The Root Cause of Why Organizations Fail With Data & AI

Wave by CLEANPNG

DEMYSTIFY DATA STRATEGY

A guide to be successful with the strategic groundwork required

Business value through Data and Artificial Intelligence. Everybody talks about it, yet most companies are struggling to monetize their data. I claim that in most cases, this is due to a lack of effective business strategy. This article shows what strategic groundwork is necessary to activate your company’s data assets.

This is part two of a series of articles, in which I demystify data strategy — an essential component for any organization striving to become data-driven in order to stay competitive in today’s digital world.

I am Jens, a business minded data expert with nearly 2 decades of practical experience in implementing data & AI use cases. I advise leaders from various industries on designing strategies & cultivating a data culture to leverage data, analytics & AI.

tl;dr

The lack of real business strategy is the root cause why so many organizations fail with their data, analytics and AI ambitions and struggle to become data-driven.Therefore, organizations should first invest in the design of their business strategies.After that, data-related choices should be charted throughout the entire organization.

Table of Contents

1. What is Going Wrong With Data?
1.1 Symptoms of Dysfunctional Data Value Creation
1.2 The Root Cause
1.3 How Big is The Problem?
2. Playing To Win
2.1 Its Origins
2.2 Strategy Is About Making Choices
2.3 The Strategy Choice Cascade
2.4 The Cheat Sheet
2.5 Making Integrated Choices
2.6 Chartering Choices
2.7 Bringing Strategy to Life
3. Data Business Needs
3.1 No Clear Data Needs
3.2 Data as Operational Duty
3.3 Data as Strategic Differentiator
3.4 Data as Business
3.5 Why Does it Matter to Know Your Data Business Needs?
4 When Is a Dedicated Data or AI Strategy Needed?
4.1 The Data Strategy Mystery
4.2 Strategy For a Data or AI Functions
4.3 A Plan to Build Data Capabilities
5 Putting It All Together
5.1 A Process to Lay The Strategic Groundwork for Data
5.2 An Example for Chartering Data-Related Choices
6 Conclusion
References

1. What is Going Wrong With Data?

Organizations big or small, public or private, all strive to become data-driven. Being data-driven means that data, which is created by internal operational processes is ‘recycled’ to support human decision making, to automate decisions and to enable digital business models.

In general, leveraging data can lead to:

More revenueLess costs, more efficiency and marginBetter risk managementMore innovation and the possibilities of novel business models based on digital products and services

For more details on the motivation and context of data-driven organizations, see part 1 of this series of articles [1].

The data consulting industry, numerous data experts (including myself) and technology vendors never tire of repeating the compelling reasons, why organizational leaders should invest in their data capabilities including skills, competencies, methodology, culture and — of course — corresponding IT tools and platforms. A quote translated from [2] brings it to the point:

“Consistent value creation with data is already a decisive competitive advantage for companies today, and in the near future it will even be essential for survival.” [2]

The topic has gained attention in recent years due to the quite impressive technical advances and the resulting medial hype around Generative Artificial Intelligence (GenAI), which has arrived in the mainstream media and — at least for me personally —is even part of many private conversations with family and friends.

Despite the current GenAI hype, the theory and the practice of data value creation are anything but new. It has appeared under different names in the past, such as Analytics, Business Intelligence, Statistical Modeling, Data Science, Machine Learning, Big Data and is now mainly called Artificial Intelligence. All these terms describe more or less different techniques to extract insights out of data, which — when done correctly — allows to improve business demonstrably.

Being a mathematician, I spent many years in the role of a Data Scientist applying statistical methods to detect patterns, which could be used by businesses to increase understanding of consumers, markets or competition, which in turn could be leveraged to increase revenue or to reduce costs or risks. There exist many practice-proven and well-understood use cases for data value creation in every industry and every point of the value chain.

Whilst there are clear applications and benefits of ‘recycling’ data to create additional business value and even to transform existing business models, I also observe a very different picture of business reality:

Many organizations just struggle to effectively and efficiently leverage data to create business benefits.

What is going wrong with data in organizations?

1.1 Symptoms of Dysfunctional Data Value Creation

The list of problems organizations have regarding data can be long [13] and each organization is certainly different. However, it often comes down to some typical suspects:

Unclear responsibilities for the orchestration and development of data solutions as well as supporting activities, such as data governance or data architecture.Unsuccessful data, analytics or AI initiatives: Analytics and AI projects take long and are expensive, or are initiated without a strong connection to specific business objectives, leading to projects that do not add measurable value.Lack of data awareness: The potential and benefits for leveraging data as an asset are unclear to employees and executives. The current and future role of data is simply not understood and opportunities are missed out. This often goes together with a lack of top-management attention.Silos of two kinds exist: organizational silos prevent collaboration, innovation and effectiveness, and data silos prevent people from accessing, combining and leveraging data from different parts of the organization, resulting in fragmented views and redundant or contradictory results.Lack of skilled personnel: Lack of data-affine business users or data experts in the organization.Poor data quality: Teams frequently report issues with data accuracy, completeness, or consistency, leading to a lack of trust in insights and decisions based on that data.

These are are symptoms many organizations know all too well. But what is the root cause of all this pain?

1.2 The Root Cause

There was and there still is the persistent misconception that data value creation is a technical challenge. If we just introduce the right platform and provide the required training, the organization will be able to effectively and efficiently use data to create business value. But this is a fallacy.

IT platforms, tools and algorithmic libraries have been around for a while, have matured, are ready to use and continue to improve. And whilst the technical extraction and preparation of data should be done ‘properly’, in order to achieve high quality data as well as scalable and flexible data models, there exist proven frameworks and best-practices to achieve exactly that.

To choose and implement IT platforms, tools, best practices and algorithmic libraries for data is a complicated, but not complex problem, which is actually solved.

Organizations either have the expertise to select and implement technology in-house or can easily get solid external expert consulting on the matter.

I claim, that the root cause for dysfunctional data value creation is a lack of coherent business strategy:

The lack of business strategy is the root cause why so many organizations fail with data, analytics and AI.

If the business strategy is not explicitly defined, organizations struggle to build and maintain the data capabilities they require to effectively and efficiently discover, innovate, build and maintain data-driven solutions.

Figure 1: Strategy as root cause why most companies fail with data. Plant picture by CLEANPNG.

For example, one core data capability required to be successful with data value generation is (data) culture [3, 4], which ensures that employees:

Understand what is possible with dataWant to use data to improve businessCan use data to do soMust use data, because it is expected from their leaders

However, in order to create an effective data culture, you need to know the direction your target culture should head to. You have to know the data business needs, which guide you in which direction you need to develop your target data culture. And these data business needs are provided by strategy.

Without a clear business strategy, it’s hard to agree on what the business needs for data, analytics, and AI are in the organization. The role of data value creation is then unclear. However, in order to build tailored and sustainable data capabilities, you simply need to understand if the main purpose of data in your organization is, for example, to provide a set of reports to the CFO or if data is a means to drive your competitive advantage, say through digital products and services.

By making implicit choices explicit through thoughtful strategy design, you build a form of communication to create a consensus about your data business needs.

Getting your data & AI capabilities right is surely not the sole benefit of crafting a solid strategy for your organization. It is rather a side effect. This should be self-evident, but for the sake of completeness, the benefits of having an explicitly and well-designed business strategy are for example:

Competitive edgeClear direction and goalsFocus and effective resource allocationEmployee alignment and moraleLong-term success and sustainabilityEffective risk management

1.3 How Big is The Problem?

When I start an engagement with a new client to help with data-related problems, one of the first information requests I usually carry out is to ask for relevant strategy documentation, most importantly the (corporate) business strategy. I often receive vague responses like:

“Our strategy is not documented, yet.”“Our strategy is outdated and needs an update.”“I am not sure, I have to do some research.”“We have a kind of action-oriented strategy.”

In cases when a strategy documentation is provided, it often turns out to be a loose list of initiatives, which all sound good, but overall does not meet common strategy standards [5a].

While I am not aware of representative statistics on the matter, and the presence of a good business strategy might certainly depend on the size of the organization and potentially other factors, such as industry, my personal impression of the German speaking market, the playing field in which I am most active, is that many companies are lacking a solid, explicit, well designed strategy.

Very few organizations pursue a solid, explicit business strategy, from which the required data, analytics and AI capabilities can be deduced.

In my experience, things look particularly challenging with small and mid-sized enterprises, the “Mittelstand”, which is the backbone of the German economy for which around 56% of employees work [6].

With this article, I aim to address the root cause of organizational data failures by clarifying what strategic work is necessary to leverage data as an asset.

To get started, let’s have a close look at what strategy actually is.

2. Playing To Win

2.1 Its Origins

Playing to Win is a strategy framework originated by Roger Martin [7], named world’s #1 management thinker in 2017, trusted advisor to CEOs of companies around the world, former Monitor consultant and former Professor of the Rotman School of Management.

The framework was developed and continuously refined in the 1980s and 1990s [5b] and culminated in a joint book [8] by Roger Martin and A. G. Lafley, who is a former CEO of P&G. The book was published in 2013.

The framework became the standard strategy methodology at P&G and has successfully been applied in many industries since. In addition, the Playing to Win framework has constantly been detailed by Roger Martin through a series of Strategy Practitioner Insights [5, 5c].

Whilst there might be other well-suited strategy frameworks out there, I choose the Playing to Win approach above others, since it is widely known and applied and comes with an entire ecosystem of processes, templates, resources and trainings that can be leveraged for designing any kind of strategy.

2.2 Strategy Is About Making Choices

In the Playing to Win framework, strategy is defined as follows [8]:

“Strategy is an integrated set of choices that uniquely positions a firm in its industry so as to create sustainable advantage and superior value relative to the competition.”

So strategy is about making (often tough) choices that give your firm a competitive edge. There is, however, no bullet-proof certainty that these choices will turn out to be the right ones. The result is always a strategic bet. The strategy design process aims to shorten your odds, i.e. to maximize the probability of success, given the information available.

Note that this definition of strategy is quite different to a plan, which is often misunderstood as strategy [9, 5d].

“Planning is the act of laying out projects with timelines, deliverables, budgets, and responsibilities.” [5d]

However, a strategy and a plan are not exclusive. Crafting plans is a natural part of the strategy design process, in order to build the capabilities required to bring the strategy to life (cf. Section 2.7 below).

There is another definition of strategy, which I particularly like, namely: “Strategy is the logic that determines what you choose to do and not do in service of a particular goal” [10].

Consequently, every organization has a strategy, whether it’s written down or not. Such implicit strategy can be deduced from the actions a company takes. The problem with such implicit strategies is, that there might not be a consensus in the organization what this implicit strategy is and that actions may be ineffective.

2.3 The Strategy Choice Cascade

Within the Playing to Win framework, there are five key choices every strategy designer must make. These choices are structured in the so called Strategy Choice Cascade, which is one of the core tools of the framework.

Figure 2: The Strategy Choice Cascade helps to structure the strategic questions you need to answer during strategy design.

The actual heart of the strategy is made of the coherent choices made in boxes two and three: Where to Play & How to Win.

How the cascade is applied is best illustrated using a real-world example, which is taken from [9] and describes choices for the corporate strategy of Southwest Airlines.

Figure 3: The Strategy Choice Cascade example for Southwest Airlines. The cascade summarizes most relevant strategic decisions.

The cascade is not filled out just once; it is revisited and utilized repeatedly for various use cases throughout the strategy design process:

Reconstruct the strategy of your competitors by observing what they do: This is a nice practice to familiarize yourself with the framework and is a kind of icebreaker [5e] to get started with the strategy design process.Document your current strategy: This is a good way to build a common understanding of the status-quo and make your current implicit choices explicit, if this has not done been before.Document new strategic possibilities: The process for designing a strategy [5f] contains a creative element, where many possible scenarios for the future target strategy are generated and systematically evaluated. These are documented and detailed using the strategy choice cascade, one for each possibility.

2.4 The Cheat Sheet

When I work with the cascade, my Playing to Win Cheat Sheet assists me, which contains some further information on the individual boxes together with the main sources for the framework.

Figure 4: Playing to Win Cheat Sheet providing details to each element of the strategic choice cascade.

2.5 Making Integrated Choices

When working on the strategy cascade, the boxes should never be filled individually [5g], but need to be considered together as the choices of a real strategy should fit together nicely and reinforce each other.

This applies particularly for the Where to Play and How to Win, which are inseparable pairs and always need to be considered together [5h].

But also when considering the Must-Have Capabilities and Management Systems, you need to keep your Where to Play and How to Win in mind. Capabilities and systems are a kind of a reality check: if you cannot build the capabilities and systems required to bring your strategy alive, you need to think of a different way to win and sometimes even of a different playing field.

Figure 5: Working on the cascade is a forth and back, where you need to keep the background in mind while working on the foreground.

From this it should be clear, that the exercise of filling the strategy choice cascade can be a quite iterative process moving back and forth until you arrive at a set of choices with a convincing theory of winning for the chosen playing field enabling the winning aspiration, which can realistically be achieved with the required capabilities, which are built & maintained with the needed management systems.

2.6 Chartering Choices

Strategic choices occur throughout the organization and not all choices are made at the top (corporate) level.

Choice Chartering [5i] is the structured process of defining, communicating, and delegating strategic choices throughout an organization to ensure aligned and effective decision-making at all levels.

Consequently, in large organizations there exist nested strategies and corresponding choice cascades, e.g. for a country, category, brand, or business unit strategies.

Not only individual business units need to make their own strategic choices and thus formulate nested choice cascades, but also functions [10], such as IT, HR, Finance, Marketing, R&D or Data & AI.

Figure 6: Nested choice cascade illustrated for the strategy of a function.

One particularity with functional strategies is, that they can be treated as natural monopolies [5j], where (internal) customers usually have no other choice than using the offering provided from that function.

2.7 Bringing Strategy to Life

A good strategy design process does not distinguish between the creation and subsequent execution phase, but builds activation into the strategy process from the very first beginning [11].

The goal of strategy activation is that everyone understands the design choices made and stakeholders are enabled to make their own choices to support the strategy.

Strategy activation includes the creation of stakeholder engagement, to make the strategy tangible and to move from design into action by building the Must-Have Capabilities and corresponding Enabling Management systems.

Figure 7: By building the capabilities and systems required, the strategy is brought to life.

Once the capabilities and systems are defined, they need to be built and maintained. A fit-gap-analysis — often called maturity assessment — for each capability or system then provides the input to create plans for building, buying or borrowing the capabilities and systems needed.

Interestingly, (data) consultancies often offer to start with maturity assessments as a kind of foot-in-the-door offering for clients. From the above mentioned, it should however be clear, that any fit-gap activities only make sense after the groundwork of strategic work has been finalized.

Bringing strategy to life also provides the link to planning, which is often confused with strategy: strategic choices typically require new capabilities to be built, for which actions plans are required, once the status-quo has been evaluated.

3. Data Business Needs

Once you have made your business strategy explicit, it should be clear what role data, analytics and AI play for your organization, i.e. what your (corporate) data business needs are.

It is as simple as that:

Business strategy must provide an answer to how relevant data for your organization or business unit will be.

When using the Playing to Win framework, one glance at the choice cascade of your business strategy should clearly indicate the relevance of data for your business.

The data business needs can take one of four different forms.

Figure 8: Overview of four possible data business needs highlighting the corresponding elements of the business strategy choice cascade in light yellow, where data, analytics and AI play a role.

Let’s deep-dive into each of these four scenarios.

3.1 No Clear Data Needs

In this scenario, there is no consensus in the organization if data value creation is at all necessary to win. That means, data seems to be irrelevant for any of the five boxes of the strategy choice cascade.

Example

An example might be a small traditional crafts business focused on handmade goods, which relies on artisanal skills, reputation, and customer relationships.

Recommendations

While I believe that data, analytics and AI are not the center of the universe and not every organization depends on it to win, as a potentially biased data professional, I would naturally challenge such a viewpoint by asking:

What current business pains & gains could be addressed with the help of data analytics & AI?What role can data, analytics & AI play for innovation?What solutions are already leveraged by competitors, or companies in your own or other industries?Are the potential benefits worth it to invest more energy and resources in this?

The organization should gain clarity about potential data use cases and how relevant these are for the business.

3.2 Data as Operational Duty

In the second scenario, data, analytics and AI are operational, but not strategic capabilities and/or systems, and should therefore be realized as cost-efficient as possible. Relevant data elements can be found within in box four and five of the cascade, namely Must-Have Capabilities and Enabling Management Systems.

Example 1

For the case of Southwest Airlines above, the business need for data, analytics and AI is to manage the complexity of airline operations efficiently and cost effectively.

The company needs to build, run and maintain Enabling Management Systems to ensure operational efficiency [12]:

A performance management system to track aircraft on-time performance in real-timeA system to monitor and optimize gate waiting timesA system to minimize fuel costsA decision support system for the planning of new stations and routes

This means for the Southwest example, the business needs for data, analytics and AI are mainly found in the last box — Enabling Management Systems.

Example 2

A regional utility company uses data, analytics and AI to monitor energy usage, predict maintenance needs, and generate internal reports. Like in Example 1, data helps to achieve operational efficiency and to meet compliance requirements, but does not provide a strategic differentiation.

Hence, data, analytics and AI are capabilities (box 4) or systems (box 5) representing an operational imperative rather than a strategic capability.

Examples for data related enabling management systems could be data reporting systems needed to provide management insights, BI tools or a corresponding data platform.

Recommendations

When building and maintaining data related capabilities and systems of the business strategy for this scenario, cost-efficiency should be a key dimension when determining whether to build, borrow or buy the capabilities and systems needed to win in the specified playing field.

Whilst there is nothing wrong with using data solely for operational efficiency, organizations should regularly assess whether strategic opportunities are being overlooked. Naturally, as a data professional I would challenge this by asking:

What role can data, analytics & AI play for innovation?Can current products and services be extended with data-based insights leading to added value for the customers and/or differentiation from competition?

Again, organizations should aim for achieving clarity about potential data use cases and how strategically relevant these are for the current business model or if the existing business model could be extended using data, analytics and AI.

3.3 Data as Strategic Differentiator

In this scenario, data, analytics, and AI serve as core differentiators, directly influencing how the company wins in the market. They are a key part of realizing the company’s value proposition, helping the firm to create a competitive edge (box 3: How to Win) or are key strategic capabilities (box 4: Must-Have Capabilities) to realize the theory of competitive advantage.

Example

This example, which is borrowed from [5k], is the one of Frito Lay, a salty snack producer (Lays, Doritos, …), which uses a direct-store-delivery system, where the product is directly delivered to convenience stores and placed on the shelf by the delivery driver.

This direct-store-delivery system is labor-intensive and hence expensive, but it differentiates Frito Lay from its competition. Having a large number of well-known products creates a massive cost advantage for Frito Lay’s delivery system (one stop for many products) producing a unique competitive advantage, that cannot be matched by competitors.

Building data and AI solutions, that predict store inventory and generate the optimal product orders for each point-of-sale, helps to reduce costs for the direct-store-delivery system. Hence, data, analytics and AI are strategic capabilities (box 4: Must-Have Capabilities), which directly support the competitive advantage (box 3: How to Win).

Recommendations

Use the business strategy cascade to communicate the strategic importance of data in your organization. Make sure that executives and all relevant stakeholders understand the impact and value of data, analytics and AI for your business. This lays the foundation for necessary data culture and data literacy developments and the organizational changes required to fully build the capabilities needed to win.

Finally, it also helps in discussion regarding funding as well as to set the right priorities.

Critically question whether it makes sense to outsource any of the data capabilities, as they are of strategic importance for the organization.

Push your thinking, consider whether there are additional opportunities for data monetization [16], potentially leading to ‘Data as Business’.

3.4 Data as Business

This is the scenario, which most people get excited about: data extends or even radically changes the current business model and organizations enter the digital economy.

In this case, digital data-driven products or services are part of the company’s offering (box 2: Where to Play) and can even be part of the vision (box 1: Winning Aspiration).

Example

This example is also borrowed from [5k] and is about Westlaw, a digital provider of legal search services. One of Westlaw’s strategic assets is a database of notes about court cases dating back to 1975, which allows lawyers to browse historical cases for their legal matters at hand.

The data is part of the firm’s offer (box 2: Where to Play) and its uniqueness in the market secures its competitive advantage (box 3: How to Win).

Recommendations

As data is clearly a company asset that secures a competitive advantage, make sure the data is treated accordingly. For example, it needs caring, nurturing, protection and quality assurance.

Hence data governance (box 4: Must-Have Capabilities) and corresponding systems (box 5: Enabling Management Systems) are an imperative.

3.5 Why Does it Matter to Know Your Data Business Needs?

Each organization is different and has unique data business needs.

Your organization needs to be aware what role data, analytics and AI plays in the business strategy, in order to know how much resources to spend for creating and maintaining the data-related capabilities and systems.

Many executives still struggle with the hype around data and AI leading to skepticism, uncertainty or to thoughtless investments into it.

Consequently, a solid business strategy and a thorough understanding of the data business needs provide guidance and helps to communicate the importance and role of data in your organization.

Therefore, I claim:

The first step to become data-driven is solid business strategy design.

4 When Is a Dedicated Data or AI Strategy Needed?

4.1 The Data Strategy Mystery

If you already think there is confusion around the term strategy, let me tell you it is even worse with the term data strategy. Nearly each article or book I read defines it somewhat differently — if it defines it at all. This gave me frequent headaches when I started to deep-dive into the topic.

In most cases the term ‘data strategy’ is used to refer to a plan for building data-related capabilities within an organization.

It is referred to as a plan for building analytics IT platforms, to roll-out new BI-tools, to build data governance, to increase data literacy, to develop data culture or to build an operating model for data management, analytics and AI.

In my opinion, the term data strategy should only be used to describe the strategy of a function, which provides data services to foremost internal, but sometimes also external data customers.

4.2 Strategy For a Data or AI Function

In many cases organizations will need a data related function, delivering services such as:

Providing integrated, modeled and quality assured analytics-ready data for business self-service users (e.g. in form of data marts, which are browsed via data catalogues)Developing and maintaining BI dashboards and reportsConducting analyses for business users, which require specialist knowledge, such as data science or machine learningProviding consulting regarding the innovation, discovery and evaluation of data use cases or consulting services related to buy vs. make decisions (e.g. for GenAI solutions)Creating and performing teaching and training for data, analytics or AIProviding continuous information requests to internal or external stakeholders, such as data exchange with suppliers or satisfying regulatory data demands

If an organization has or will have a function for data management, analytics or AI, which provides services to (internal) data customers, it simply requires a strategy as outlined in Section 2.6 above.

How the strategy choice cascade is applied to design such a data strategy, was topic of part 1 of this series [1].

Figure 8: Visualization of the Data Strategy Choice Cascade [1] shows how to translate the cascade to the context of a data function.

A clear strategy allows the function to gain clarity, where to play and how to win with its data customers. It also guides and aligns its actions, to effectively allocate resources and to enhance the value provided to the organization.

If your data function does not have an explicit strategy, it is likely to default to a damaging implicit strategy, which is either the ‘servile strategy’ (trying to satisfy all possible business demands) or the ‘imperial strategy’ (doing data, analytics or AI just for the sake of it) [10].

Depending on the nature and focus of the services and products provided in this function, you may call it data strategy, analytics strategy, BI strategy or AI strategy.

The data strategy of the function usually contains capabilities and systems that span across or exist in multiple parts of the organization, such as a corporate analytics data platform. In strategy language, such capabilities and systems are referred to as a reinforcing rods [8, 5l].

4.3 A Plan to Build Data Capabilities

The plan to build data related capabilities and systems is the topic most companies are enthusiastic when it comes to data, analytics or AI.

To create such a plan can be a challenging endeavor, so it is well invested time. All I am saying is that you need to make sure, that the strategic groundwork has been done beforehand, so you know what kind of capabilities and systems you actually need to win. The nature of the plan and the requirements for many design choices depend on that strategic groundwork.

The scope of such ‘data plans’ can vary widely depending on the organization’s data business needs: from building a reporting system and corresponding data landscape exclusively for controlling purposes to enabling many business units and functions to extensively leverage potentials from data use cases along the entire value chain.

The latter often goes together with a company-wide transformation program, and the plans for building data capabilities can be part of an overarching data-driven business transformation roadmap, led by a dedicated leader for data, analytics or AI [14, 15] – sometimes called Chief Data Officer.

5 Putting It All Together

A plan to build data or AI capabilities and systems should come last, as the result of a fit-gap-analysis, in which the target state is provided by the preceding strategic choices.

Therefore, organizations require clarity about their data business needs, i.e. what role data, analytics and AI will play in their future business. The bottom line: You need to start with the strategic groundwork.

5.1 A Process to Lay The Strategic Groundwork for Data

A process to approach the design of effective data and AI capabilities, could look as follows:

Figure 9: A four-step process to lay the strategic groundwork for data, analytics and AI as basis for a data-driven organization.

1. (Re-) Design your business strategy

Identify existing strategic choices, whether implicit or explicitIdentify, what data business needs your organization or business unit has and make sure that the related strategic choices regarding data, analytics and AI are clearly formulated within your business strategyEnsure that executives and relevant stakeholders are aligned on the future role of data, analytics and AI in your organization

2. Charter relevant choices

Charter strategic choices for a data function and design your data strategy for the functionCharter other related data choices for relevant business units and/or functions

3. Assess & plan capabilities and systems

This applies for all levels of strategy within the organizationPerform a maturity assessment of each data related capability and system required to bring your strategy to lifeCreate a plan for building, extending or maintaining each capability and systemFor your data strategy this may include: organizational design, a data target operating model, technology and tools, data management, data integration, data modeling, data architecture, data governance, data democratization, data quality management, master data management, data innovation management, BI, data science, data literacy and data culture

4. Build capabilities and systems

Conduct the projects to build and maintain your capabilities and systems required to win with dataMeasure the success of your strategy and monitor progressBy laying the strategic foundation, you will address the root cause of dysfunctional data value creation in your organization.

Laying the strategic foundation ensures that executives and employees will understand the role of data within your organization. This, in turn, will help to build effective and sustainable data capabilities and to provide guidelines on the necessary resources to invest in developing and maintaining these capabilities.

For step 1, we already saw examples in Section 3 if this article. How might the choice chartering within step 2 look like?

5.2 An Example for Chartering Data-Related Choices

Let’s apply step two of the process to the above example of Frito Lay, where the store inventory forecasting supports the competitive advantage of the direct-store-delivery system.

This use case for store inventory prediction for the sales department may be one of many AI use cases for Frito Lay, so that an AI capability is required in many parts of the organization. Let’s say Frito Lay therefore decides to build a central AI capability leading to a dedicated AI function.

Figure 10: Example for chartering data & AI related choices in an organization.

The AI function leader is then tasked with designing an AI strategy that supports the corporate strategy and directly contributes to Frito Lay’s How to Win. One Where To Play in the AI strategy would be to provide forecasting solutions for the sales department.

To ensure the AI forecasting solution has an impact, it would also be necessary to charter data-related choices to the sales leader, such that sales commits to use the AI-driven insights within its planning process. This means that ‘AI-driven sales planning’ becomes a Must-Have Capability of the sales strategy.

6 Conclusion

The relevance of good business strategy for successful data value creation appears to be underestimated and undervalued. Companies must have a well-defined, explicit business strategy, from which any data, analytics and AI demands are apparent. This strategic foundation is the starting point for chartering data-related choices, whether in dedicated data management, analytics, or AI functions, and for building the necessary capabilities across business units.

For your organization to become data-driven, ensure you have a solid strategic foundation. Otherwise, you will likely run into troubles and will experience the symptoms of dysfunctional data value creation, which we all know too well.

Looking ahead, the competitive landscape is evolving rapidly. The most successful organizations of the future will be those that recognize data as a strategic asset, not just a byproduct of their operations. As AI technologies mature and integrate further into business processes, companies with a coherent strategy will be poised to capitalize on these advancements, creating new revenue streams, driving innovation, and maintaining a sustainable edge over their competition. In the coming years, we will see a growing divide between companies that have laid the strategic groundwork for data and AI and those that continue with business as usual. The former will thrive, while the latter may struggle to survive in an increasingly data-driven world.

The time to act is now: prioritize your strategy (re-) design, align on your data business needs, and build the capabilities needed to win with data. Only then can you truly harness the transformative power of data, analytics and AI for long-term success.

References

[1] Jens Linden, The Data Strategy Choice Cascade (2024), Medium article published in Towards Data Science

[2] Sebastian Wernicke, Data Inspired (2024), book in German language published by Vahlen

[3] Carsten Bange, Data Culture: Definition, Herausforderungen & Maßnahmen (2022), article

[4] Jens Linden, Datenkultur — Was & Warum? (2024), LinkedIn post

[5] Roger Martin, Playing to Win/ Practitioner Insights (2024), website with list of articles

[5a] Roger Martin, From Laudable List to How to Really Win (2020), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5b] Roger Martin, The Origins of Playing To Win (2023), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5c] Roger Martin, (Playing to Win) x 5 (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5d] Roger Martin, Strategy vs. Planning: Complements not Substitutes (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5e] Roger Martin, The Best Strategy Icebreaker (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5f] Roger Martin, The Strategic Choice Structuring Process, Medium article of the ‘Playing to Win Practitioner Insights’ series

[5g] Roger Martin, Overcoming the Integrative Strategy Challenge (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5h] Roger Martin, On the Inseparability of Where-to-Play and How-to-Win (2020), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5i] Roger Martin, Strategic Choice Chartering (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5j] Roger Martin, Strategy for Natural Monopolies (2024), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5k] Roger Martin, Distinguishing How-to-Win from Capabilities in Your Strategy Choice (2021), Medium article of the ‘Playing to Win Practitioner Insights’ series

[5l] Roger Martin, Corporate vs. Business Unit Strategy (2022), Medium article of the ‘Playing to Win Practitioner Insights’ series

[6] statista, Mittelstand als Rückgrat der Wirtschaft (2024), website in German language

[7] Roger Martin’s website (2024)

[8] A. G. Lafley and Roger L. Martin, Playing to Win (2013), book published by Harvard Business Review Press

[9] Roger Martin, A Plan Is Not a Strategy (2022), video

[10] Roger Martin, Jennifer Riel, The One Thing You Need to Know About Managing Functions (2019), article published in Harvard Business Review

[11] IDEO U, Activating Strategy (2024), website

[12] Tableau, Southwest Airlines maintains on-time flights and optimizes fleet performance with Tableau (2024), website

[13] Jens Linden, Data First Aid Kit (2022), LinkedIn Post

[14] Caroline Carruthers and Peter Jackson, The Chief Data Officer’s Playbook 2nd edition (2020), book published by Facet Publishing

[15] Caroline Carruthers and Peter Jackson, Data-Driven Business Transformation (2019), book published by Wiley

[16] Jens Linden, Datenmonetarisierung — Das ‘Where to Play’ für Datenstrategien (2024), LinkedIn Post

Unless otherwise noted, all images are by the author.

The Root Cause of Why Organizations Fail With Data & AI was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.