Citation
L'auteur
Charles Ngando Black
(cngando@msn.com) - (Pas d'affiliation)
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Aperçu
This article examines the challenges faced by Chief Data Officers (CDOs) in aligning data governance initiatives with their organization’s strategic objectives. It highlights how the current model, primarily focused on technical aspects, limits the strategic impact of these initiatives. This misalignment widens the gap between implicit executive expectations and actual outcomes. Drawing on academic perspectives and practical insights, the article proposes an integrated governance model that directly links tactical and operational actions to strategic priorities. This model provides practitioners with tangible benefits in implementation, impact, and initiative management, while also contributing to research on strategic alignment in data governance.
Contenu
Introduction
In implementation, strategic data governance initiatives must be translated into tactical and operational levels. However, multiple studies reveal that these initiatives are often not connected to the organization’s strategy. As a result, they appear as mere technical processes, closely resembling autonomous operational data governance initiatives.
This article highlights the limitations of this technical approach, disconnected from overarching objectives, which is a significant source of challenges for CDOs and a major reason for the failure of many data governance initiatives. It then proposes an aligned approach that addresses these limitations by integrating strategic, tactical, and operational levels. Through two real-world cases, it demonstrates how this alignment can unlock the full value of data.
Lack of Strategic Alignment in Data Governance Initiatives
According to MIT Sloan (2022) and Davenport (2023), the average tenure of a Chief Data Officer (CDO) is only two and a half years. This instability stems from high yet unclear expectations, which pressure CDOs to deliver strategic results without a clear framework aligned with organizational priorities.
Gartner (2023) emphasizes that many CDOs struggle to demonstrate the value of their work due to a disconnect between their initiatives and the organization’s objectives. This disconnect undermines their legitimacy in the eyes of executives, who perceive their contributions as less relevant to overarching priorities.
Additionally, Search Analytics (2023) points out that the absence of strategic dashboards hampers CDOs’ ability to align their actions with business goals and to demonstrate their impact. The lack of strategic indicators weakens their position, as they lack the tools necessary to showcase the effectiveness and relevance of their initiatives.
Finally, Gartner (2023) notes that many data governance initiatives fail because they focus on technical compliance rather than strategic alignment. This lack of relevance to broader objectives fragments governance efforts and complicates the justification of data-related investments.
These observations, though from diverse sources, reveal a logical sequence: high yet vague expectations for CDOs create pressure for visible results without a clear strategic framework. In the absence of well-defined goals, CDOs often focus on technical aspects that are misaligned with organizational priorities. This gap prevents the creation of strategic dashboards and makes it impossible to measure the impact of initiatives in relation to organizational priorities. It limits the perceived value by leadership, weakens the position of CDOs, and contributes to the failure of most data governance initiatives.
Limitations of the Current Data Governance Model
The challenges faced by CDOs are closely tied to a lack of strategic alignment. They stem from a data governance model focused on technical and operational aspects, with no direct connection to the organization’s strategic objectives.
This widely adopted model is supported by tools available in the market. These tools are primarily designed to automate governance processes such as data documentation, classification, organization, quality, security and access, protection, compliance, and auditing.
While these tools facilitate daily governance tasks, their approach often remains limited to compliance and technical control. They primarily address practical questions: “what” to describe data, “who” to define roles and responsibilities, and “how” to establish policies and processes. However, they fail to address the “why” — the link between data governance and the organization’s strategic objectives. This lack of strategic vision hinders the integration of data governance into long-term value creation, isolating it from the organization’s broader priorities and ambitions.
Recent advancements supporting data product governance or enabling data monetization do not resolve this disconnection. They continue to adopt a technical and operational approach, without embedding governance within a broader strategic perspective.
The diagram below illustrates this limitation: it highlights operational data governance processes without establishing a direct connection to the organization’s overarching strategic priorities. While these activities are necessary, they often remain isolated from broader goals, such as enhancing customer experience, improving competitiveness, or driving revenue growth. Without this connection, governance processes operate in silos, focusing on technical compliance rather than contributing to value creation aligned with the organization’s strategy.
In this context, reporting and evaluating actions remain limited. The data collected and indicators tracked are primarily focused on compliance and technical quality, with no direct connection to the organization’s strategic objectives. For instance, reports may show the percentage of data meeting quality standards but fail to demonstrate how this quality supports business goals, such as customer satisfaction or commercial performance. This misalignment prevents leaders from fully understanding the impact of data governance on achieving broader organizational objectives.
To address this gap, it is essential to integrate the strategic, tactical, and operational levels of data governance. Truly strategic governance would enable the deployment of concrete actions at the operational level while providing upward visibility through indicators aligned with organizational priorities. By developing monitoring and evaluation mechanisms that measure the impact of governance actions on strategic objectives, data governance could become a vital lever for supporting the organization’s vision and demonstrating its value in a tangible way.
Opportunity for an Integrated Approach to Strategic Data Governance
The limitations of the current data governance model, often centered on technical processes disconnected from strategic objectives, highlight the need for an integrated approach. This approach aims to link the organization’s strategy with its operations, transforming data governance into a central lever for achieving business priorities. By aligning governance actions with overarching objectives, integrated governance becomes a tool for strategic management.
The diagram below represents a governance model integrated with the corporate strategy:
The left section (on a gray background) illustrates the strategic dimension of data governance, which is closely aligned with the organization’s overarching objectives. In this integrated vision, strategic governance goes beyond setting general directions: it establishes a framework where every strategic need—such as data protection or quality—is identified and translated into appropriate governance policies. These policies ensure that priorities like customer satisfaction, competitiveness, and operational efficiency are addressed in a measurable and aligned manner, ensuring that data governance meets the core needs of the organization.
This diagram further illustrates how strategic governance is concretely implemented at the operational level. Once governance policies are defined, they are translated into tailored procedures to ensure their daily application. For example, if the organization has a strategic need for data protection, this requirement materializes as a protection policy, which is further broken down into specific procedures, such as access control, sensitive data monitoring, and incident management. This integrated model ensures that each operational process, while meeting technical requirements, also contributes to strategic objectives.
With this integrated governance approach, the organization can establish strategic management. Performance indicators go beyond mere technical compliance and include measures of the impact of governance actions on the organization’s overarching priorities. A coherent reporting system, aligned with the strategy, enables leaders to monitor progress toward objectives and adjust actions based on results. By linking daily operations to strategy, this integrated governance transforms data into a critical strategic asset, supporting the organization’s vision and maximizing tangible value creation.
Case Studies: Benefits of Integrated Data Governance
In the e-commerce sector, customer satisfaction and retention are key strategic objectives, with personalized shopping experiences serving as a crucial lever to stand out in a highly competitive market.
An operational approach to data governance implements security policies and data quality standards to protect customer information and ensure compliance. However, this approach remains limited to technical objectives and does not directly support the personalization of the customer experience.
Conversely, an integrated approach to data governance aligns these practices with personalization priorities by leveraging customer data to identify preferences and tailor recommendations. To measure this strategic impact, the company can track the adoption of targeted governance standards in personalization projects and the deployment of governance capabilities, such as policies for protecting and utilizing critical data for customer satisfaction. These indicators demonstrate how data governance directly supports retention and engagement objectives, contributing to strategic growth.
In the financial sector, regulatory compliance and risk management are strategic priorities that demand rigorous data governance to protect the organization and its clients.
In an operational approach, data governance often focuses on technical compliance, ensuring sensitive data meets protection and quality requirements. However, this approach does not always track how governance practices impact overarching objectives.
By adopting an integrated approach, the organization can directly link these practices to strategic goals, such as establishing transaction traceability standards and policies for monitoring access to sensitive data to prevent fraud. To evaluate this strategic contribution, the company can monitor the number of critical projects using these traceability and security standards, as well as progress in implementing key governance roles to ensure compliance. These indicators demonstrate how data governance acts as a lever for security and resilience, two major organizational priorities.
Conclusion
This article highlights the challenges faced by CDOs and the limitations of data governance initiatives that are disconnected from the organization’s strategic objectives. Often perceived as mere technical processes, these initiatives struggle to demonstrate their value, weakening the position of CDOs and limiting the impact of data governance. Vague expectations of CDOs exacerbate this gap, making it difficult for them to prove the relevance of their work to the organization’s priorities.
Integrating data governance into strategy overcomes these obstacles. By aligning governance practices with overarching priorities through tailored policies, standards, and processes, organizations can transform their data into a lever for achieving competitiveness, customer satisfaction, and compliance. This approach equips CDOs with the tools to make their initiatives visible and demonstrate their direct contribution to strategy.
By linking governance actions to organizational objectives, CDOs gain a solid framework to showcase how their initiatives support growth and security. This integrated approach establishes data governance as a critical pillar for achieving the organization’s ambitions and positions CDOs as key players in driving success.
Bibliographie
Davenport, T. (2023). Why Do Chief Data Officers Have Such Short Tenures? Retrieved from https://www.tomdavenport.com
Gartner. (2023). Understand Data Governance Trends & Strategies. Retrieved from https://www.gartner.com
GDS Group. (2023). Maligned and Misunderstood: Why Chief Data Officers Don’t Last Long in the Job. Retrieved from https://gdsgroup.com
MIT Sloan. (2022). Chief data officers don’t stay in their roles long. Here’s why. Retrieved from https://mitsloan.mit.edu
Ngando Black, C. (2024). Le cadre de gestion des données, un impératif pour les organisations. Management & Data Science. Disponible sur https://management-datascience.org/articles/4736/
Ngando Black, C. (2024). Modèle et initiative de gouvernance des données : quels assortiments ? Management & Data Science. Disponible sur https://management-datascience.org/articles/27916/
Ngando Black, C. (2024). Gouvernance et stratégie des données : Un tandem pour créer de la valeur. Management & Data Science. Disponible sur https://management-datascience.org/articles/37427/
Search Analytics. (2023). Why Do Chief Data Officers Have Such Short Tenures? Retrieved from https://searchanalytics.com
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