Strategic Prospective: Recommendations For A Future With Data-Driven Decisions

How does one navigate through the complex web of information, technologies, and organizational structures, while creating a solid foundation for future data-driven decisions and innovations?

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In our final part of the article series on Datentreiber Canvases, we take a closer look on this challenge using the Data Strategy Design Method.

The right information is the fuel for every decision-making process in companies. In our previous articles, we demonstrated how challenges and problems are identified using the 3 Boxes Canvas. We then examined these problems with the Cause-Effect Canvas for their causes. In the final step, this acquired information must be translated into a value for the future data strategy. Now, the next stage is to define clear recommendations for successful implementation. After all, the true value of information becomes apparent only when it can be translated into actionable steps.

The Question Of “How”

On their transformative journey to data-driven decisions and processes in the company, we recall the fictitious company example of Sad HIPPO Company. So far, it has been driven not by data, but by opinions. The metaphorical image of ‘HIPPO’ here represents decision-making based on gut feeling and experience. (Acronym for “Highest Paid Person´s Opinion”) metaphorically represented.

In the first article, the team retrospective focused on using the 3 Boxes Canvas for review and identification of relevant problems. The question of “What happened” was in the foreground.

Our team at Sad HIPPO Company analyzed the found problems with the Cause-Effect Canvas for their underlying causes and described a problem area. Here, the question dealt with the “Why”, the reasons behind the obvious. The crucial next step now lies before us: translating these insights into actionable steps for data-driven decisions.

Plan Data-Driven Decisions From the Causes

We now turn to the question of “How”: How do we transfer the gained insights from the Cause-Effect Canvas into actionable steps. For this, the team, guided by the workshop leader, returns to the filled-out Cause-Effect Canvas from previous meetings. This hangs either as an A0 sheet in its place or is digitally accessible via a Miro Board.

The reason for returning is simple: here we find a detailed mapping of a fault tree: From six different categories, causal chains lead to the core problem, here “Problems in ensuring data quality”.

We looked for causes from different perspectives that are partly and cross-sectionally related to this problem area. We recognize that problem areas are multidimensional. We are now interested in whether commonalities and patterns within these most diverse perspectives can be identified.

This is a good moment for the workshop leader to send the team on a pattern search and to encourage a reorganization on the canvas: causes are now re-categorized, aiming to recognize the cross-sectional commonalities, by sorting related notes into groups, the so-called “clustering”:

In this step, recurring themes such as budget constraints, but also cultural barriers, which underlie the causes of problems, already become apparent. The team and the workshop leader now jointly find new summarizing titles for the emerged clusters. Now it also becomes clear: Clear starting points for improvements lie in the processes, as well as in the organization and leadership. Through the grouping, clear patterns become visible.

In a typical workshop environment for developing a strategic orientation, more than one problem area needs to be worked out. Therefore, we also assume in our fictitious example that the Sad HIPPO Company includes the remaining two problems in their analysis. This happens either as a complete causal analysis on separate canvases, which are later merged, or as in this case, by using the existing clusters on the same canvas: Related but not redundant causes are added to the already recognizable patterns.

By adding further problem fields and their causes, the positive and negative synergies become increasingly clear. After completing the clustering, the 3 Boxes Canvas is now used to take these recognizable patterns as a basis, to consolidate solid recommendations for action heuristically or by using specific methods, such as the SMART criteria.

The task of the workshop leader is now not only to stimulate the participants to recognize levers for finding solutions and to determine approaches for clear recommendations for action, but also to promote their structured presentation on the canvas.

Finalizing The Workshop With The Multifaceted 3 Boxes Canvas

The relationships already recognizable from the existing groupings are now directly transferred to the 3 fields on the new canvas. The fields are titled “Start”, “Continue”, and “Stop”.


Here, the participants write down actions that should be introduced as new initiatives or practices. Given the recognized patterns, they decide that:

  • Implementation of regular cross-functional workshops is necessary to improve communication and mutual understanding between the data and marketing teams, and to break down silo thinking.
  • A program to promote digital literacy is developed to bridge the gap in understanding the importance of data quality and the necessary technological skills, as well as to foster an understanding of the importance of data-driven decisions.
  • A special budget for IT innovations and training is set up to modernize the technological infrastructure and continuously educate employees.


In this area, actions that already have a positive influence and should therefore be maintained or even expanded are noted down:

  • The use of advanced analytical tools and methods for data evaluation will not only be continued but intensified through additional resources and training for employees.
  • The existing approaches to process automation are to be further developed and expanded to more areas to achieve efficiency gains and reduce manual errors.


In this field, participants identify practices that have proven to be obstructive or do not bring the desired benefit:

  • The practice of cutting training first in budget cuts will be discontinued, as it has been recognized that this causes more costs in the long term by solidifying problems with data quality and inefficient use of technology.
  • Allowing operations in isolated departmental silos ends to promote a more coherent, company-wide strategy for data management and use.
  • Outdated technologies and systems will be gradually phased out, instead of continuing to waste resources on their maintenance, paving the way for modern solutions.

The team is satisfied and would like to congratulate themselves already. However, the workshop leader insists on digging deeper, asking more specifically, and not forgetting that overarching insights already exist: Especially in the area of processes and organization as well as leadership, there is enormous potential for improvement, as many causes are rooted here.

In this sense, the team should continue to ask “How” and both generate a variety of ideas and pay particular attention to what might be the core drivers of the identified problem areas. This process is often referred to as a “Guesstimation“: It is both a first estimate and an intuitive evaluation, without claiming to have complete or fact-based knowledge. In subsequent steps, the ideas can then be re-evaluated, for example, based on the SMART criteria.

Define SMART Goals And Ask “How Might We?” Questions

Together, the team now takes a step further and sharpens the already identified ideas and projects through more specific, goal-oriented questions. The workshop leader encourages the team to apply the “How might we?” question method to generate more concrete ideas from the generic statements on the notes. Also, the SMART method (SMART stands for: specific, measurable, accepted, realistic, time-bound) should help to ensure that the planned measures are not just on paper, but cause realistic and effective changes in the organization.

To illustrate this process, the workshop leader introduces three exemplary “How can we?” questions, directly derived from the previously discussed topic areas:

  1. “How might we” improve communication between our departments so that knowledge exchange and collaboration become the norm? This question aims to develop concrete measures to promote information flow and collaboration between different teams, for example, by introducing regular interdisciplinary meetings or creating shared digital workspaces.
  2. “How might we” create an environment where continuous education and development of each individual is encouraged? This is about developing specific strategies on how the company can support a culture of lifelong learning, such as through individual training plans or providing an internal learning portal.
  3. “How might we” design our technological infrastructure to be flexible enough to meet future requirements? This question encourages the team to think beyond the modernization of IT systems and develop a plan for continuously adapting and updating technologies.

The concept behind these questions, which is often applied in Design Thinking processes, is to transform the problem statement into an open, solution-oriented question that stimulates creative thinking and leads to innovative solutions.

Therefore, the team of Sad HIPPO Company can now look back on the progress made with satisfaction and was able to visualize immediately tangible and structured results on the 3 Boxes Canvas.

The workshop leader emphasizes that answering these questions not only found more specific and actionable solutions but also helped to critically question the underlying assumptions and challenges once again. Additionally, the found answers and ideas should always meet the majority of the SMART criteria (specific, measurable, accepted, realistic, time-bound) to present themselves as justifiable options.

Moving Towards a Future of Data-Driven Decisions with Datentreiber

The methods we have demonstrated are two possible ways to translate the insights gained into recommendations for action. This ensures that ideas move beyond the confines of sticky notes and become actionable in corporate reality. This requires a delicate touch by the workshop leader, a solid set of methods like the Data Strategy Design Method from Datentreiber, and also a collective “yes” from the entire team.

In our example, the Sad HIPPO Company has made a significant step towards a data-driven future through structured engagement with past events and the identification of core problems, all the way to the development of SMART goals.

Not only were existing questions answered, but new challenges were also uncovered. These have far-reaching strategic and operational consequences. For the Sad HIPPO Company team, it’s clear: The transformation requires more than just overcoming current obstacles. It’s about a profound realignment, creating an agile and innovative environment. For this, continuous change management, investments in leadership and employee development, strategic resource allocation, agile processes, a resilience-oriented culture, as well as regular measurements and adjustments are possible important milestones towards a future of data-driven decisions.

The Sad HIPPO Company team learned to connect fragmented elements of a problem area, asking the right questions and setting the right direction through the formulation of concrete goals. Discovering new and open questions is just as important a step as setting recommendations for action.

Train. Think. Transform. – For Your AI Strategy of Data-Driven Decisions

Datentreiber is ready as a partner to support companies on this journey. With our proven Data Strategy Design Method and an expert network for various areas of data, AI, and business questions, we accompany you from developing a holistic data strategy to successful implementation towards data-driven decisions.

Our guiding principle: “Train.Think.Transform.” are more than just buzzwords; they form the foundation of our end-to-end solutions.

Train. – stands for training all employees to create a common understanding of data and the foundation for innovative changes.

Think. – challenges every company to tackle challenges and problems and to develop individual solutions towards data-driven decisions. Supported by our proven Data Strategy Design Method with over 18 interconnected canvases, we work with you to develop clear recommendations for action to enable informed decisions in your organization.

Transform. – means successfully implementing the recommendations for action. We can accompany your teams through coaching during the implementation, as well as rely on an expert network to master individual challenges through their use.

Do you want to make data-driven decisions? Book a free, 15-minute consultation with Georg Arens.

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