Rethinking Data Leadership: Beyond Dashboards and Towards True Business Impact

In today's data-driven world, a startling 70% of organizations are struggling to unlock the value of their data and innovate effectively (Digitalisation World). Despite massive investments in data infrastructure and analytics tools, many companies view data as a technology problem rather than a management problem (Harvard Business Review). This misalignment is at the heart of why so many data initiatives fail to deliver tangible business results. It's time to rethink what it means to be truly data-driven and how data leadership can drive real business impact. Before we start, I want to share my personal beliefs and principles that have shaped my approach to data leadership. These insights have been forged through years of experience, particularly during my time at Idealo and Zalando, as well as through numerous interactions with C-level executives of smaller companies as a consultant at Tale About Data Core Beliefs and Principles Data as a Profit Driver: I firmly believe that data should not be viewed merely as a cost center line item. Instead, it should be recognized as a powerful profit driver. Every dataset, KPI, and AI/ML model should have a clear revenue line, either through cost savings or revenue generation. Focus on ROI and Utilization: In the face of emerging technologies like AI, which often come with significant costs, I advocate for a laser-sharp focus on ROI and utilization. It's crucial to distinguish between technologies that genuinely add value and those that are merely trendy but ineffective. Pragmatic and Revenue-Driven Approach: I'm a strong proponent of maintaining a focus on SMART KPIs and staying pragmatic in data strategy. Every data initiative should have a clear return on investment (ROI) and tangible business impact. Data Quality is Paramount: The effectiveness of emerging technologies, particularly AI and ML models, is directly tied to the quality of data. Organizations must prioritize data quality to ensure the success of their data initiatives. Balancing Data Utility and Privacy: While I believe in the power of data, I also recognize the importance of user preferences and privacy. It's crucial to find the right balance between leveraging data for business improvements and respecting the user, and no, not every event must be of legitimate interest. Alignment with Business Strategy: Data strategy and initiatives need to be a direct reflection of the company's vision, mission, and goals - both short and long-term. I always work backward from what the company needs, determining what data will help achieve these goals. Fostering a True Data-Driven Culture: It's not enough for companies to claim they're data-driven. They need to demonstrate how they're using data to generate profit, not just noise. This involves asking critical questions about data utilization and its impact on costs, revenue, and user actions. Lessons From Experience My time as a data strategist exposed me to many misconceptions about data utilization in large organizations. I've witnessed how good plans can go to waste and how data initiatives can create a lot of activity without moving the needle in the right direction. As a consultant, my interactions with C-level executives of smaller companies revealed a common pitfall: the tendency to view data as a tool to claim "data-driven" status without truly understanding how to generate profit from it. Many would focus on metrics like installs or user growth without connecting these to tangible business outcomes. While many organizations are talking about Data ROI, few have cracked the code for several reasons: Difficulty in defining what constitutes a data product Challenges in treating data products as assets Uncertainty about ownership of revenue and cost streams Fear of Missing Out (FOMO) on data and KPIs These challenges underscore the need for a more structured approach to data leadership and ROI calculation. Approach to Implementation While implementing these principles on a large scale is relatively new to me, I believe in being prepared and adaptable. Here's how I approach it: The 5 W's of Effective KPIs: I use a system I call the 5 W's (Why, What, Where, When, Who) to ensure our KPIs are purposeful and actionable. Continuous Learning: I stay current with emerging trends by maintaining a newsletter where I summarize podcasts, news articles, and blog posts. I also speak at conferences and meet with different vendors to learn about their products. Open to Change, but Firm on Principles: While I'm always open to learning that I might be wrong, I believe life is too short to work for companies unwilling to break out of ineffective cycles. I'm wary of empty promises about data initiatives that fail to materialize due to a lack of focus or competence. The Critical Metrics: Data ROI and Utilization To guide your data team's success and measure its impact on the business, two crucial metrics should be at the forefront of every data initiative: Data ROI (Return on Investment) Definition: The financial return generated from data-driven initiatives relative to the investment in data collection, storage, and analysis. Calculation: (Value generated from data initiatives - Cost of data initiatives) / Cost of data initiatives Key considerations: Evaluate the effectiveness of data products (AI models, dashboards, etc.) Assess financial returns in terms of profit maximization or cost savings Continuously optimize data operations to improve ROI Data Utilization Definition: The percentage of collected and stored data actively used for meaningful purposes. Calculation: (Amount of data actively used / Total amount of stored data) × 100 Key considerations: Identify data adding value vs. merely occupying storage Regularly clean and aggregate or delete unused data Explore untapped opportunities in existing data Looking to the Future As we move forward, I see several key challenges and opportunities: Cost Management: Emerging technologies, particularly in AI, will come with significant costs. This reinforces the need for a strong focus on ROI and utilization. Tool Proliferation: While many companies have standardized visualization tools, AI is seeing employees bring in their preferred tools. This creates potential risks for data security and governance. Balancing Innovation and Practicality: It will be crucial for organizations to distinguish between genuinely useful AI applications and those that are merely gimmicks. The key will be evaluating how these tools help users generate more revenue or improve efficiency. In conclusion, my data leadership philosophy is centered on viewing data as a crucial asset with clear, measurable value. By emphasizing ROI, aligning with business strategy, and fostering a truly data-driven culture, I aim to help organizations leverage their data for tangible business success. This approach, combined with a commitment to continuous learning and adaptation, is how I believe we can drive significant organizational change and innovation in the rapidly evolving data landscape. As we've explored, the journey to effective data leadership is complex and filled with challenges. But it's a journey worth taking. I want to hear from you: If you're struggling with data initiatives, what specific roadblocks are you facing? For those who have achieved success, what strategies have worked for you? Share your experiences in the comments below. Let's build a community of practice around effective data leadership and drive real business impact together. Your insights could be the key to unlocking data's true potential in organizations worldwide. In today's data-driven world, a startling 70% of organizations are struggling to unlock the value of their data and innovate effectively (Digitalisation World). Despite massive investments in data infrastructure and analytics tools, many companies view data as a technology problem rather than a management problem (Harvard Business Review). This misalignment is at the heart of why so many data initiatives fail to deliver tangible business results. It's time to rethink what it means to be truly data-driven and how data leadership can drive real business impact. Before we start, I want to share my personal beliefs and principles that have shaped my approach to data leadership. These insights have been forged through years of experience, particularly during my time at Idealo and Zalando, as well as through numerous interactions with C-level executives of smaller companies as a consultant at Tale About Data Core Beliefs and Principles Data as a Profit Driver: I firmly believe that data should not be viewed merely as a cost center line item. Instead, it should be recognized as a powerful profit driver. Every dataset, KPI, and AI/ML model should have a clear revenue line, either through cost savings or revenue generation. Focus on ROI and Utilization: In the face of emerging technologies like AI, which often come with significant costs, I advocate for a laser-sharp focus on ROI and utilization. It's crucial to distinguish between technologies that genuinely add value and those that are merely trendy but ineffective. Pragmatic and Revenue-Driven Approach: I'm a strong proponent of maintaining a focus on SMART KPIs and staying pragmatic in data strategy. Every data initiative should have a clear return on investment (ROI) and tangible business impact. Data Quality is Paramount: The effectiveness of emerging technologies, particularly AI and ML models, is directly tied to the quality of data. Organizations must prioritize data quality to ensure the success of their data initiatives. Balancing Data Utility and Privacy: While I believe in the power of data, I also recognize the importance of user preferences and privacy. It's crucial to find the right balance between leveraging data for business improvements and respecting the user, and no, not every event must be of legitimate interest. Alignment with Business Strategy: Data strategy and initiatives need to be a direct reflection of the company's vision, mission, and goals - both short and long-term. I always work backward from what the company needs, determining what data will help achieve these goals. Fostering a True Data-Driven Culture: It's not enough for companies to claim they're data-driven. They need to demonstrate how they're using data to generate profit, not just noise. This involves asking critical questions about data utilization and its impact on costs, revenue, and user actions. Data as a Profit Driver: I firmly believe that data should not be viewed merely as a cost center line item. Instead, it should be recognized as a powerful profit driver. Every dataset, KPI, and AI/ML model should have a clear revenue line, either through cost savings or revenue generation. Data as a Profit Driver : I firmly believe that data should not be viewed merely as a cost center line item. Instead, it should be recognized as a powerful profit driver. Every dataset, KPI, and AI/ML model should have a clear revenue line, either through cost savings or revenue generation. Data as a Profit Driver Focus on ROI and Utilization: In the face of emerging technologies like AI, which often come with significant costs, I advocate for a laser-sharp focus on ROI and utilization. It's crucial to distinguish between technologies that genuinely add value and those that are merely trendy but ineffective. Focus on ROI and Utilization : In the face of emerging technologies like AI, which often come with significant costs, I advocate for a laser-sharp focus on ROI and utilization. It's crucial to distinguish between technologies that genuinely add value and those that are merely trendy but ineffective. Focus on ROI and Utilization Pragmatic and Revenue-Driven Approach: I'm a strong proponent of maintaining a focus on SMART KPIs and staying pragmatic in data strategy. Every data initiative should have a clear return on investment (ROI) and tangible business impact. Pragmatic and Revenue-Driven Approach : I'm a strong proponent of maintaining a focus on SMART KPIs and staying pragmatic in data strategy. Every data initiative should have a clear return on investment (ROI) and tangible business impact. Pragmatic and Revenue-Driven Approach Data Quality is Paramount: The effectiveness of emerging technologies, particularly AI and ML models, is directly tied to the quality of data. Organizations must prioritize data quality to ensure the success of their data initiatives. Data Quality is Paramount : The effectiveness of emerging technologies, particularly AI and ML models, is directly tied to the quality of data. Organizations must prioritize data quality to ensure the success of their data initiatives. Data Quality is Paramount Balancing Data Utility and Privacy: While I believe in the power of data, I also recognize the importance of user preferences and privacy. It's crucial to find the right balance between leveraging data for business improvements and respecting the user, and no, not every event must be of legitimate interest. Balancing Data Utility and Privacy : While I believe in the power of data, I also recognize the importance of user preferences and privacy. It's crucial to find the right balance between leveraging data for business improvements and respecting the user, and no, not every event must be of legitimate interest. Balancing Data Utility and Privacy Alignment with Business Strategy: Data strategy and initiatives need to be a direct reflection of the company's vision, mission, and goals - both short and long-term. I always work backward from what the company needs, determining what data will help achieve these goals. Alignment with Business Strategy : Data strategy and initiatives need to be a direct reflection of the company's vision, mission, and goals - both short and long-term. I always work backward from what the company needs, determining what data will help achieve these goals. Alignment with Business Strategy Fostering a True Data-Driven Culture: It's not enough for companies to claim they're data-driven. They need to demonstrate how they're using data to generate profit, not just noise. This involves asking critical questions about data utilization and its impact on costs, revenue, and user actions. Fostering a True Data-Driven Culture : It's not enough for companies to claim they're data-driven. They need to demonstrate how they're using data to generate profit, not just noise. This involves asking critical questions about data utilization and its impact on costs, revenue, and user actions. Fostering a True Data-Driven Culture Lessons From Experience My time as a data strategist exposed me to many misconceptions about data utilization in large organizations. I've witnessed how good plans can go to waste and how data initiatives can create a lot of activity without moving the needle in the right direction. As a consultant, my interactions with C-level executives of smaller companies revealed a common pitfall: the tendency to view data as a tool to claim "data-driven" status without truly understanding how to generate profit from it. Many would focus on metrics like installs or user growth without connecting these to tangible business outcomes. While many organizations are talking about Data ROI, few have cracked the code for several reasons: Difficulty in defining what constitutes a data product Challenges in treating data products as assets Uncertainty about ownership of revenue and cost streams Fear of Missing Out (FOMO) on data and KPIs Difficulty in defining what constitutes a data product Challenges in treating data products as assets Uncertainty about ownership of revenue and cost streams Fear of Missing Out (FOMO) on data and KPIs These challenges underscore the need for a more structured approach to data leadership and ROI calculation. Approach to Implementation While implementing these principles on a large scale is relatively new to me, I believe in being prepared and adaptable. Here's how I approach it: The 5 W's of Effective KPIs: I use a system I call the 5 W's (Why, What, Where, When, Who) to ensure our KPIs are purposeful and actionable. Continuous Learning: I stay current with emerging trends by maintaining a newsletter where I summarize podcasts, news articles, and blog posts. I also speak at conferences and meet with different vendors to learn about their products. Open to Change, but Firm on Principles: While I'm always open to learning that I might be wrong, I believe life is too short to work for companies unwilling to break out of ineffective cycles. I'm wary of empty promises about data initiatives that fail to materialize due to a lack of focus or competence. The 5 W's of Effective KPIs: I use a system I call the 5 W's (Why, What, Where, When, Who) to ensure our KPIs are purposeful and actionable. The 5 W's of Effective KPIs : I use a system I call the 5 W's (Why, What, Where, When, Who) to ensure our KPIs are purposeful and actionable. The 5 W's of Effective KPIs Continuous Learning: I stay current with emerging trends by maintaining a newsletter where I summarize podcasts, news articles, and blog posts. I also speak at conferences and meet with different vendors to learn about their products. Continuous Learning : I stay current with emerging trends by maintaining a newsletter where I summarize podcasts, news articles, and blog posts. I also speak at conferences and meet with different vendors to learn about their products. Continuous Learning Open to Change, but Firm on Principles: While I'm always open to learning that I might be wrong, I believe life is too short to work for companies unwilling to break out of ineffective cycles. I'm wary of empty promises about data initiatives that fail to materialize due to a lack of focus or competence. Open to Change, but Firm on Principles : While I'm always open to learning that I might be wrong, I believe life is too short to work for companies unwilling to break out of ineffective cycles. I'm wary of empty promises about data initiatives that fail to materialize due to a lack of focus or competence. Open to Change, but Firm on Principles The Critical Metrics: Data ROI and Utilization To guide your data team's success and measure its impact on the business, two crucial metrics should be at the forefront of every data initiative: Data ROI (Return on Investment) Definition: The financial return generated from data-driven initiatives relative to the investment in data collection, storage, and analysis. Calculation: (Value generated from data initiatives - Cost of data initiatives) / Cost of data initiatives Key considerations: Evaluate the effectiveness of data products (AI models, dashboards, etc.) Assess financial returns in terms of profit maximization or cost savings Continuously optimize data operations to improve ROI Data Utilization Definition: The percentage of collected and stored data actively used for meaningful purposes. Calculation: (Amount of data actively used / Total amount of stored data) × 100 Key considerations: Identify data adding value vs. merely occupying storage Regularly clean and aggregate or delete unused data Explore untapped opportunities in existing data Data ROI (Return on Investment) Definition: The financial return generated from data-driven initiatives relative to the investment in data collection, storage, and analysis. Calculation: (Value generated from data initiatives - Cost of data initiatives) / Cost of data initiatives Key considerations: Evaluate the effectiveness of data products (AI models, dashboards, etc.) Assess financial returns in terms of profit maximization or cost savings Continuously optimize data operations to improve ROI Data ROI (Return on Investment) Evaluate the effectiveness of data products (AI models, dashboards, etc.) Assess financial returns in terms of profit maximization or cost savings Continuously optimize data operations to improve ROI Evaluate the effectiveness of data products (AI models, dashboards, etc.) Evaluate the effectiveness of data products (AI models, dashboards, etc.) Assess financial returns in terms of profit maximization or cost savings Assess financial returns in terms of profit maximization or cost savings Continuously optimize data operations to improve ROI Continuously optimize data operations to improve ROI Data Utilization Definition: The percentage of collected and stored data actively used for meaningful purposes. Calculation: (Amount of data actively used / Total amount of stored data) × 100 Key considerations: Identify data adding value vs. merely occupying storage Regularly clean and aggregate or delete unused data Explore untapped opportunities in existing data Data Utilization Identify data adding value vs. merely occupying storage Regularly clean and aggregate or delete unused data Explore untapped opportunities in existing data Identify data adding value vs. merely occupying storage Regularly clean and aggregate or delete unused data Explore untapped opportunities in existing data Looking to the Future As we move forward, I see several key challenges and opportunities: Cost Management: Emerging technologies, particularly in AI, will come with significant costs. This reinforces the need for a strong focus on ROI and utilization. Tool Proliferation: While many companies have standardized visualization tools, AI is seeing employees bring in their preferred tools. This creates potential risks for data security and governance. Balancing Innovation and Practicality: It will be crucial for organizations to distinguish between genuinely useful AI applications and those that are merely gimmicks. The key will be evaluating how these tools help users generate more revenue or improve efficiency. Cost Management: Emerging technologies, particularly in AI, will come with significant costs. This reinforces the need for a strong focus on ROI and utilization. Cost Management : Emerging technologies, particularly in AI, will come with significant costs. This reinforces the need for a strong focus on ROI and utilization. Cost Management Tool Proliferation: While many companies have standardized visualization tools, AI is seeing employees bring in their preferred tools. This creates potential risks for data security and governance. Tool Proliferation : While many companies have standardized visualization tools, AI is seeing employees bring in their preferred tools. This creates potential risks for data security and governance. Tool Proliferation Balancing Innovation and Practicality: It will be crucial for organizations to distinguish between genuinely useful AI applications and those that are merely gimmicks. The key will be evaluating how these tools help users generate more revenue or improve efficiency. Balancing Innovation and Practicality : It will be crucial for organizations to distinguish between genuinely useful AI applications and those that are merely gimmicks. The key will be evaluating how these tools help users generate more revenue or improve efficiency. Balancing Innovation and Practicality In conclusion, my data leadership philosophy is centered on viewing data as a crucial asset with clear, measurable value. By emphasizing ROI, aligning with business strategy, and fostering a truly data-driven culture, I aim to help organizations leverage their data for tangible business success. This approach, combined with a commitment to continuous learning and adaptation, is how I believe we can drive significant organizational change and innovation in the rapidly evolving data landscape. As we've explored, the journey to effective data leadership is complex and filled with challenges. But it's a journey worth taking. I want to hear from you: If you're struggling with data initiatives, what specific roadblocks are you facing? For those who have achieved success, what strategies have worked for you? If you're struggling with data initiatives, what specific roadblocks are you facing? For those who have achieved success, what strategies have worked for you? Share your experiences in the comments below. Let's build a community of practice around effective data leadership and drive real business impact together. Your insights could be the key to unlocking data's true potential in organizations worldwide.