TL;DR
Most businesses collect data but few manage it well. Without a clear data strategy, data efforts become fragmented, expensive, and disconnected from real business goals. This article, based on Marilu Lopez's book Data Strategies for Data Governance, explains what a proper data strategy looks like, how to assess where your organisation stands today, and how to use the PAC Method; a pragmatic, layered approach to building one step by step.
Who Should Read This?
Anyone responsible for data in their organisation, whether that is a data analyst, a data manager, a business intelligence lead, or an operations manager in a growing company.
About the Book
Data Strategies for Data Governance by Marilu Lopez introduces the Data Strategy PAC Method: a structured approach to building data strategies that are Pragmatic, Agile, and Communicable. The book is divided into two parts. The first part covers the theory and context, including what data strategy is, why it matters, and how to structure it. The second part is a step-by-step guide for putting the method into practice. This article draws from the first part of the book, with a focus on making the ideas accessible for small and medium-sized enterprises.
Why Your Data Efforts Are Not Working
Digital transformation is not just about adopting new technology. It involves changing business models, internal processes, and organisational culture. At the foundation of all of this is data management; the discipline that makes data reliable enough for analysts, scientists, and decision-makers to actually use.
The problem is that poorly managed data is costly, and the risks are largely hidden. One well-known consequence is that data professionals spend up to 80% of their time collecting, cleaning, and preparing data, leaving very little time for actual analysis and insight generation. This is not a data science problem; it is a data management problem. Data management professionals should be trained to handle this, rather than expecting analysts and scientists to fill the gap.
Many organisations try to solve this by investing in new platforms and tools. But technology alone does not fix the issue. It is common to hear organisations expressing frustration about ineffective data governance even after significant platform investments. The most critical challenge to solve is not technical; it is communication. Data silos form when teams do not talk to each other, and no software tool fixes that on its own.
💡 SME Suggestion: In smaller organisations, data silos are often informal rather than structural. A simple, recurring cross-team meeting to discuss data-related issues can be a powerful first step before investing in any tooling.
Key takeaway: Data management is the foundation for digital transformation, and poor communication; not poor technology; is the biggest barrier to getting it right.
What a Data Strategy Actually Is
The word "strategy" is used loosely in business, so it is worth being precise. According to DAMA, a strategy is "a set of decisions that set a direction and define an approach to solving a problem or achieving a goal." Applied to data, Donna Burbank adds that it requires "an understanding of the data needs inherent in the business strategy."
A useful working definition from Lopez is this: Data strategies are the highest-level guidance in an organisation on intelligently assigning resources to work in an integrated way to achieve data-related goals and contribute to achieving business strategic objectives.
This is an important distinction. A data strategy is not a technology plan. It is not a list of tools to buy. It is a business-aligned set of priorities that connects data efforts to what the organisation is actually trying to achieve.
A well-developed data strategy should cover several areas: alignment to business goals, the data required to support those goals, the rationale for having a data management programme, data principles (governing by principles rather than enforcing rigid rules), prioritisation of capabilities and functions, and a clear picture of the organisational structure and roles needed to carry it out.
💡 SME Suggestion: In a small team, a data strategy does not need to be a lengthy document. A single well-structured canvas or one-pager that captures business questions, pain points, and priorities is a perfectly valid starting point.
Key takeaway: A data strategy is a business alignment tool, not a technical plan; it should connect data decisions directly to the goals of the organisation.
Knowing Where You Stand: The Data Management Maturity Model
Before you can build a strategy, you need to understand your current state. This is where a data management maturity model comes in. A maturity model measures the ability and effectiveness of people to perform processes within specific disciplines. It acts as a guide to define the roadmaps that take an organisation from its current state to a desired one.
One widely used framework is the Data Management Capability Assessment Model (DCAM) by the Enterprise Data Management Council. It describes the capabilities and actions required to acquire, produce, handle, and maintain trustworthy data. DCAM follows six maturity levels: not initiated, conceptual, developmental, defined, achieved, and enhanced.
Maturity can be assessed in two ways: by asking people how they perceive the current state, or by collecting evidence of maturity. Both approaches have value. The key insight is that without a clear picture of current maturity, it is impossible to prioritise what to do next. Clearly defined capabilities and maturity targets anchor the milestones in your roadmap.
💡 SME Suggestion: A full DCAM assessment can be resource-intensive. For smaller teams, a simplified self-assessment across five or six key data management areas (such as data quality, metadata, and governance) gives enough signal to start prioritising without requiring months of work.
Key takeaway: Assessing your current data management maturity is not optional; it is what makes prioritisation possible and turns vague ambitions into a concrete roadmap.
The PAC Method: A Framework Built for Action
The Data Strategy PAC Method provides a structured approach to defining and communicating data strategies. PAC stands for Pragmatic, Agile, and Communicable. It has three components: the Data Strategies Framework, the Set of Data Strategy Canvases, and the Data Strategy Cycle.
The framework defines the different data strategies an organisation needs. The canvases are the tools used to communicate those strategies to stakeholders in a clear and accessible way. The cycle describes the steps that should be revisited annually to keep strategies current and relevant.
The key principle behind the method is prioritisation. No organisation can address everything at once, and trying to do so leads to poor progress and low confidence. The method gives teams a way to focus on what matters most right now while keeping an eye on the longer-term direction.
💡 SME Suggestion: For a small team, even completing the first cycle partially; starting with just the Data Alignment Strategy canvas; is meaningful progress. The goal is direction and shared understanding, not perfection.
Key takeaway: The PAC Method works because it forces prioritisation; it gives organisations a structured way to focus data efforts rather than trying to fix everything at once.
The Four Layers of a Data Strategy
Data strategy is not a single document. It is a set of layered strategies, each building on the one before it. Lopez defines four levels:
- Data Alignment Strategy: Identifies the data the organisation needs to execute its business strategy. It addresses the gap between the data the business currently has and what it actually needs. Stakeholders must agree on the top strategic statements that guide their work.
- Data Management Strategy: Prioritises which data management functions to focus on. Critically, no more than three data management functions should be addressed at the same time. One is always data governance; the other two are chosen based on priority.
- Data Governance Strategy: Defines the scope of data governance and sets short-, mid-, and long-term priorities across five categories: capabilities, structure, objects to govern, organisational units in scope, and metrics.
- Specific Data Management Function Strategies: After expectations are set for data management and governance, strategies are defined for individual functions, starting with the top two priorities.
All of these strategies must also align with the IT strategy, which is where all technology platforms live. Beyond IT, two other strategies play a supporting role: a change management strategy (because data strategies require people to change how they work) and a communication strategy (because a strategy that is not shared cannot be acted upon).
💡 SME Suggestion: In a small business, all four layers can often be captured in a lightweight set of one-page canvases rather than separate formal documents. The structure still matters; the format does not have to be heavy.
Key takeaway: Data strategy is not one thing; it is a stack of aligned priorities, and you build it from the top down, starting with business alignment before moving to governance and specific functions.
Making Strategy Visible: The Power of Canvases
One of the most practical ideas in the PAC Method is the use of canvases: single-page visual tools inspired by the Business Model Canvas by Alexander Osterwalder. The core idea is to simplify the communication of a strategy and increase the chance of a shared understanding across the business.
Each data strategy has its own canvas. The inputs to these canvases come from four sources: business strategic objectives, business questions (the questions leaders need answered, even if the data does not yet exist), data-related pain points (prioritised by impact), and motivations (what is driving the organisation to invest in data management in the first place). A fifth input, behaviours to be changed, addresses how people in the business interact with, use, and take responsibility for data.
The canvases include:
- Data Alignment Strategy Canvas: Built from business objectives, key questions, and pain points.
- Data Management Strategy Canvas: Adds motivations and data-related behaviours to the mix.
- Data Governance Strategy Canvas: Uses the same inputs as the data management canvas, focused on governance priorities.
- Data Management Specific Function Strategy Canvas: Used once the top priority functions are identified.
- Data Governance Business Model Canvas: Helps the data governance team develop a common understanding of what data governance means to the business.
💡 SME Suggestion: If your organisation has never discussed data strategy before, starting with just the Data Alignment Strategy Canvas in a single workshop with two or three stakeholders can surface a surprising amount of clarity about priorities and pain points.
Key takeaway: A canvas is not just a pretty slide; it is a communication tool that forces clarity and creates a shared reference point for everyone involved in data strategy.
Getting the Right People in the Room
Data strategies should be democratised. They should be clearly defined, widely communicated, and accessible to everyone who uses them. But who should actually be defining them?
The data governance lead plays the role of master orchestrator. They facilitate the process of articulating strategies but do not define them alone. The strategies must involve representatives from across the business who understand both the business problems and the data-related challenges in each area.
Sponsorship matters too. It is better to have a lead sponsor from the business side rather than the IT side. This ensures the strategy stays connected to business outcomes rather than becoming a technology project. Other sponsors can be senior managers from business units where the most significant data problems are found.
Several key success factors make the process work: detailed and careful planning, a proper kick-off meeting to align stakeholders, a level-setting session to create a shared baseline of data management concepts, early engagement with the internal communications team, and ensuring that data strategies are included in the broader strategic planning cycles of the business.
💡 SME Suggestion: In a small company, a single engaged sponsor at the leadership level; even a founder or a department head; can act as both strategic lead and communication champion. The key is commitment, not title.
Key takeaway: Data strategy definition must be a business-wide effort led by a data governance lead and anchored by a business-side sponsor, not driven solely by the IT or data team.
The Path Forward: Education, Assessment, and Action
Building a data strategy is not a one-time project. It is the beginning of a cycle. Lopez describes the journey toward an effective data management programme in four stages: education, assessment, data strategies, and operating models.
Education comes first, because not everyone needs the same level of knowledge but everyone needs to understand the basics. Organisations that skip this step often find that their strategies are misunderstood or ignored. Assessment follows: understanding how different stakeholders perceive the organisation and comparing that perception against a reference model. Identified gaps inform the prioritisation of the strategies that come next.
Once strategies are defined, the work moves into designing operating models; the conceptual designs for the top priority data management functions. From there, the team designs an operational plan derived from the high-level roadmap and enters the execution cycle.
The order and pace at which data management function strategies are developed depend entirely on the priorities set in the data management strategy. This is intentional. It is a system designed to move at the pace the organisation can sustain.
💡 SME Suggestion: For an SME, the education phase can be as simple as a 60-minute internal session where the data lead walks the team through key concepts. Getting everyone to speak the same language is one of the highest-leverage things you can do early on.
Key takeaway: Data strategy is a cycle, not a project; it begins with education and assessment, and its pace should match what your organisation can realistically sustain.
Where to Go from Here
The concepts covered in this article come from the first part of Data Strategies for Data Governance by Marilu Lopez, which focuses on the theory and context behind the PAC Method. If you want to move beyond the framework and into the practical steps of actually implementing data strategies in your organisation, the second part of the book is where to look. It walks through the implementation process in a step-by-step format, covering how to run workshops, complete the canvases, and build your roadmap from the ground up. Whether you are just starting to think about data governance or trying to bring more structure to an existing effort, the book is a practical and accessible guide worth having on your shelf.
