Introduction

Data has become a core asset for organizations, powering innovation, driving strategic decisions, and ensuring operational efficiency. However, fully realizing this potential requires robust data management practices, supported by an appropriate enterprise data management framework (DMF). The selection of such a framework is often guided by traditional methods, focusing on technical features and budgetary constraints. While these approaches provide practical short-term solutions, they often fail to address the long-term ambitions of organizations or adapt to the evolving complexity of their data needs.

This article proposes a strategic approach to selecting a DMF, one that prioritizes business needs and aligns them with organizational goals. By adopting a methodology that evaluates DMFs not just for their technical capabilities but also for their adaptability and scalability, organizations can better position themselves to manage data effectively. The strategic approach ensures that data management solutions contribute to operational efficiency while supporting broader strategic objectives.

1 – Technical and Operational Approach

1.1 – Principles

The traditional approach to selecting a data management framework (DMF) relies on two main categories of criteria:

Technical criteria reflect a framework’s ability to provide recommendations, standards, and methodologies tailored to organizational needs.

Economic criteria help assess the resources required to implement, maintain, and improve the framework once adopted by the organization.

Technical and economic criteria work together to evaluate the suitability of a DMF for a specific organizational context.

This approach has been extensively developed in the literature. Gartner (2022) highlights its effectiveness for organizations seeking to minimize operational risks. It enables rapid and pragmatic implementation by ensuring alignment between organizational constraints and the framework’s capabilities. Khatri and Brown (2010) emphasize its usefulness in meeting immediate needs, particularly in environments where priorities are primarily operational.

1.2 – Implementation

The implementation of this approach follows a logical process that begins with an analysis of the technical requirements of DMFs. This analysis identifies their complexity, evaluates the effort required for their implementation, and consequently determines the necessary resources. The association of DMFs with the most suitable business profiles naturally stems from this process.

Technical Requirements (DMF) → Complexity/Effort → Necessary Resources → Business Profile

DAMA-DMBOK, historically the first and most well-known framework, stands out for its comprehensive conceptual approach. However, it neither provides technical standards nor concrete operational mechanisms, significantly increasing the complexity of its implementation. Applying its principles requires in-depth interpretation and specific adaptation by each organization, necessitating substantial effort to develop tools and internal practices aligned with its recommendations. Consequently, this framework is primarily suited to large organizations with significant financial resources, expert teams, and a high investment capacity.

Conversely, CMMI-DMM adopts a modular and progressive approach, characterized by low initial technical requirements. This makes it an accessible solution for medium-sized businesses or those with limited budgets undergoing digital transformation, seeking gradual improvement in their data management practices. However, the lack of explicit guidelines for managing large data volumes or advanced governance may lead to increased complexity as needs evolve. In such cases, additional investments in tools and expertise may become necessary.

EDMC-DCAM, with its strong focus on data quality and compliance, stands out for its well-defined technical mechanisms. However, this rigor translates into high complexity and significant effort, particularly for ongoing maintenance and meeting strict regulatory requirements. This framework is therefore particularly suited to large institutions operating in heavily regulated sectors such as finance or healthcare, with substantial financial resources and specialized teams in compliance and governance.

Finally, DC-ODMF (Data Crossroads – The Orange Data Management Framework) emphasizes simplicity and modularity, reducing the complexity and effort required for its implementation. With its low technical and financial requirements, this framework is ideally suited to startups and SMEs with limited resources and simple data management needs. However, in complex environments requiring advanced interoperability or significant scalability, its limitations quickly become apparent, making its adaptation more challenging and demanding.

1.3 – Limits

This selection approach reveals fundamental weaknesses that compromise the relevance and efficiency of data management frameworks (DMFs) in organizational contexts. These limitations originate from a fragmented evaluation of frameworks, where technical criteria are often assessed in isolation, without a clear connection to business needs and strategic objectives.

This method places excessive priority on technical aspects such as interoperability, flexibility, or scalability, treating them more as intrinsic characteristics than as tools serving a broader purpose. For example, a framework might be considered effective because it offers modular recommendations or best practices for managing growing data volumes, yet these capacities might not align with the organization’s priorities or team expectations. Without this prior reflection, the framework, while technically sound, may prove inadequate in meeting real business needs or supporting a coherent strategic vision.

This misalignment leads to significant operational and strategic challenges. The methodologies defined by the framework, though ambitious, may appear disconnected from the day-to-day realities of teams, rendering their application cumbersome and, at times, ineffective. This gap risks discouraging users, limiting framework adoption, and reducing its overall impact. Additionally, the lack of clear alignment with strategic priorities hampers the organization’s ability to leverage data as a driver of innovation or competitiveness. Tools and systems implemented according to the framework’s recommendations, though technically robust, may fail to fully harness the potential of available data.

2 – Strategic Approach

The strategic approach is built on a methodical framework that places business needs at the heart of the selection process for data management frameworks (DMFs). Unlike approaches focused solely on technical or economic constraints, it adopts a holistic view by connecting business requirements, framework capabilities, and the resources available to organizations, while considering their ability to evolve in alignment with organizational goals.

2.1 – Principles

This approach is constructed using a logical sequence that begins with an analysis of business needs:

Business Needs → Business Profiles → Suitable DMFs → Adaptation Effort → Economic Profiles

Once identified, these needs are translated into business profiles that encompass essential functions such as data description and structuring, governance, or strategic oversight. Each profile is then matched with the most appropriate DMFs, chosen based on their methodological characteristics or their capacity to address the expressed needs.

A key step involves evaluating the adaptation effort required to align these frameworks with business needs. Some frameworks may demand minimal modifications and integrate quickly, while others require more complex adjustments, such as customizing their recommendations or adapting them to existing systems. This evaluation then helps identify the types of organizations capable of supporting these efforts, based on their resources and organizational structure. Start-ups and SMEs often favor flexible and easily adjustable frameworks, while large enterprises can invest in more comprehensive frameworks that require greater integration efforts.

2.2 – Implementation

The table below applies this logic and identifies seven business profiles. It provides a nuanced view of the interactions between business profiles, business needs, eligible data management frameworks, and types of organizations. It illustrates how specific business needs drive the selection of frameworks, considering their ability to effectively meet these expectations. Evaluating the adaptation effort required for each framework refines this alignment by integrating organizational constraints without diverting attention from business priorities. The inclusion of profiles such as multisite coordination or digital transformation enriches the analysis by addressing complex strategic challenges without compromising operational relevance.

2.3 – Contributions

The strategic approach to selecting a data management framework (DMF) is based on a rigorous methodology that emphasizes business needs as the essential starting point. It coherently links organizational priorities, the characteristics of frameworks, and the capacities of companies, ensuring an optimal match among these elements. This process promotes informed decision-making while aligning with both strategic and operational objectives.

Take, for example, a public institution aiming to enhance regulatory compliance. By identifying a need for structuring and managing sensitive data, it might prioritize the “Organization and Governance” profile. The EDMC-DCAM framework, recognized for its ability to structure governance policies and meet regulatory requirements, would be selected. The evaluation reveals a low adaptation effort, enabling rapid integration. This strategic approach ensures that the technical solutions chosen are not only aligned with immediate needs but also support long-term organizational ambitions.

By adopting this method, organizations maximize their ability to meet business demands while adapting to technological and organizational changes. It ensures optimal resource use by selecting modular, scalable, and strategically aligned frameworks. Whether structuring data for a start-up, ensuring compliance for a public institution, or coordinating global activities for a multinational, this strategic approach provides a robust and sustainable response to the challenges of data management.

Conclusion

The selection of a Data Management Framework (DMF) requires a perspective that transcends immediate constraints. By refocusing the process on business needs and adopting a combined approach, organizations can choose frameworks aligned with their priorities while considering technical and economic feasibility. This dual perspective ensures a sustainable and adaptive implementation, allowing organizations to maximize the strategic value of their data while navigating future challenges.