Did you know that, by 2023, nearly 70% of the data available to businesses was simply not being used?
Collected. Stored. Forgotten...
Today, data is ubiquitous, but only clear governance can transform it into a genuine strategic lever.
In this article, we give you the keys to effective data governance, so that your data no longer lies dormant in your systems, but becomes a real asset for your decisions, your teams and your performance.
CONTENTS
2. Why is data governance strategic for your company?
3. The 4 pillars of an effective data governance strategy
4. Mistakes to avoid in a data governance strategy
5. Concrete examples of data governance implementation in companies
Data governance covers all the rules, processes and responsibilities needed to control the quality, reliability and use of data within a company.
It aims to structure data management so that it is used in a coherent, secure and compliant way, particularly in complex environments (ERP, CRM, AI...).
In a webinar hosted by our Expert Data Governance and Digital Transformation consultants and members of the FocusTribes community, we discussed the subject of data governance in depth.
Webinar hosted by :
Dounia BOUKHEDA - Expert consultant in data governance & data projects
Tarik RIDA - Data management and enterprise architecture consultant
Elio Della Bruna - Master Data Governance and Data migration SAP Specialist
Data governance is at the heart of :
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And because we always want to bring you value-added content, we've prepared a FREE ebook that gives you all the keys to setting up an effective data governance strategy. Download it now!
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A common language is essential. This means defining shared nomenclatures, naming rules, formats and indicators between business and IT teams. This is the basis for aligning all players with the same interpretation of data.
Governance is based on clear, controlled processes: creation, modification, deletion, as well as management of requests, validations and escalations. These workflows must be documented and integrated into daily practices.
An effective strategy requires a clear division of responsibilities:
Data owners (on the business side) define the rules.
Data stewards (often IT or functional) apply and monitor them.
High-level sponsorship (CIO, business management, CDO...) is essential to ensure buy-in and cross-functionality.
Tools don't make governance, but they do support it. They are solutions that make it possible to :
map data (data catalog),
monitor data quality,
document processes,
traceability and auditability.
Tools such as Collibra, Informatica, Microsoft Purview or Ab Initio were cited as references, depending on the use case.
A common mistake is to think that data governance is the sole responsibility of IT. In reality, it needs to be cross-functional: business units have a central role to play in defining rules and requirements, and in ensuring that processes are appropriated.
Implementing a technical solution (such as Collibra, Informatica, etc.) without first defining processes, roles and objectives is ineffective. Tools should support an already structured organization, not the other way around.
Without shared governance, different functions (finance, purchasing, IT, supply, etc.) tend to develop their own repositories, generating inconsistencies, duplication and conflicts of use.
The success of a data governance approach also depends on the human factor. Failing to train employees or make them aware of data-related issues will slow down the adoption and sustainability of the project.
The absence ofmonitoring indicators (KPI data) makes it impossible to assess the real impact of actions taken: data quality, compliance rate, process adoption rate, etc. Effective governance is a long-term process.
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In addition to providing methodological input, our consultants shared two concrete case studies, illustrating the challenges and benefits of a well-structured data governance strategy.
Dounia Boukheda reported on a project carried out within the Société Générale Group, the aim of which was to comply with BCBS 239 regulations. This regulatory framework requires systemic banking institutions to guarantee the reliability, integrity and traceability of their risk data.
To meet these requirements, a data governance system has been set up around :
shared management rules,
a cross-functional governance framework involving the business lines,
and rigorous process documentation.
This project has professionalized the management of critical data and enhanced the overall quality of risk data.
Elio Della Bruna presented an assignment carried out at a German pharmaceutical company, faced with synchronization problems between its Data Factory and its MDG (Master Data Governance) system.
Data flow was difficult, creating inconsistencies between IT and business repositories.
To resolve this, the approach was to :
clarify responsibilities via a structured RACI,
formalizing datacollection and validation processes,
implement quality control rules right from the data entry stage.
Would you like to find out more about our experience feedback and discover the best practices to adopt for a successful data process?
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