There seems to be a consensus that leveraging data, analytics, and AI to create a data-driven organization requires a clear strategic approach. However, there is less clarity and agreement on exactly what this strategic approach should look like in practice.
This article provides a short overview of what strategy work I believe is required to become data-driven. It offers a summary of a detailed deep-dive I recently wrote and is the fourth installment in a series demystifying data strategy.
I am Jens, a Business-minded data expert with nearly two decades of experience in implementing data and AI use cases. I help leaders across industries design strategies and foster cultures that unlock the full potential of data and algorithms.
Table of Contents
1 The Challenge of Becoming Data-Driven
2 The Problem With Data Strategy
3 Understanding Business Strategy
4 Common Data Strategy Misconceptions
5 Strategy for Designing Data-Driven Organizations
6 Conclusions
References
1. The Challenge of Becoming Data-Driven
The business world is currently abuzz with developments in Artificial Intelligence (AI) — from the race among leading technology companies to build ever more advanced models, to the opportunities for everyday businesses to leverage AI to cut costs, generate new revenue streams, or mitigate risks.
While time will reveal the full extent of AI’s benefits and risks, let’s look beyond the hype and focus on how organizations can harness data today to optimize or extend their existing business models.
Data can be leveraged in various ways, including:
- Control: Creating static reports for monitoring and oversight
- Automation: Automating tasks and decisions to address complicated business challenges
- Decision making: Generating insights that support decision making of complex business problems
- Innovation: Create insights to ask and answer the right questions about customers, competitors, technology and industry
The potential of data spans virtually every industry, including healthcare, finance, retail, manufacturing, energy & utilities, software development, media, and the public sector. Furthermore, data-driven opportunities exist across the entire value chain of organizations as well as within most supporting functions.
Despite this vast potential, many organizations struggle to identify and unlock the value of data, analytics, and AI. There is no simple recipe or universal plan to becoming data-driven. It is a complex challenge.
First, the use cases that provide value to a company are highly specific to its context. Second, not only are the use cases unique, but so too are the people, challenges, and external conditions that shape an organization’s journey to become data-driven.
Common challenges organizations face when trying to leverage data include People, Organization and Technology.
The complexity of becoming data-driven is widely acknowledged, and many organizations recognize the need for a strategic approach to managing this complexity. As a result, the term ‘data strategy’ has gained significant attention.
But what exactly is a data strategy? What problems does it solve, and what does it not address?
2. The Problem With Data Strategy
Iargue that there is no universal consensus on these questions, neither within the data community nor among business professionals. Moreover, I assert that many existing interpretations of data strategy contain fundamental misconceptions [1].
I have been working in the data field for approximately 20 years — first as a data scientist in industry and consulting, and later as a data strategist helping organizations tackle the many challenges they face in becoming data-driven. Recognizing the lack of a shared understanding of data strategy, I felt that effectively guiding organizations requires a deeper grasp of Business Strategy.
Through my exploration of business strategy design, I arrived at the following conclusions:
- Business strategy itself is widely misunderstood — despite the existence of well-established definitions. Even without the data aspect, a lack of ‘strategy literacy’ prevents many companies from achieving greater success — including leveraging data.
- Data Strategy is often misdefined — its use and interpretation frequently contradict established definitions and well-known strategy frameworks.
In my opinion, this lack of shared understanding creates a serious problem. It hampers focused discussions between data professionals — like myself — and executives, ultimately acting as yet another roadblock for organizations seeking to unlock the value of data.
However, I believe this issue is solvable. My proposal is to adopt a well-established business strategy framework and apply it to designing data-driven organizations. By doing so, we can create a common language and a shared understanding for both business leaders and data professionals.
My ambition is not to dismiss approaches of esteemed colleagues who have successfully applied their own methodologies. There is no single solution for complex problems. My aim is rather to contribute to clarity and a joint language of business and data professionals — ultimately increasing the effectiveness of designing data-driven organizations.
This article is a summary of a detailed playbook I recently published [1], providing a concise and focused overview.
3. Understanding Business Strategy
Business strategy is about making deliberate choices — choices about a company’s ambition, about which customers it serves, which products or services it offers, and how it delivers superior value relative to competitors. It also includes choices on which activities to prioritize and which systems are needed to measure success and progress. Every company makes these choices — either consciously and explicitly through a strategy design process or unconsciously and implicitly.
Strategy is defined as a set of integrated and mutually reinforcing choices that together form a compelling logic for how a company wins in the market. Winning means creating sustainable competitive advantage and delivering superior value relative to the competition.
This definition of strategy aligns with the Playing to Win framework [2], developed by Roger Martin [3], a globally recognized business thinker, CEO advisor, former Monitor consultant, and Professor Emeritus at the Rotman School of Management.
I personally consider Playing to Win one of the most powerful strategy frameworks available. I chose Playing to Win because it is widely recognized as a standard for effective strategy design. Moreover, it comes with a comprehensive ecosystem of resources, including literature [2, 4], structured processes [4a], templates, and training programs [5] — all of which support the design and activation of any kind of strategy.
A crucial aspect of the strategy definition above is integration — the choices forming a successful strategy must fit together into a cohesive whole. They must logically align and reinforce each other, leading to a plausible, difficult-to-replicate theory of how to outperform competitors.
To illustrate this concept, I like to use the analogy of LEGO bricks — just as LEGO pieces interlock to form a strong structure, strategic choices must be well-connected to create a solid competitive logic.
The Playing to Win framework organizes strategic choices into five key building blocks, visualized in the so-called Strategy Choice Cascade:
The cascade illustrates that an organization needs to make choices for:
- Winning Aspiration: What winning means for the organization.
- Where to Play: Which customers to serve and which products or services to offer.
- How to Win: The approach to winning in the chosen market.
- Capabilities: The critical activities and resources needed.
- Management Systems: The systems, processes, norms, culture, and metrics required to build and maintain these capabilities.
These choices are not a loosely connected list but must be carefully integrated to form a coherent and compelling whole — this is your strategy.
Another crucial characteristic of strategy is that it is singular [4b]. This means that a company selling one product in one geography requires one business strategy. However, an organization may also have additional functional strategies. If a function has its own budget, it requires a strategy to guide its investment choices [6]. This leads to the development of HR, IT, or Marketing strategies, among others.
4. Common Data Strategy Misconceptions
Apersistent misconception is that a separate data/analytics/BI/AI strategy is needed to define how these elements create value and competitive advantage. However, we have just established that an effective business strategy is singular: there is one coherent set of strategic choices that determines how an organization creates competitive advantage and delivers superior value relative to the competition.
Introducing a separate strategy that defines how an organization wins with data, analytics, and AI — alongside a business strategy that defines how it wins otherwise — is neither necessary nor advisable. To put it in the words of the 1986 fantasy action-adventure Highlander: “There can only be one.” [7]
Proposing a data strategy in this dysfunctional form is likely to irritate business professionals and executives — especially those with a strong understanding of strategy. The same principle applies to digital strategy — treating it as a separate entity rather than embedding it within the business strategy leads to similar misalignment.
For another typical misunderstanding of data strategy, you just need to ask ChatGPT (here I used version o3-mini-high), which nicely averages what the internet provides: “A data strategy is a comprehensive plan that outlines how an organization collects, manages, analyzes, and leverages data as a strategic asset to drive decision-making and achieve its business goals.”
Confusing plans with strategy is a well-known misunderstanding — even beyond the data context. Roger Martin, the originator of the Playing to Win framework, has dedicated significant time and effort to explaining the difference between a plan and a strategy [8, 4c-f].
In essence, plans focus on certainty — they outline projects with timelines, deliverables, budgets, and responsibilities. Strategy, on the other hand, is about uncertainty — it consists of choices that represent a bet on how an organization aims to win. Planning complements strategy but does not replace it. It should naturally follow strategy design as a means to activate the strategy.
5. Strategy for Designing Data-Driven Organizations
What strategy work is then required to design a data-driven organization? The answer is straightforward: if data, analytics, and AI contribute to your company’s ability to win, the relevant choices must be embedded in your business strategy. As with any approach that provides a competitive advantage, data-related strategic choices should not exist in isolation but as an integral part of the singular business strategy.
During the strategy design process [4a], organizations must identify where data can create a competitive edge. This defines the strategic data demands of an organization.
However, not every organization will differentiate itself through data, analytics, or AI [9]. For some, data-related efforts may remain purely operational, focusing on financial reporting, process efficiencies, or other standard practices that competitors also follow. In this scenario, your organization possesses ‘just’ operational data demands.
Regardless of whether data plays a strategic or operational role — or both — many organizations require a dedicated function for data, analytics, BI, insights and/or AI. To operate effectively, this function needs a strategy [6]. Depending on its scope and focus, you might choose to call this your ‘data strategy’ [10].
Interested in more details? I invite readers to deep dive into my data strategy playbook [1], which contains detailed processes, best practices and industry examples.
6. Conclusions
Strategy is key to business success. Unfortunately, rigorous and effective business strategy design appears to be a lost art. This is one reason why organizations attempt to apply dysfunctional data strategy variants, with the well-intended goal of helping organizations become data-driven.
However, a well-designed business strategy is singular and defines how an organization wins in the market. If a company wins with the help of data and AI, these strategic choices are simply part of the business strategy, forming an integrated set of choices that create a compelling logic for how the organization can sustainably outperform the competition.
As a consequence, organizations must develop both strategy and data literacy as foundational elements for designing data-driven organizations. Moreover, dysfunctional data strategy approaches must be discarded to create space for a shared language and mutual understanding.
By leveraging existing and established strategy frameworks, organizations can move beyond the common misconceptions surrounding data strategy and ensure that data, analytics, and AI truly serve as enablers of business success.
References
[1] Jens Linden, How Most Organizations Get Data & AI Strategy Wrong — and How to Fix It (2025), article published in Towards Data Science
[2] A. G. Lafley and Roger L. Martin, Playing to Win (2013), book published by Harvard Business Review Press
[3] Roger Martin’s website (2024)
[4] Roger Martin, Playing to Win/ Practitioner Insights (2024), website with list of articles
[4a] Roger Martin, The Strategic Choice Structuring Process (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series
[4b] Roger Martin, Strategy is Singular (2023), Medium article of the ‘Playing to Win Practitioner Insights’ series
[4c] Roger Martin, The Five Deadliest Strategy Myths, Medium article of the ‘Playing to Win Practitioner Insights’ series
[4d] Roger Martin, Why Planning Over Strategy? (2022), Medium article of the ‘Playing to Win Practitioner Insights’ series
[4e] Roger Martin, Strategy vs. Planning: Complements not Substitutes (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series
[4f] Roger Martin, From Strategy to Planning (2021), Medium article of the ‘Playing to Win Practitioner Insights’ series
[5] Jennifer Riel, How to Make Your Strategy Real (2024), IDEOU Blog entry
[6] Roger Martin, Jennifer Riel, The One Thing You Need to Know About Managing Functions (2019), article published in Harvard Business Review
[7] Jens Linden, Data Strategy with a twist — there can be only one! (2025), LinkedIn blog entry
[8] Roger Martin, A Plan Is Not a Strategy (2022), video
[9] Jens Linden, The Root Cause of Why Organizations Fail With Data & AI (2024), Medium article published in Toward Data Science
[10] Jens Linden, The Data Strategy Choice Cascade (2024), Medium article published in Towards Data Science
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