AI Act alignment.
02

Construction of the data roadmap

  • Prioritisation of projects (MDM, quality, BI, AI, etc.),
  • Quality, security and data usage rules,
  • Coordination with existing IT projects,
  • Definition of management indicators,
  • Realistic and operational target vision.
03

Long-term support

  • Operational support for teams,
  • Awareness raising and change management,
  • Continuous adjustment of the system.
04

Data governance and MDM

A close and complementary relationship

Data governance defines the overall framework.
MDM (Master Data Management) is one of its key operational components, dedicated to ensuring the reliability of reference data.

In many cases, MDM is identified as a priority project in the roadmap.

Cases where data governance is a prerequisite

Project
data or AI

Redesign
ERP / CRM

Regulatory
requirements

Post-acquisition
harmonisation

Improvement
reporting

Why choose Netsystem?

Netsystem supports organisations in implementing pragmatic data governance that is tailored to their maturity and results-oriented.
Our approach combines data expertise, understanding of information systems, cybersecurity and compliance to ensure truly operational governance.

Independent of software publishers, we favour progressive and concrete approaches that secure projects, ensure reliable decisions and quickly create value around critical data.

"Data governance has become a key issue in securing transformation projects and ensuring reliable decision-making. At Netsystem, we favour a pragmatic approach based on clear rules, well-defined responsibilities and realistic priorities. The initial benefits are quickly apparent, particularly in terms of data quality and risk management."

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