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 appear like in observe.
This text offers a brief overview of what technique work I consider is required to develop into data-driven. It provides a abstract of a detailed deep-dive I not too long ago wrote and is the fourth installment in a series demystifying data strategy.
I am Jens, a Business-minded knowledge knowledgeable with almost twenty years of expertise in implementing knowledge and AI use instances. I assist leaders throughout industries design methods and foster cultures that unlock the complete 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 Information-Pushed
The enterprise world is at present abuzz with developments in Synthetic Intelligence (AI) — from the race amongst main expertise firms 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 complete extent of AI’s advantages and dangers, let’s look past the hype and deal with how organizations can harness knowledge immediately to optimize or prolong their current enterprise fashions.
Information could be leveraged in numerous methods, together with:
- Management: Creating static experiences for monitoring and oversight
- Automation: Automating duties and choices to deal with difficult enterprise challenges
- Determination making: Producing insights that assist determination making of advanced enterprise issues
- Innovation: Create insights to ask and reply the precise questions on clients, opponents, expertise and trade
The potential of knowledge spans nearly each trade, together with healthcare, finance, retail, manufacturing, power & utilities, software program growth, media, and the general public sector. Moreover, data-driven alternatives exist throughout the whole worth chain of organizations in addition to inside most supporting features.
Regardless of this huge potential, many organizations battle to establish 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 instances that present worth to an organization are extremely particular to its context. Second, not solely are the use instances distinctive, however so too are the folks, challenges, and exterior situations that form a corporation’s journey to develop into data-driven.
Frequent challenges organizations face when making an attempt to leverage knowledge embrace Folks, 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 Information 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 current interpretations of data strategy contain fundamental misconceptions [1].
I’ve been working within the knowledge subject for roughly 20 years — first as a knowledge scientist in trade and consulting, and later as a knowledge strategist serving to organizations deal with 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 information side, a scarcity of ‘technique literacy’ prevents many firms from reaching larger success — together with leveraging knowledge.
- Data Strategy is commonly misdefined — its use and interpretation often contradict established definitions and well-known technique frameworks.
For my part, this lack of shared understanding creates a significant issue. It hampers targeted discussions between knowledge professionals — like myself — and executives, in the end appearing as yet one more roadblock for organizations in search of to unlock the worth of knowledge.
Nonetheless, I consider this challenge 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 typical 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 answer for advanced issues. My goal is slightly to contribute to readability and a joint language of enterprise and knowledge professionals — in the end growing the effectiveness of designing data-driven organizations.
This text is a abstract of a detailed playbook I recently published [1], offering a concise and targeted overview.
3. Understanding Enterprise Technique
Enterprise technique is about making deliberate selections — selections about an organization’s ambition, about which clients 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 methods 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 a 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 guide, and Professor Emeritus on the Rotman Faculty of Administration.
I personally contemplate Enjoying to Win one of the vital highly effective technique frameworks obtainable. I selected Enjoying to Win as a result of it’s well known as a regular 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 type of technique.
A vital side of the technique definition above is integration — the alternatives forming a profitable technique should match collectively right into a cohesive entire. They need to logically align and reinforce one another, resulting in a believable, difficult-to-replicate principle of how one can outperform opponents.
For example 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 should be well-connected to create a stable aggressive logic.
The Enjoying to Win framework organizes strategic selections into 5 key constructing blocks, visualized within the so-called Technique Alternative Cascade:
The cascade illustrates that a corporation must make selections for:
- Successful Aspiration: What successful means for the group.
- The place to Play: Which clients to serve and which services or products to supply.
- The best way to Win: The method to successful within the chosen market.
- Capabilities: The crucial actions and sources wanted.
- Administration Programs: The methods, processes, norms, tradition, and metrics required to construct and keep these capabilities.
These selections aren’t a loosely related record however should be fastidiously built-in to type a coherent and compelling entire — that is your technique.
One other crucial characteristic of strategy is that it is singular [4b]. Because of this an organization promoting one product in a single geography requires one enterprise technique. Nonetheless, a corporation can also have further practical methods. If a perform 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 Information 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, we’ve got 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 mandatory 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 more likely 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 slightly than embedding it inside the enterprise technique results in related misalignment.
For an additional typical misunderstanding of knowledge technique, you simply have to ask ChatGPT (right here I used model o3-mini-high), which properly averages what the web offers: “A knowledge 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 information context. Roger Martin, the originator of the Enjoying 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 initiatives with timelines, deliverables, budgets, and duties. Technique, however, is about uncertainty — it consists of selections that characterize a guess on how a corporation goals to win. Planning complements strategy however doesn’t change it. It ought to naturally observe technique design as a way to activate the technique.
5. Technique for Designing Information-Pushed Organizations
What technique work is then required to design a data-driven group? The reply is easy: if knowledge, analytics, and AI contribute to your organization’s potential to win, the related selections should be embedded in your corporation 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.
Throughout the strategy design process [4a], organizations should establish 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 could stay purely operational, specializing in monetary reporting, course of efficiencies, or different commonplace practices that opponents additionally observe. On this state of affairs, your group possesses ‘simply’ operational knowledge calls for.
No matter whether or not knowledge performs a strategic or operational function — or each — many organizations require a devoted perform for knowledge, analytics, BI, insights and/or AI. To function successfully, this function needs a strategy [6]. Relying on its scope and focus, you would possibly select to name this your ‘data strategy’ [10].
Occupied with extra particulars? I invite readers to deep dive into my data strategy playbook [1], which accommodates detailed processes, finest 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 purpose why organizations try to use dysfunctional knowledge technique variants, with the well-intended objective of serving to organizations develop into 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 a 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 should be discarded to create area for a shared language and mutual understanding.
By leveraging current and established technique frameworks, organizations can transfer past the widespread misconceptions surrounding knowledge technique and be certain that knowledge, analytics, and AI actually function enablers of enterprise success.
References
[1] Jens Linden, How Most Organizations Get Data & AI Strategy Wrong — and How to Fix It (2025), article revealed in Towards Data Science
[2] A. G. Lafley and Roger L. Martin, Playing to Win (2013), e book revealed by Harvard Enterprise Assessment Press
[3] Roger Martin’s website (2024)
[4] Roger Martin, Playing to Win/ Practitioner Insights (2024), web site with record of articles
[4a] Roger Martin, The Strategic Choice Structuring Process (2024), Medium article of the ‘Playing to Win Practitioner Insights’ sequence
[4b] Roger Martin, Strategy is Singular (2023), Medium article of the ‘Playing to Win Practitioner Insights’ sequence
[4c] Roger Martin, The Five Deadliest Strategy Myths, Medium article of the ‘Playing to Win Practitioner Insights’ sequence
[4d] Roger Martin, Why Planning Over Strategy? (2022), Medium article of the ‘Playing to Win Practitioner Insights’ sequence
[4e] Roger Martin, Strategy vs. Planning: Complements not Substitutes (2024), Medium article of the ‘Playing to Win Practitioner Insights’ sequence
[4f] Roger Martin, From Strategy to Planning (2021), Medium article of the ‘Playing to Win Practitioner Insights’ sequence
[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 revealed in Harvard Enterprise Assessment
[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 revealed in Toward Data Science
[10] Jens Linden, The Data Strategy Choice Cascade (2024), Medium article revealed in Towards Data Science
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