In Section 4 we saw that it is not task of the data strategy — defined as functional strategy — to define how the organization creates value or a competitive advantage using data, analytics or AI. This is the task of the business, aggregation and/or functional strategies, which we defined in Section 3.5. These strategies define how to win. And sometimes, the organization can win with the help of data, analytics and AI.
In this section, we first have a look at how data strategic choices are a natural part of business strategy design. With data strategic choices I mean those choices of an organization’s theory to win, which rely on or involve the use of data.
I then argue, that data use case discovery, innovation and validation should be viewed as an integral part of the Strategic Choice Structuring Process for business, aggregation or functional strategy design. This naturally bridges business strategy and data use case design, which is often perceived as one of the greatest challenges for organizations to become data-driven.
5.1 Data Choices as Part of Your Business Strategy
When you make your choices of Where to Play, How to Win and what capabilities and systems you need to win as part of your business strategy design, some of these choices might rely on leveraging data, analytics and AI. Strategic choices for data and AI capabilities are a natural part of your business strategy. I provided some examples for this in [5].
Therefore, organizations should consider data and AI as something, which needs to be weaved into the very fabric of their business strategy.
Strategic choices with data or AI relevance are simply part of your business, aggregation, or functional strategy.
For illustration purposes, think of your strategic choices as Lego pieces, from which you build your Strategic Choice Cascade, and data-related strategic choices are just an integral part of it.
Let’s have a look at an example, where AI is a capability as part of the strategy.
Example
I used this example, which is borrowed from [1x], already before in part two of this series of articles [5]. It is about a salty snack producer, 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 the company from its competition. It is a strategic choice for the regional aggregation strategy, those realization might be chartered [1r] to the Sales department.
Building data and AI solutions to predict store inventory and to generate optimal product orders for each point-of-sale helps reduce costs for the direct-store-delivery system. Hence, the choice to posses a data and AI capability, which enables the organization to predict store inventory, would be a strategic choice of the function Sales strategy, which directly supports the competitive advantage of the organization.
This data-related strategic choice provides a clear strategic data demand [5], i.e. a strategic requirements of the organization for the usage of data and AI. It is an integrated part of the company’s strategy and reinforces other strategic choices regarding differentiation from the competition.
The example illustrates that strategy — in this case it was the strategy of the Sales function — should define how data and AI create strategic value in the organization and not a supplementary data strategy ‘misconception’.
Data use case innovation should therefore be an integral part of business strategy design. The following two subsections show that the strategy design process of the Playing to Win framework provides a natural environment to integrate data use case innovation.
5.2 Data for Problem Formulation and Possibility Generation
Data use cases are the heart of data value creation, but organizations often struggle to identify their individual set of feasible and impactful use cases.
When innovating data use cases, it is often not differentiated whether the purpose is optimization of existing processes or the creation of new businesses opportunities. However, it is essential to differentiate between operational use cases, which address routine needs or improve efficiency within existing systems, and strategic use cases, which directly support competitive advantage and enable new ways of operating. The latter should be part of business strategy design.
There exist well-known and proven formats and techniques for use case discovery and innovation [2, 22]. These often incorporate elements of Design Thinking and are called Data Thinking [23], due to the inherent iterative approach required.
In fact, these formats and techniques can — and should — be directly integrated into the business strategy design process rather than being stand-alone exercises. This aligns nicely with the Strategic Choice Structuring Process, which is not surprising, as it also leverages Design Thinking [1o]. Thus, the Playing to Win framework allows to naturally combine data use case innovation and business strategy design, bridging the gap between business and data folks.
Integrating data use case innovation into the business strategy design process contributes to bridging the gap between business and data folks
This way, the business strategy design team can leverage data and AI as a tool to create strategic innovations by combining human creativity with data insights. This applies to Step 1 – problem formulation – as well as to Step 3 – possibility generation. Let’s have s look at an example.
Example
Consider the salty snack food company as in the previous examples. The salty snack food company noticed declining appeal among health-conscious consumers. An analysis of sales data, combined with social media sentiment analysis, highlighted a key insight: customers increasingly sought personalized snack options. Inspired by this insight, the strategy design team moved beyond merely optimizing existing offerings and stated the following problem with the corresponding how might we question:
1 Problem Definition
Customers are increasingly seeking personalized snack options, but our current product offerings are standardized, leading to declining market share among health-conscious consumers.
2 How Might We Question
How might we better meet the growing demand for personalized snack options among health-conscious consumers?
By combining the data-driven insights with creative formats, the team developed the following innovative possibilities:
3.a Possibility ‘Snack Kits’: Offer build-your-own snack kits in stores where customers can manually select ingredients to create their own mix, catering to their unique preferences.
3.b Possibility ‘Influencers’: Collaborate with health and wellness influencers to design branded snack bundles targeted at their followers, promoting personalization through co-creation with trusted figures.
3.c Possibility ‘Direct-to-Consumer Platform’: Design a direct-to-consumer platform where customers can input their dietary preferences and receive personalized snack bundles using AI-driven recommendations based on insights from customer data.
These possibilities weren’t simply derived from data — they were inspired by data, leveraging insights as a foundation for creative solutions that addressed both customer desires and business goals.
Furthermore, with possibility 3.c the design team created a new data use case. This illustrates that within the strategy design process, data and AI can be used to:
- Inspire the problem formulation
- Create new data use cases when innovating new possibilities
The implication for this is, that:
Subject matter experts for data, analytics and AI should be a natural part of any strategy design.
This connects nicely back to Section 4.4 and serves as a general motivation for including ‘business strategy design support’ as a Where to Play choice within the data offering of the data strategy.
Not only the innovation of new data use cases can be incorporated into the business strategy design, but also the subsequent iterative and incremental development.
5.3 Data Use Case Validation as Part of Testing
When organizations discover or innovate data and AI use cases, it is not always clear whether the desired solution will work as intended. There are uncertain assumptions that need to be identified, evaluated and eventually validated often using proof-of-concepts or minimum viable products. This is where design thinking meets data use case design.
This process perfectly aligns with Step 6 — Testing and Transformation — of the Strategic Choice Structuring Process outlined in Section 3.4.
The Strategic Choice Structuring Process naturally allows integrating iterative and incremental data use case development.
For possibility 3.c (Direct-to-Consumer Platform) of the previous example, this might look as follows:
Example (continued)
For each strategic possibility generated, the assumptions for the possibility being a great strategy need to be identified and evaluated. This is the task of Step 4 — What Would Have to Be True? — of the Strategic Choice Structuring Process.
Step 4 is about identifying critical conditions for customers, competition and company. To illustrate the concept, some of the critical conditions might be:
For customers, it would have to be true, that health-conscious consumers are willing to use a digital platform to personalize their snack options.
For competitors, it would have to be true, that they are either unable or unwilling to replicate the direct-to-consumer platform quickly. This is the so called can’t/won’t test [1x, 1y, 1z].
For company, it would have to be true that we can consistently derive meaningful and actionable recommendations from historical consumer data to personalize snack bundles effectively.
If the strategy design team would not be reasonably confident about that any of the identified critical conditions is currently true or could be made true, it would declare it as a barrier, which might stop the team from choosing possibility 3.c as final strategy. If a barrier has been identified, the team would design and conduct a test, in order to learn more and to gain (or lose) confidence that the condition could be made true. This is Step 6 of the Strategic Choice Structuring Process.
For the last of the three critical conditions above (company), a meaningful test could be to design a minimum viable product, where a data science team builds an algorithm using data available. The recommendations are then probed with a set of potential customers.
Such a test would validate, if the company has sufficient data to make meaningful predictions and if the data quality of that data as well as the prediction accuracy of the used algorithms are good enough to derive meaningful recommendations.
The example illustrates, that:
The data use case innovation and development process applied by data professionals, can be seamlessly integrated into the generic strategy design process of the Playing to Win framework.
Note that by doing so, business experts and data experts work together closely to design and probe new strategic possibilities, which addresses a common problem in organizations: the existing gaps between business and data teams.
5.4 Concluding Remarks on Business Strategy & Data
To conclude this section, we first link the strategic data demands originating from the business strategy design process back to the data strategy design process described in Section 4.3.
Strategic Data Demands as Inputs for Data Strategy
We saw that a part of Step I of the data strategy design process is to determine the strategic data demands of the organization, which then become the input in form of requirements for building a data function.
From this one may conclude that:
A solid design of business, aggregation and functional strategies is a necessary condition for success with data, analytics and AI in an organization.
And this is where in my opinion the root cause of why organizations fail with data & AI lies [5]: With solid strategy design becoming a lost art [1v], many organizations lack a sound strategic architecture and strategy design process. The lack of strategic competencies has many negative effects for an organization. One such effect is that organizations struggle to use data as an asset.
By leveraging the Playing to Win strategy framework to create a solid strategy architecture, organizations can embed data-related strategic innovation and resulting choices into their business strategy, generating clear strategic demands that inform and guide the design of the data function’s strategy, ensuring alignment and focus.
Data leadership
As we concluded that leveraging strategic value from data, analytics and AI is the task of business strategy design, it is clear that the responsibility lies ultimately with the respective strategy owners, i.e. the corresponding leaders.
This emphasizes once more that leaders of all kinds and levels need to embrace a data culture, ensuring they understand, are willing to, are skilled to, and are required by their superiors to exploit possibilities to use data and AI as strategic levers. They need to become so called ‘data explorers’ as defined in [2].
Leaders of all kinds need to become data explorers
Data, analytics and AI are therefore a score skill in a digital world. That does not solely apply to data professionals such as data scientist or data engineers, but is also true for business leaders.
The ROI question for programs to become data-driven
Transformations of any kind are not an end in themselves. A transformation program to become data-driven should always arise from business strategy. Such programs are the plans and initiatives designed to activate the strategy, ensuring the realization of the organization’s Winning Aspiration, Where to Play, and How to Win choices.
When it comes to committing resources to these transformation programs, I often encounter executives and leaders raising questions about the return on investment (ROI) — an understandable concern. However, the evaluation of anticipated costs should occur earlier during the business strategy design process, not during data strategy design or even at the implementation stage.
Ideally, the need for specific capabilities or systems should naturally emerge from a well-defined strategy. When ROI calculations dominate the discussion, it may indicate that the leadership perceives the proposed investments as unaligned with immediate strategic priorities.
During the business strategy design process, the design team should ask, “What would have to be true regarding the cost of building the required capabilities and systems?” If uncertainty exists about the costs, this critical condition becomes a barrier, prompting the need for testing. Such tests must evaluate both the strategic benefit and the associated costs, ensuring that data investments are assessed within the broader context of organizational priorities.
This again highlights the importance of involving data teams in the business strategy design process and demonstrates that strategy and execution are not separate phases but instead form a seamless transition to activation as soon as capabilities and systems are designed.
The next section provides a detailed walk-through of the developed framework, applying it to a practical example that builds upon the earlier case of a salty snack company.
