There appears to be a consensus that leveraging knowledge, analytics, and AI to create a data-driven group requires a transparent strategic method. Nonetheless, there’s much less readability and settlement on precisely what this strategic method ought to seem like in follow.
This text gives a brief overview of what technique work I consider is required to turn out to be data-driven. It provides a abstract of a detailed deep-dive I just lately wrote and is the fourth installment in a series demystifying data strategy.
I am Jens, a Business-minded knowledge skilled with practically twenty years of expertise in implementing knowledge and AI use circumstances. I assist leaders throughout industries design methods and foster cultures that unlock the total potential of knowledge and algorithms.
Desk 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 Problem of Turning into Knowledge-Pushed
The enterprise world is at present abuzz with developments in Synthetic Intelligence (AI) — from the race amongst main know-how corporations to construct ever extra superior fashions, to the alternatives for on a regular basis companies to leverage AI to chop prices, generate new income streams, or mitigate dangers.
Whereas time will reveal the total extent of AI’s advantages and dangers, let’s look past the hype and deal with how organizations can harness knowledge as we speak to optimize or prolong their present enterprise fashions.
Knowledge may be leveraged in numerous methods, together with:
- Management: Creating static stories for monitoring and oversight
- Automation: Automating duties and choices to handle difficult enterprise challenges
- Resolution making: Producing insights that assist determination making of advanced enterprise issues
- Innovation: Create insights to ask and reply the suitable questions on prospects, opponents, know-how and trade
The potential of knowledge spans just about each trade, together with healthcare, finance, retail, manufacturing, vitality & utilities, software program growth, media, and the general public sector. Moreover, data-driven alternatives exist throughout your entire worth chain of organizations in addition to inside most supporting features.
Regardless of this huge potential, many organizations battle to determine and unlock the worth of knowledge, analytics, and AI. There isn’t a easy recipe or common plan to changing into data-driven. It’s a advanced problem.
First, the use circumstances that present worth to an organization are extremely particular to its context. Second, not solely are the use circumstances distinctive, however so too are the individuals, challenges, and exterior situations that form a corporation’s journey to turn out to be data-driven.
Frequent challenges organizations face when making an attempt to leverage knowledge embrace Individuals, Group and Expertise.
The complexity of changing into data-driven is broadly acknowledged, and lots of organizations acknowledge the necessity for a strategic method to managing this complexity. Because of this, the time period ‘knowledge technique’ has gained vital consideration.
However what precisely is a knowledge technique? What issues does it remedy, and what does it not tackle?
2. The Downside With Knowledge Technique
Iargue that there is no such thing as a common consensus on these questions, neither inside the knowledge neighborhood nor amongst enterprise professionals. Furthermore, I assert that many present interpretations of data strategy contain fundamental misconceptions [1].
I’ve been working within the knowledge area for roughly 20 years — first as a knowledge scientist in trade and consulting, and later as a knowledge strategist serving to organizations sort out the various challenges they face in changing into data-driven. Recognizing the dearth of a shared understanding of knowledge technique, I felt that successfully guiding organizations requires a deeper grasp of Business Strategy.
Via my exploration of enterprise technique design, I arrived on the following conclusions:
- Enterprise technique itself is broadly misunderstood — regardless of the existence of well-established definitions. Even with out the info facet, an absence of ‘technique literacy’ prevents many corporations from reaching higher success — together with leveraging knowledge.
- Data Strategy is commonly misdefined — its use and interpretation steadily contradict established definitions and well-known technique frameworks.
In my view, this lack of shared understanding creates a major problem. It hampers centered discussions between knowledge professionals — like myself — and executives, in the end performing as one more roadblock for organizations searching for to unlock the worth of knowledge.
Nonetheless, I consider this difficulty is solvable. My proposal is to undertake a well-established enterprise technique framework and apply it to designing data-driven organizations. By doing so, we are able to create a standard language and a shared understanding for each enterprise leaders and knowledge professionals.
My ambition is to not dismiss approaches of esteemed colleagues who’ve efficiently utilized their very own methodologies. There isn’t a single resolution for advanced issues. My purpose is moderately to contribute to readability and a joint language of enterprise and knowledge professionals — in the end rising the effectiveness of designing data-driven organizations.
This text is a abstract of a detailed playbook I recently published [1], offering a concise and centered overview.
3. Understanding Enterprise Technique
Enterprise technique is about making deliberate selections — selections about an organization’s ambition, about which prospects it serves, which services or products it provides, and the way it delivers superior worth relative to opponents. It additionally consists of selections on which actions to prioritize and which techniques are wanted to measure success and progress. Each firm makes these selections — both consciously and explicitly by means of a method design course of or unconsciously and implicitly.
Technique is outlined as a set of built-in and mutually reinforcing selections that collectively type a compelling logic for the way an organization wins available in the market. Successful means creating sustainable aggressive benefit and delivering superior worth relative to the competitors.
This definition of technique aligns with the Playing to Win framework [2], developed by Roger Martin [3], a globally acknowledged enterprise thinker, CEO advisor, former Monitor marketing consultant, and Professor Emeritus on the Rotman College of Administration.
I personally take into account Taking part in to Win probably the most highly effective technique frameworks out there. I selected Taking part in to Win as a result of it’s widely known as a normal for efficient technique design. Furthermore, it comes with a complete ecosystem of sources, together with literature [2, 4], structured processes [4a], templates, and training programs [5] — all of which assist the design and activation of any form of technique.
A vital facet of the technique definition above is integration — the alternatives forming a profitable technique should match collectively right into a cohesive complete. They have to logically align and reinforce one another, resulting in a believable, difficult-to-replicate principle of how one can outperform opponents.
For instance this idea, I like to make use of the analogy of LEGO bricks — simply as LEGO items interlock to type a powerful construction, strategic selections have to be well-connected to create a stable aggressive logic.
The Taking part in to Win framework organizes strategic selections into 5 key constructing blocks, visualized within the so-called Technique Selection Cascade:
The cascade illustrates that a corporation must make selections for:
- Successful Aspiration: What successful means for the group.
- The place to Play: Which prospects to serve and which services or products to supply.
- The right way to Win: The method to successful within the chosen market.
- Capabilities: The vital actions and sources wanted.
- Administration Techniques: The techniques, processes, norms, tradition, and metrics required to construct and keep these capabilities.
These selections should not a loosely related checklist however have to be rigorously built-in to type a coherent and compelling complete — that is your technique.
One other crucial characteristic of strategy is that it is singular [4b]. Which means an organization promoting one product in a single geography requires one enterprise technique. Nonetheless, a corporation might also have extra purposeful methods. If a operate has its personal price range, it requires a strategy to guide its investment choices [6]. This results in the event of HR, IT, or Advertising methods, amongst others.
4. Frequent Knowledge Technique Misconceptions
Apersistent false impression is {that a} separate knowledge/analytics/BI/AI technique is required to outline how these parts create worth and aggressive benefit. Nonetheless, now we have simply established that an efficient enterprise technique is singular: there’s one coherent set of strategic selections that determines how a corporation creates aggressive benefit and delivers superior worth relative to the competitors.
Introducing a separate technique that defines how a corporation wins with knowledge, analytics, and AI — alongside a enterprise technique that defines the way it wins in any other case — is neither needed nor advisable. To place it within the phrases of the 1986 fantasy action-adventure Highlander: “There can only be one.” [7]
Proposing a knowledge technique on this dysfunctional type is prone to irritate enterprise professionals and executives — particularly these with a powerful understanding of technique. The identical precept applies to digital technique — treating it as a separate entity moderately than embedding it inside the enterprise technique results in comparable misalignment.
For one more typical misunderstanding of knowledge technique, you simply must ask ChatGPT (right here I used model o3-mini-high), which properly averages what the web gives: “An information technique is a complete plan that outlines how a corporation collects, manages, analyzes, and leverages knowledge as a strategic asset to drive decision-making and obtain its enterprise targets.”
Complicated plans with technique is a well known misunderstanding — even past the info context. Roger Martin, the originator of the Taking part in to Win framework, has devoted vital effort and time to explaining the difference between a plan and a strategy [8, 4c-f].
In essence, plans deal with certainty — they define tasks with timelines, deliverables, budgets, and tasks. Technique, alternatively, is about uncertainty — it consists of selections that signify a wager on how a corporation goals to win. Planning complements strategy however doesn’t exchange it. It ought to naturally comply with technique design as a way to activate the technique.
5. Technique for Designing Knowledge-Pushed Organizations
What technique work is then required to design a data-driven group? The reply is simple: if knowledge, analytics, and AI contribute to your organization’s capability to win, the related selections have to be embedded in what you are promoting technique. As with every method that gives a aggressive benefit, data-related strategic selections mustn’t exist in isolation however as an integral a part of the singular enterprise technique.
Through the strategy design process [4a], organizations should determine the place knowledge can create a aggressive edge. This defines the strategic knowledge calls for of a corporation.
Nonetheless, not each group will differentiate itself by means of knowledge, analytics, or AI [9]. For some, data-related efforts might stay purely operational, specializing in monetary reporting, course of efficiencies, or different normal practices that opponents additionally comply with. On this situation, your group possesses ‘simply’ operational knowledge calls for.
No matter whether or not knowledge performs a strategic or operational position — or each — many organizations require a devoted operate for knowledge, analytics, BI, insights and/or AI. To function successfully, this function needs a strategy [6]. Relying on its scope and focus, you may select to name this your ‘data strategy’ [10].
Serious about extra particulars? I invite readers to deep dive into my data strategy playbook [1], which incorporates detailed processes, greatest practices and trade examples.
6. Conclusions
Technique is essential to enterprise success. Sadly, rigorous and efficient enterprise technique design seems to be a misplaced artwork. That is one motive why organizations try to use dysfunctional knowledge technique variants, with the well-intended purpose of serving to organizations turn out to be data-driven.
Nonetheless, a well-designed enterprise technique is singular and defines how a corporation wins available in the market. If an organization wins with the assistance of knowledge and AI, these strategic selections are merely a part of the enterprise technique, forming an built-in set of selections that create a compelling logic for the way the group can sustainably outperform the competitors.
As a consequence, organizations should develop each technique and knowledge literacy as foundational parts for designing data-driven organizations. Furthermore, dysfunctional knowledge technique approaches have to be discarded to create area for a shared language and mutual understanding.
By leveraging present and established technique frameworks, organizations can transfer past the widespread misconceptions surrounding knowledge technique and be certain that knowledge, analytics, and AI really function enablers of enterprise success.
References
[1] Jens Linden, How Most Organizations Get Data & AI Strategy Wrong — and How to Fix It (2025), article printed in Towards Data Science
[2] A. G. Lafley and Roger L. Martin, Playing to Win (2013), e book printed by Harvard Enterprise Evaluate Press
[3] Roger Martin’s website (2024)
[4] Roger Martin, Playing to Win/ Practitioner Insights (2024), web site with checklist of articles
[4a] Roger Martin, The Strategic Choice Structuring Process (2024), Medium article of the ‘Playing to Win Practitioner Insights’ collection
[4b] Roger Martin, Strategy is Singular (2023), Medium article of the ‘Playing to Win Practitioner Insights’ collection
[4c] Roger Martin, The Five Deadliest Strategy Myths, Medium article of the ‘Playing to Win Practitioner Insights’ collection
[4d] Roger Martin, Why Planning Over Strategy? (2022), Medium article of the ‘Playing to Win Practitioner Insights’ collection
[4e] Roger Martin, Strategy vs. Planning: Complements not Substitutes (2024), Medium article of the ‘Playing to Win Practitioner Insights’ collection
[4f] Roger Martin, From Strategy to Planning (2021), Medium article of the ‘Playing to Win Practitioner Insights’ collection
[5] Jennifer Riel, How to Make Your Strategy Real (2024), IDEOU Weblog entry
[6] Roger Martin, Jennifer Riel, The One Thing You Need to Know About Managing Functions (2019), article printed in Harvard Enterprise Evaluate
[7] Jens Linden, Data Strategy with a twist — there can be only one! (2025), LinkedIn weblog 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 printed in Toward Data Science
[10] Jens Linden, The Data Strategy Choice Cascade (2024), Medium article printed in Towards Data Science
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