This article presents The Data Management Toolkit 2.0.
This article presents the common challenges organizations worldwide face when implementing data governance, and, most importantly, demonstrates how to address them. From the beginning of my data management career, many stakeholders I’ve worked with have consistently expressed a need: “We don’t need to know what we must do. We need to know how to do it.” That’s why this article not only addresses the challenges but also provides solutions. My new book, The Data Management Toolkit 2.0, is designed to answer that very question.
Common Challenges with Data Governance
A couple of months ago, I conducted several polls on LinkedIn and during my online masterclasses. These polls revealed three key challenges organizations experience when implementing data governance.
Challenge 1: Organizations Recognize the Need for Data Governance but Struggle with Implementation.
Figure 1 presents the results of the first poll.
Figure 1. The status of data governance frameworks in organizations.
Most organizations acknowledge the importance of data governance. Nearly 60% are actively working on establishing a framework—36% in development and 24% in implementation. However, despite this recognition, only 16% have a fully operational framework, underscoring the challenges of transitioning from planning to execution.
This gap suggests that while organizations understand the necessity of data governance, they often face barriers such as resource constraints, unclear strategies, or difficulties integrating governance into daily operations.
Challenge 2: Most Organizations Acknowledge the Need to Improve Their Governance Framework.
The second poll focused on the adequacy of current data governance frameworks. The results revealed that 48% of organizations need to refine their existing framework, while 44% need to build a new one from scratch (Figure 2).
Figure 2: Organizations’ plans regarding a data governance framework.
This indicates widespread recognition that improvements are essential, whether through enhancing current frameworks or developing entirely new models. If you’re reading this article, it’s likely that your organization is facing similar challenges.
Challenge 3: Organizations Face Multiple Barriers During Implementation
The third challenge relates specifically to implementation. For several consecutive years, I have conducted the same poll on LinkedIn. Figure 3 presents the latest results.
Figure 3: Key challenges in implementing a data governance framework.
This poll examines the most significant barriers organizations encounter when implementing governance frameworks. One of the most notable trends is the increasing difficulty in securing C-suite and business support—projected to become the most significant barrier by 2025. While the importance of governance is well understood, leadership buy-in remains a crucial missing piece for successful implementation.
Governance struggles are rarely isolated; they are often interconnected. Many organizations face multiple challenges at once. The high number of respondents selecting “All of the above” in the 2024 poll reflects the fact that organizations are dealing with governance holistically rather than confronting isolated issues.
The Data Management Toolkit 2.0 serves as a guide to assist in addressing the challenges mentioned above.
Difference and Similarities Between Two Book Editions
The first edition of this book was published in 2019. You should ask: What are the differences? The answer is straightforward: the titles and structure are similar, but the contents are different (Figure 4).
Figure 4: Two editions of the Data Management Toolkit.
Why Is It Different?
When I wrote the first edition of The Data Management Toolkit, I had several intentions in mind:
- To create a “business card” to introduce myself to the data management community
- To share my experiences with newcomers who were once in the same position I was at the start of my journey
- To present an integrated approach for implementing a data management framework in small- and medium-sized organizations
These intentions led to some unexpected and rewarding outcomes:
- Despite not being promoted for commercial purposes, the book became the best seller among my five publications
- Data management consultants—among the first readers—began using it in their own consulting practices
- Large multinational companies reached out for coaching, training, and consulting based on the book’s content
Since its original release in 2019, much has changed:
- New versions of industry standards:
For instance, the Data Management Capability Assessment Model (DCAM) evolved into a Framework. The Open Group also released the 10th version of the TOGAF® Standard (The Open Group Architecture Framework). - Broader consulting experience:
In recent years, I’ve gained more expertise in emerging areas, including data mesh architecture, metadata management, and knowledge graphs. My client base has also shifted—from primarily small businesses to mid-sized and large organizations. - Methodology enhancements:
The first edition introduced the 5-step “Data Management Star” method. That method has since evolved into a core component of the R.A.N.G.E. Data Management Framework.
The second edition goes beyond methodology. It not only explains how to implement core data management capabilities but also shows how implementation differs across various data architecture scenarios (see Figure 5).
Figure 5: Data architecture types considered in the scope of this book.
The core goals of The Data Management Toolkit 2.0 are:
- Provide a practical method to scope and implement a data management framework tailored to an organization’s needs and resources
Many factors influence the scope of data management. Each organization must either build a new framework or adapt an existing one to fit its specific context. While core data management capabilities are often consistent across industries, implementation approaches will vary depending on organizational structure, maturity, and available resources.
- Support readers in applying the framework through practical implementation
The book is designed as a step-by-step guide. Following the recommended sequence of steps leads to the structured implementation of a data management framework. The book also includes a variety of templates that can be tailored to meet the unique needs of different organizations.
- Develop hands-on, applicable skills within organization.
This book serves as a resource for training internal teams and engaging staff in the data management initiative. By using the provided templates and examples, readers can apply the methods directly in their day-to-day work and develop practical capabilities.
How the Book Assists in Dealing with the Challenge.
To address the challenges discussed above, The Data Management Toolkit 2.0 is built around the O.R.A.N.G.E. Data Management Framework (DMF), illustrated in Figure 6.
Figure 6: The O.R.A.N.G.E. Data Management Framework (DMF).
This framework offers a comprehensive set of ready-to-use models, methods, and templates designed to support the entire lifecycle of data governance—from scoping and designing a framework to implementing, measuring its maturity and performance, and scaling it. The six phases of the O.R.A.N.G.E. DMF correspond to these goals and can be adapted to various data initiatives. While each phase follows a logical dependency, they can also be executed in parallel, forming a flexible high-level roadmap.
EVOLVE: Ensuring Scalability and Adaptability
The Evolve phase provides guidance on how to design a governance framework that can grow with the business. Scalability should be a built-in feature from the beginning, not an afterthought. This phase prepares organizations to adapt the framework to future business needs and emerging challenges.
ORGANIZE: Setting a Realistic and Strategic Foundation
The Organize phase focuses on evaluating organizational needs and available resources. In practice, I often see organizations launching multiple data initiatives without assessing their capacity. This leads to failed projects due to resource constraints and shifting priorities. Another common pitfall is investing in governance tools without first identifying business requirements. Additionally, some organizations rush into trendy concepts like data mesh or AI without a foundational governance framework in place.
To help organizations avoid these traps, the ORANGE DMF introduces the Strategic S.C.O.P.E. Formula—a tool that guides organizations in defining a feasible and sustainable scope for their governance efforts, tailored to real-world conditions.
RENDER: Designing the Governance Framework
Once the scope is set, the Render phase focuses on designing the governance framework. Most data-related initiatives require a similar set of core capabilities: governance, business, data, application and technology architecture, data quality, security, analytics, and, crucially, metadata management. The challenge is that in many organizations, these capabilities often exist in isolated or informal forms, resulting in operational inefficiencies.
To address this, the ORANGE DMF offers the Customized Capability D.I.A.G.R.A.M., which supports the structured design of a formal, coherent governance framework. Key deliverables include the operating model and organizational structure, covering governance bodies, self-organized teams, and defined roles. For each capability, the framework also defines necessary policies, processes, roles, artifacts, and IT tool requirements.
ACTIVATE: Implementing the Framework Effectively
The Activate phase addresses the challenges of implementation. One of the core issues is that data management capabilities are interdependent – outputs from one often serve as inputs for another. As a result, implementation must follow an integrated approach.
Another challenge lies in deciding whether to separate or combine design and implementation phases. While integration can improve efficiency, there’s no one-size-fits-all solution. Each organization must evaluate its unique context.
To help navigate these decisions, the ORANGE DMF provides Integrated Implementation Road M.A.P.S., guiding organizations through various implementation strategies based on logical dependencies and resource availability.
NAVIGATE and GROW: Measuring Performance and Maturity
The Navigate and Grow phases focus on measuring performance and maturity—two critical aspects of managing and evolving the governance framework. Measuring performance and maturity presents several challenges. It must be done at multiple levels of the organization and must distinguish between implementation performance and operational performance, each requiring its own KPIs.
The ORANGE DMF addresses these issues through two structured approaches:
- The G.A.I.N. Performance Management Method, which supports performance tracking at the implementation and execution stages.
- The G.R.O.W. Maturity Assessment Approach, which helps assess the governance framework’s maturity over time and guides continuous improvement.
If you’re interested in the book, you can download the first few chapters for free to review HERE. I hope it provides you with a clear understanding of how The Data Management Toolkit 2.0 can support your data governance journey. I look forward to your feedback and hope the toolkit becomes a valuable resource for your organization.
