How Zen Principles Can Revolutionize Your Data Strategy

In many companies, even the best data strategies quickly crumble under pressure. Why do the right tools and talent fail to stop data chaos? February 2022. I walked into a mobile gaming company's virtual conference room and prepared to present their data strategy after three days of intensive preparation. Before I could even say hello, the CEO enthusiastically waved a document at me: "We've worked on a list of data initiatives we want to accomplish this year – inspired by you!" The room buzzed with ambition. The list was impressive: automated campaign creation across Facebook and Google, 30-day revenue forecasting for finance, self-service funnel analysis in Looker, and a comprehensive game performance dashboard for the CEO. We set the strategy, and though not everyone seemed completely convinced, they committed to the plan. At least for now. Fast forward to November 2022. I'm back in the same room for a strategy review, and my heart sinks. Out of 23 defined KPIs, only 6 made it to the CEO's dashboard. The rest of the initiatives? Buried under an avalanche of competing priorities and infrastructure limitations. What went wrong? The signs were classic: Stakeholders who disagreed with the plan started creating "urgent" tickets for their initiatives Infrastructure buckled under growing session volumes and processing demands The team retreated into firefighting mode, with every ticket marked "high priority" Basic infrastructure needs were overlooked in favor of flashy features But this wasn't an isolated case. Across organizations, I kept seeing the same pattern: ambitious strategies crumbling under the weight of reality. Something had to change. And fast. This wasn't just my story. Across every organization I'd worked with, I saw the same pattern: Ambitious data strategies crumbling under daily pressures Teams drowning in technical debt while chasing perfection Data engineers working overtime yet falling behind Costs spiraled while impact remained elusive The Awakening: A Plate of Hummus and a Basketball Coach After writing "Data is Like a Plate of Hummus," I thought I had cracked the code of data strategy (spoiler: keep it simple, keep it pragmatic; just like a hummus dish). But watching our carefully crafted plans repeatedly crumble, I realized I was missing something fundamental. That's when Phil Jackson's words hit me: "But trying to eliminate anger never works. The more you try to suppress it, the more likely it is to erupt later in a more virulent form. A better approach is to become as intimate as possible with how anger works on your mind and body so that you can transform its underlying energy into something productive." Replace "anger" with "data chaos," and Jackson had perfectly diagnosed our problem. We weren't failing because our strategies were wrong – we were failing because we were fighting against the inherent nature of data ecosystems. The Tale of Two Systems: When Theory Met Reality This insight came to life at Idealo, where we faced a critical decision: System A: The Template Tangle Our ingestion process required manual template maintenance Teams complained about workflow inefficiencies Data quality was inconsistent But the system was stable and understood System B: The Aging Engine Our processing engine was reaching end-of-life Costs were mounting Scalability was becoming impossible But upgrading promised 50% cost savings Traditional strategy frameworks pointed to fixing the ingestion process – we were a platform team, and there was no place for templates in our portfolio, it affected more teams and had clearer ROI. But something felt off about this "logical" choice. While the data chaos was real, I soon realized that the problem wasn't just about technical debt or stakeholder misalignment. It was about our approach to the data ecosystem itself. Finding Wisdom in Imperfection This insight led me to look for wisdom in unexpected places. Three Zen concepts shaped how I approached data strategy moving forward. Wabi-Sabi (侘寂): Finding beauty in imperfection Applied to data: Accepting that no system will ever be perfect Business Application: In data strategy, this means accepting that no system will be perfect and understanding that continual improvement is more valuable than chasing perfection. Think of it as iterating on data systems to address flaws rather than trying to create the "perfect" model that doesn’t exist. Real example: At Zalando, our forecasting model started at 75% accuracy. Instead of delaying the launch, we embraced imperfection and improved iteratively. The Middle Way (中道): In systems: Balancing immediate needs with long-term vision. Business Application: In data strategy, this translates to balancing short-term KPIs (like quick wins) with long-term goals (like a sustainable data culture). Don’t over-invest in quick fixes without building a strong foundation for the future. Real example: At Idealo, we chose to upgrade the processing engine, accepting template inefficiencies while solving critical stability issues. Reading the Air (空気を読む): In data: Creating self-explanatory systems and visualizations. Implementation: Our dashboards evolved to tell stories without lengthy explanations. Birth of the Vision Board: Where Philosophy Meets Practice Rather than trying to eliminate chaos, I learned to navigate it. The result was a framework for clarity, where each initiative, KPI, and principle could be evaluated for its true value – not just in terms of ROI but in its place within the broader data ecosystem. These experiences illuminated three crucial insights that became the foundation of the Vision Board: Present Reality Matters - This is the basic step we all do before setting the data strategy Document current capabilities honestly Quantify costs of data inefficiencies Understanding user needs deeply Current State: User Needs, Usage Patterns, Pain Points, Cost Analysis Future Vision Needs Balance - How our data ecosystem will look three years from now? What capabilities will we need to have to support the organization? Set ambitious but achievable goals Focus on capabilities, not tools Plan for evolution, not perfection 3-Year Horizon: Capabilities, Architecture, Integration, Scalability Success Requires Guidance - How do we evaluate our progress, how can we ensure we focus on the right things, and what guides us moving forward? Define clear metrics (like Data ROI) Establish core principles Create change management strategies KPIs & Principles: Data ROI, Utilization, User Adoption, Cost per Query While the Vision Board is a powerful tool for clarifying data priorities, it’s not about achieving perfection—it’s about navigating the complexities of data work with a clear sense of purpose. The real value lies in simplifying the decision-making process, not in making everything crystal clear right away. What I’ve learned over the years is that building a data ecosystem isn’t about eliminating all chaos or confusion. It's about creating a space where you can see what matters most, why it matters, and what it costs or benefits. The Vision Board is just a start—it's one framework to give clarity to your work, but it’s also a constant work in progress. Embrace the learning curve. We don’t need everything figured out upfront. We just need to start the journey in the right direction. If you’re curious about the full details of how I apply the Vision Board, I’ve shared the process in-depth in my Substack publication Cooking Data. There, I go into the tools and tactics that have worked best for me, with room for experimentation and improvement. But even without diving into the full system, remember: it’s about progress, not perfection. The goal is to focus on clarity and iteration over time. Stakeholder Alignment and ROI Evaluation The success of a data strategy is often judged by its return on investment (ROI)—but this ROI isn’t just about revenue or savings. It’s about aligning with the broader goals of the business, ensuring that every data initiative supports what truly matters to stakeholders. When we use the Vision Board, we can evaluate initiatives through clear KPIs that reflect both short-term and long-term goals. These KPIs act as the guiding metrics to prioritize initiatives that not only create value today but also have the potential to scale. This way, we avoid getting lost in the weeds of tactical problems and stay focused on the big picture. For stakeholders, the Vision Board helps demystify the process. By setting clear principles and evaluating ROI from a multi-dimensional perspective, we can show how each initiative contributes to both immediate impact and long-term sustainability. The result is a data strategy that speaks their language, fosters informed decisions, and aligns with business needs—not just technical goals. Clarity on Work and Enjoying the Journey It’s easy to focus on the end result, especially when we’re working on data initiatives that seem like they’ll be game-changers. But data strategy is a journey—one that unfolds with every decision and every iteration. The Vision Board isn’t just a destination; it’s a tool that allows us to enjoy the journey of building and refining. When we embrace the process, we find that the real value doesn’t come from having all the answers upfront. It comes from adjusting, learning, and refining along the way. The Vision Board helps map the path, but it’s the continual refinement that builds true resilience and alignment within teams. It reminds us to celebrate the small wins, reflect on the lessons learned, and find joy in continuous growth. And let’s not forget: working on your data ecosystem isn’t just about hitting business goals. It’s about building a culture of learning and empowering your team. By embracing imperfections and adapting to challenges, you cultivate a mindset that’s more resilient to setbacks and more capable of handling the ever-changing landscape of data. Invite Feedback As I continue to refine and evolve the Vision Board, I’m eager to hear your thoughts and experiences. The framework is still evolving—it’s meant to be a starting point for dialogue, not a final solution. Every organization faces its unique challenges, and I believe that sharing experiences and insights will make us all better at navigating this complex landscape. Reflect on how the Vision Board could shape your organization’s data strategy. Share your thoughts below or reach out to discuss how we can evolve the process together. What could be improved? If you're interested in exploring this further or have ideas for enhancing the system, I encourage you to reach out. Let’s make data strategy a more collaborative, adaptable, and enjoyable journey for everyone involved. Interested in learning how the Vision Board could work for your team? Join the community discussion on Cooking Data, where we dive deeper into real-world applications and challenges. In many companies, even the best data strategies quickly crumble under pressure. Why do the right tools and talent fail to stop data chaos? February 2022. I walked into a mobile gaming company's virtual conference room and prepared to present their data strategy after three days of intensive preparation. Before I could even say hello, the CEO enthusiastically waved a document at me: "We've worked on a list of data initiatives we want to accomplish this year – inspired by you!" The room buzzed with ambition. The list was impressive: automated campaign creation across Facebook and Google, 30-day revenue forecasting for finance, self-service funnel analysis in Looker, and a comprehensive game performance dashboard for the CEO. We set the strategy, and though not everyone seemed completely convinced, they committed to the plan. At least for now. Fast forward to November 2022. I'm back in the same room for a strategy review, and my heart sinks. Out of 23 defined KPIs, only 6 made it to the CEO's dashboard. The rest of the initiatives? Buried under an avalanche of competing priorities and infrastructure limitations. What went wrong? The signs were classic: Stakeholders who disagreed with the plan started creating "urgent" tickets for their initiatives Infrastructure buckled under growing session volumes and processing demands The team retreated into firefighting mode, with every ticket marked "high priority" Basic infrastructure needs were overlooked in favor of flashy features Stakeholders who disagreed with the plan started creating "urgent" tickets for their initiatives Infrastructure buckled under growing session volumes and processing demands The team retreated into firefighting mode, with every ticket marked "high priority" Basic infrastructure needs were overlooked in favor of flashy features But this wasn't an isolated case. Across organizations, I kept seeing the same pattern: ambitious strategies crumbling under the weight of reality. Something had to change. And fast. This wasn't just my story. Across every organization I'd worked with, I saw the same pattern: Ambitious data strategies crumbling under daily pressures Teams drowning in technical debt while chasing perfection Data engineers working overtime yet falling behind Costs spiraled while impact remained elusive Ambitious data strategies crumbling under daily pressures Teams drowning in technical debt while chasing perfection Data engineers working overtime yet falling behind Costs spiraled while impact remained elusive The Awakening: A Plate of Hummus and a Basketball Coach After writing "Data is Like a Plate of Hummus," I thought I had cracked the code of data strategy (spoiler: keep it simple, keep it pragmatic; just like a hummus dish). But watching our carefully crafted plans repeatedly crumble, I realized I was missing something fundamental. That's when Phil Jackson's words hit me: "But trying to eliminate anger never works. The more you try to suppress it, the more likely it is to erupt later in a more virulent form. A better approach is to become as intimate as possible with how anger works on your mind and body so that you can transform its underlying energy into something productive." "But trying to eliminate anger never works. The more you try to suppress it, the more likely it is to erupt later in a more virulent form. A better approach is to become as intimate as possible with how anger works on your mind and body so that you can transform its underlying energy into something productive." Replace "anger" with "data chaos," and Jackson had perfectly diagnosed our problem. We weren't failing because our strategies were wrong – we were failing because we were fighting against the inherent nature of data ecosystems. The Tale of Two Systems: When Theory Met Reality This insight came to life at Idealo, where we faced a critical decision: System A: The Template Tangle System A: The Template Tangle Our ingestion process required manual template maintenance Teams complained about workflow inefficiencies Data quality was inconsistent But the system was stable and understood Our ingestion process required manual template maintenance Teams complained about workflow inefficiencies Data quality was inconsistent But the system was stable and understood System B: The Aging Engine System B: The Aging Engine Our processing engine was reaching end-of-life Costs were mounting Scalability was becoming impossible But upgrading promised 50% cost savings Our processing engine was reaching end-of-life Costs were mounting Scalability was becoming impossible But upgrading promised 50% cost savings Traditional strategy frameworks pointed to fixing the ingestion process – we were a platform team, and there was no place for templates in our portfolio, it affected more teams and had clearer ROI. But something felt off about this "logical" choice. While the data chaos was real, I soon realized that the problem wasn't just about technical debt or stakeholder misalignment. It was about our approach to the data ecosystem itself. Finding Wisdom in Imperfection This insight led me to look for wisdom in unexpected places. Three Zen concepts shaped how I approached data strategy moving forward. Wabi-Sabi (侘寂): Finding beauty in imperfection Applied to data: Accepting that no system will ever be perfect Business Application: In data strategy, this means accepting that no system will be perfect and understanding that continual improvement is more valuable than chasing perfection. Think of it as iterating on data systems to address flaws rather than trying to create the "perfect" model that doesn’t exist. Real example: At Zalando, our forecasting model started at 75% accuracy. Instead of delaying the launch, we embraced imperfection and improved iteratively. The Middle Way (中道): In systems: Balancing immediate needs with long-term vision. Business Application: In data strategy, this translates to balancing short-term KPIs (like quick wins) with long-term goals (like a sustainable data culture). Don’t over-invest in quick fixes without building a strong foundation for the future. Real example: At Idealo, we chose to upgrade the processing engine, accepting template inefficiencies while solving critical stability issues. Reading the Air (空気を読む): In data: Creating self-explanatory systems and visualizations. Implementation: Our dashboards evolved to tell stories without lengthy explanations. Wabi-Sabi (侘寂) : Finding beauty in imperfection Applied to data: Accepting that no system will ever be perfect Business Application: In data strategy, this means accepting that no system will be perfect and understanding that continual improvement is more valuable than chasing perfection. Think of it as iterating on data systems to address flaws rather than trying to create the "perfect" model that doesn’t exist. Real example: At Zalando, our forecasting model started at 75% accuracy. Instead of delaying the launch, we embraced imperfection and improved iteratively. Wabi-Sabi (侘寂) Applied to data: Accepting that no system will ever be perfect Business Application: In data strategy, this means accepting that no system will be perfect and understanding that continual improvement is more valuable than chasing perfection. Think of it as iterating on data systems to address flaws rather than trying to create the "perfect" model that doesn’t exist. Real example: At Zalando, our forecasting model started at 75% accuracy. Instead of delaying the launch, we embraced imperfection and improved iteratively. Applied to data: Accepting that no system will ever be perfect Business Application: In data strategy, this means accepting that no system will be perfect and understanding that continual improvement is more valuable than chasing perfection. Think of it as iterating on data systems to address flaws rather than trying to create the "perfect" model that doesn’t exist. Applied to data: Accepting that no system will ever be perfect Applied to data: Business Application : In data strategy, this means accepting that no system will be perfect and understanding that continual improvement is more valuable than chasing perfection. Think of it as iterating on data systems to address flaws rather than trying to create the "perfect" model that doesn’t exist. Business Application Real example: At Zalando, our forecasting model started at 75% accuracy. Instead of delaying the launch, we embraced imperfection and improved iteratively. Real example: At Zalando, our forecasting model started at 75% accuracy. Instead of delaying the launch, we embraced imperfection and improved iteratively. Real example: The Middle Way (中道) : In systems: Balancing immediate needs with long-term vision. Business Application: In data strategy, this translates to balancing short-term KPIs (like quick wins) with long-term goals (like a sustainable data culture). Don’t over-invest in quick fixes without building a strong foundation for the future. Real example: At Idealo, we chose to upgrade the processing engine, accepting template inefficiencies while solving critical stability issues. The Middle Way (中道) In systems: Balancing immediate needs with long-term vision. Business Application: In data strategy, this translates to balancing short-term KPIs (like quick wins) with long-term goals (like a sustainable data culture). Don’t over-invest in quick fixes without building a strong foundation for the future. Real example: At Idealo, we chose to upgrade the processing engine, accepting template inefficiencies while solving critical stability issues. In systems: Balancing immediate needs with long-term vision. Business Application: In data strategy, this translates to balancing short-term KPIs (like quick wins) with long-term goals (like a sustainable data culture). Don’t over-invest in quick fixes without building a strong foundation for the future. In systems: Balancing immediate needs with long-term vision. In systems: Business Application : In data strategy, this translates to balancing short-term KPIs (like quick wins) with long-term goals (like a sustainable data culture). Don’t over-invest in quick fixes without building a strong foundation for the future. Business Application Real example: At Idealo, we chose to upgrade the processing engine, accepting template inefficiencies while solving critical stability issues. Real example: At Idealo, we chose to upgrade the processing engine, accepting template inefficiencies while solving critical stability issues. Real example: Reading the Air (空気を読む) : In data: Creating self-explanatory systems and visualizations. Implementation: Our dashboards evolved to tell stories without lengthy explanations. Reading the Air (空気を読む) In data: Creating self-explanatory systems and visualizations. Implementation: Our dashboards evolved to tell stories without lengthy explanations. In data: Creating self-explanatory systems and visualizations. Implementation: Our dashboards evolved to tell stories without lengthy explanations. Birth of the Vision Board: Where Philosophy Meets Practice Rather than trying to eliminate chaos, I learned to navigate it. The result was a framework for clarity, where each initiative, KPI, and principle could be evaluated for its true value – not just in terms of ROI but in its place within the broader data ecosystem. These experiences illuminated three crucial insights that became the foundation of the Vision Board: Present Reality Matters - This is the basic step we all do before setting the data strategy Document current capabilities honestly Quantify costs of data inefficiencies Understanding user needs deeply Current State: User Needs, Usage Patterns, Pain Points, Cost Analysis Future Vision Needs Balance - How our data ecosystem will look three years from now? What capabilities will we need to have to support the organization? Set ambitious but achievable goals Focus on capabilities, not tools Plan for evolution, not perfection 3-Year Horizon: Capabilities, Architecture, Integration, Scalability Success Requires Guidance - How do we evaluate our progress, how can we ensure we focus on the right things, and what guides us moving forward? Define clear metrics (like Data ROI) Establish core principles Create change management strategies KPIs & Principles: Data ROI, Utilization, User Adoption, Cost per Query Present Reality Matters - This is the basic step we all do before setting the data strategy Document current capabilities honestly Quantify costs of data inefficiencies Understanding user needs deeply Current State: User Needs, Usage Patterns, Pain Points, Cost Analysis Present Reality Matters - Document current capabilities honestly Quantify costs of data inefficiencies Understanding user needs deeply Current State: User Needs, Usage Patterns, Pain Points, Cost Analysis Document current capabilities honestly Document current capabilities honestly Quantify costs of data inefficiencies Quantify costs of data inefficiencies Understanding user needs deeply Understanding user needs deeply Current State: User Needs, Usage Patterns, Pain Points, Cost Analysis Current State: User Needs, Usage Patterns, Pain Points, Cost Analysis Current State: Future Vision Needs Balance - How our data ecosystem will look three years from now? What capabilities will we need to have to support the organization? Set ambitious but achievable goals Focus on capabilities, not tools Plan for evolution, not perfection 3-Year Horizon: Capabilities, Architecture, Integration, Scalability Future Vision Needs Balance Set ambitious but achievable goals Focus on capabilities, not tools Plan for evolution, not perfection 3-Year Horizon: Capabilities, Architecture, Integration, Scalability Set ambitious but achievable goals Set ambitious but achievable goals Focus on capabilities, not tools Focus on capabilities, not tools Plan for evolution, not perfection Plan for evolution, not perfection 3-Year Horizon: Capabilities, Architecture, Integration, Scalability 3-Year Horizon: Capabilities, Architecture, Integration, Scalability 3-Year Horizon: Success Requires Guidance - How do we evaluate our progress, how can we ensure we focus on the right things, and what guides us moving forward? Define clear metrics (like Data ROI) Establish core principles Create change management strategies KPIs & Principles: Data ROI, Utilization, User Adoption, Cost per Query Success Requires Guidance Define clear metrics (like Data ROI) Establish core principles Create change management strategies KPIs & Principles: Data ROI, Utilization, User Adoption, Cost per Query Define clear metrics (like Data ROI) Establish core principles Create change management strategies KPIs & Principles: Data ROI, Utilization, User Adoption, Cost per Query KPIs & Principles: While the Vision Board is a powerful tool for clarifying data priorities, it’s not about achieving perfection—it’s about navigating the complexities of data work with a clear sense of purpose. The real value lies in simplifying the decision-making process, not in making everything crystal clear right away. navigating the complexities What I’ve learned over the years is that building a data ecosystem isn’t about eliminating all chaos or confusion. It's about creating a space where you can see what matters most , why it matters, and what it costs or benefits. what matters most The Vision Board is just a start—it's one framework to give clarity to your work, but it’s also a constant work in progress. Embrace the learning curve. We don’t need everything figured out upfront. We just need to start the journey in the right direction. If you’re curious about the full details of how I apply the Vision Board, I’ve shared the process in-depth in my Substack publication Cooking Data . There, I go into the tools and tactics that have worked best for me, with room for experimentation and improvement. But even without diving into the full system, remember: it’s about progress, not perfection. The goal is to focus on clarity and iteration over time. Cooking Data Stakeholder Alignment and ROI Evaluation Stakeholder Alignment and ROI Evaluation The success of a data strategy is often judged by its return on investment (ROI)—but this ROI isn’t just about revenue or savings. It’s about aligning with the broader goals of the business, ensuring that every data initiative supports what truly matters to stakeholders. supports what truly matters When we use the Vision Board, we can evaluate initiatives through clear KPIs that reflect both short-term and long-term goals. These KPIs act as the guiding metrics to prioritize initiatives that not only create value today but also have the potential to scale. This way, we avoid getting lost in the weeds of tactical problems and stay focused on the big picture. clear KPIs guiding metrics For stakeholders, the Vision Board helps demystify the process. By setting clear principles and evaluating ROI from a multi-dimensional perspective, we can show how each initiative contributes to both immediate impact and long-term sustainability . The result is a data strategy that speaks their language, fosters informed decisions, and aligns with business needs —not just technical goals. both immediate impact and long-term sustainability business needs Clarity on Work and Enjoying the Journey Clarity on Work and Enjoying the Journey It’s easy to focus on the end result, especially when we’re working on data initiatives that seem like they’ll be game-changers. But data strategy is a journey— one that unfolds with every decision and every iteration . The Vision Board isn’t just a destination; it’s a tool that allows us to enjoy the journey of building and refining. one that unfolds with every decision and every iteration When we embrace the process, we find that the real value doesn’t come from having all the answers upfront. It comes from adjusting, learning, and refining along the way . The Vision Board helps map the path, but it’s the continual refinement that builds true resilience and alignment within teams. It reminds us to celebrate the small wins, reflect on the lessons learned, and find joy in continuous growth. adjusting, learning, and refining along the way And let’s not forget: working on your data ecosystem isn’t just about hitting business goals. It’s about building a culture of learning and empowering your team. By embracing imperfections and adapting to challenges, you cultivate a mindset that’s more resilient to setbacks and more capable of handling the ever-changing landscape of data. building a culture of learning Invite Feedback Invite Feedback As I continue to refine and evolve the Vision Board, I’m eager to hear your thoughts and experiences. The framework is still evolving— it’s meant to be a starting point for dialogue, not a final solution . Every organization faces its unique challenges, and I believe that sharing experiences and insights will make us all better at navigating this complex landscape. it’s meant to be a starting point for dialogue, not a final solution Reflect on how the Vision Board could shape your organization’s data strategy. Share your thoughts below or reach out to discuss how we can evolve the process together. What could be improved? If you're interested in exploring this further or have ideas for enhancing the system, I encourage you to reach out. Let’s make data strategy a more collaborative, adaptable, and enjoyable journey for everyone involved. collaborative, adaptable, and enjoyable journey Interested in learning how the Vision Board could work for your team? Join the community discussion on Cooking Data, where we dive deeper into real-world applications and challenges.