98% of Data Strategies Fail: Let's Fix It

Picture this: You're Gordon Ramsay, the world-renowned chef, walking into yet another restaurant for your hit show "Kitchen Nightmares." The owner boasts about their state-of-the-art kitchen equipment, the diverse menu, and the talented staff. But as you dig deeper, you find a chaotic kitchen, a confused team, and dishes that are all presentation and no substance. Sound familiar? If you're a C-suite executive in a data-driven company, it might. Just swap out the kitchen for your data infrastructure, the menu for your KPIs, and the staff for your data team. Welcome to the world of data strategy nightmares, where even the most well-equipped organizations can find themselves serving up a mess instead of actionable insights. The Recipe for Disaster: A Real-World Data Nightmare Let me share a story that might hit close to home for many of you. Not too long ago, I was called in to consult for a promising startup we'll call "CezGames." They had all the ingredients for success - a charismatic CEO with a vision, a CTO boasting an impressive tech pedigree, and a team eager to leverage data for growth. They even had a hefty budget for building out their data infrastructure. It seemed like a recipe for success. But much like the restaurants Gordon Ramsay visits, appearances can be deceiving. The CEO, a very technical person, had decided to focus their data strategy workshop on the technology that would be used and how to build the data pipelines. The room was filled with excitement as they mapped out all the data flows and systems. However, there was a crucial ingredient missing from this elaborate recipe: the 'why.' No one stopped to ask why they were tracking this data or why it should be in their dashboards. The result? A beautiful map of data and flows, but no information about why any of it mattered. It was like having a kitchen full of ingredients but no idea what dish you're trying to cook. Common Data Strategy Nightmares: The Spoiled Ingredients Before we dive into fixing this mess, let's identify some common nightmares that can spoil your data strategy from the start: Technology Obsession: Debating Snowflake vs. Databricks before you even know what insights you need. It's like arguing over gas vs. electric stoves when you haven't decided on your menu. Misplaced Ownership: Handing the entire data strategy to the CTO, who may be more comfortable with servers than business KPIs. This is akin to letting your equipment supplier run the restaurant. Lack of Executive Engagement: A CEO who's afraid of data is like a restaurant owner who's never tasted the food. You can't lead what you don't understand. Disconnected KPIs: Tracking vanity metrics that don't indicate the health of your business. It's like judging a restaurant's success by how shiny the cutlery is. Unclear Accountability: When everyone (and no one) is responsible for data quality. This is the data equivalent of "too many cooks spoil the broth." Over-engineering: Building a data infrastructure fit for a tech giant when you're still a small startup. It's like equipping a food truck with a Michelin-star kitchen. Ignoring Data Governance: Treating data like a free-for-all buffet instead of a carefully curated menu. Without proper governance, your data can quickly become a health hazard. Viewing Data as a Cost Center: Failing to see data strategy as a profit driver and growth enabler. This is like seeing your ingredients as just an expense, rather than the foundation of your culinary creations. Now that we've identified these common nightmares, it's time to channel our inner Gordon Ramsay and turn this data disaster into a well-oiled, insight-generating machine. In the following sections, we'll dive into the step-by-step process of fixing your data strategy, ensuring that you're serving up actionable insights instead of a data nightmare. Fixing the Data Strategy: A New Recipe for Success Now, let's roll up our sleeves and fix this data strategy disaster. Here's a step-by-step recipe for success: Start with the CEO's Order (The CEO's Role) Imagine you're opening a new restaurant. Before you hire chefs or buy equipment, you need to decide what kind of food you're serving. Similarly, the CEO needs to clearly articulate what "dishes" (insights) they need to run the business effectively. Action Item: Use the 5 W's framework to define your key performance indicators (KPIs): Why: Understanding the purpose behind each KPI What: Aligning KPIs with specific actions Where: Mapping KPI impact across the organization When: Determining the right frequency for monitoring and action Who: Assigning clear ownership and responsibility For example, instead of just tracking "user engagement," get specific with the WHY: Use this template: "We measure [KPI] because it tells us [insight], to achieve [business objective]." We measure time on site because it tells us how long our users are spending their time on the platform, to achieve our business goals to increase the time spent by users by 15% by the end of this year from 3 minutes and 25 seconds to 3 minutes and 56 seconds. Assign the Head Chefs (Ownership and Accountability) Once you know what dishes you're serving, it's time to assign your head chefs. In the data world, this means finding the right owners for each KPI. Action Item: For each KPI, assign an owner who is: A subject matter expert who understands the business context, for example finance person will know best about the revenue metric why it is correct or not, and how it’s being used. A product manager who can treat the KPI as a "product" to be maintained and improved each KPI becomes a product, yes, it won’t have regular sprints in most cases, but it has a product mentality each time things are changing, and it has a rollout plan, change strategy, and end of life process. Remember, in small companies or startups, resources are often limited. You may not have enough people to run all tasks related to data. This means you need to be very focused and specific on what you need to see and how you get the information you need, especially when product development and revenue activities often take higher priority. Design Your Kitchen (Data Architecture and Technology) Now that you know what you're cooking and who's in charge of each dish, it's time to set up your kitchen. This is where you'll think about data architecture and technology as well as pipelines. Action Item: Before choosing tools, map out your data flow: Identify data sources (your "ingredients") Determine data volumes (how much are you "cooking"?) Plan data transformations (your "cooking processes") Decide on data storage (your "pantry" and "refrigerator") Choose visualization tools (your "plating" and "presentation") Remember, you don't need an industrial-sized freezer if you're only serving fresh, local ingredients. Similarly, don't invest in heavy-duty data processing tools if your data volume doesn't warrant it. Implement Data Governance (Your Kitchen Rules) Every good kitchen has rules to ensure food safety and quality. In the data world, this is your data governance. Action Item: Embed governance into your architecture: Set up data validation and quality checks at each stage of your data pipeline Implement clear data access protocols Create a data dictionary so everyone speaks the same "language" Establish a process for updating and deprecating data sources Start Small, Iterate Often Remember, even the best chefs start with simple dishes before creating complex menus. Begin with a few key KPIs, get them right, and then expand. Gordon Ramsay's kitchen is a symphony of precision. For his Beef Wellington, he begins with a rigorous selection process for the beef, opting for a prime cut with optimal marbling. The pastry, a labor of love, is meticulously layered, ensuring a crisp exterior and flaky interior. But it's the duxelles that truly define his Wellington. A blend of finely chopped mushrooms, shallots, and herbs, it's cooked down to an intense umami-rich paste. Ramsay often mentions the importance of balancing flavors, ensuring that the richness of the beef is complemented by the earthy depth of the duxelles. The final touch is a delicate layer of pâté, providing a luxurious mouthfeel. With each element meticulously prepared, the Wellington is assembled with a surgeon's precision, ready to be transformed in the oven into a culinary masterpiece. Action Item: Choose 3-5 core KPIs to focus on initially. Build out the entire process for these, from data collection to visualization. Get feedback, refine, and only then move on to more complex metrics. The Secret Sauce: Data as a Profit Driver Here's a crucial ingredient many organizations miss: data should be viewed as a profit unit, not a cost center. In most companies, people tend to think about the data strategy and its outcome as a sunk cost. But in reality, having data means that you can drive the business forward. It's your only way to discover trends, understand your users, and find your money drivers and money wasters. Think of data as a plate of hummus. It should be as simple and accessible as a hummus dish, but as complex and value-driving as the layers of flavors in a well-prepared hummus. It's a strategy everyone needs to take care of and understand, especially the CEO! I am a member of a group of self-cooks who have been trying for years to break the secret of some of their top hummus restaurants and how they drive the umami effect into the hummus that makes them so unique The Taste of Success: A Culture of Data-Driven Decision Making When you have a clear data strategy and an understanding of the KPIs they use, the team becomes much more focused. They know how they're being measured, and they can easily evaluate their efforts. They have something to be proud of when it works well or a story to tell when it breaks. Imagine a CMO and their team discussing what the CEO needs to see about their activity. Instead of just listing metrics and KPIs that create no action or feeling by the CEO, they're now able to present insights that drive decisions and show a clear understanding of how they're being measured and evaluated by the executive team. Your Call to Action: From Data Chaos to Data Cuisine If you're a C-suite executive struggling with your data strategy, it's time to step back and reassess. Are you focused on the right metrics? Have you assigned ownership? Is your technology supporting your needs or overwhelming them? Remember, a successful data strategy isn't about having the fanciest tools or the biggest data lake. It's about creating a system that consistently delivers the insights you need to run and grow your business. Ready to turn your data strategy from a messy kitchen into a Michelin-star operation? Let's talk. Reach out to discuss how I can make your organization truly data-driven, focusing on what truly matters for your success. After all, in the world of data, as in cooking, simplicity, quality, and precision trump complexity every time. Bon appétit! Picture this: You're Gordon Ramsay, the world-renowned chef, walking into yet another restaurant for your hit show "Kitchen Nightmares." The owner boasts about their state-of-the-art kitchen equipment, the diverse menu, and the talented staff. But as you dig deeper, you find a chaotic kitchen, a confused team, and dishes that are all presentation and no substance. Sound familiar? If you're a C-suite executive in a data-driven company, it might. Just swap out the kitchen for your data infrastructure, the menu for your KPIs, and the staff for your data team. Welcome to the world of data strategy nightmares, where even the most well-equipped organizations can find themselves serving up a mess instead of actionable insights. The Recipe for Disaster: A Real-World Data Nightmare Let me share a story that might hit close to home for many of you. Not too long ago, I was called in to consult for a promising startup we'll call "CezGames." They had all the ingredients for success - a charismatic CEO with a vision, a CTO boasting an impressive tech pedigree, and a team eager to leverage data for growth. They even had a hefty budget for building out their data infrastructure. It seemed like a recipe for success. But much like the restaurants Gordon Ramsay visits, appearances can be deceiving. The CEO, a very technical person, had decided to focus their data strategy workshop on the technology that would be used and how to build the data pipelines. The room was filled with excitement as they mapped out all the data flows and systems. However, there was a crucial ingredient missing from this elaborate recipe: the 'why.' No one stopped to ask why they were tracking this data or why it should be in their dashboards. The result? A beautiful map of data and flows, but no information about why any of it mattered. It was like having a kitchen full of ingredients but no idea what dish you're trying to cook. Common Data Strategy Nightmares: The Spoiled Ingredients Before we dive into fixing this mess, let's identify some common nightmares that can spoil your data strategy from the start: Technology Obsession: Debating Snowflake vs. Databricks before you even know what insights you need. It's like arguing over gas vs. electric stoves when you haven't decided on your menu. Misplaced Ownership: Handing the entire data strategy to the CTO, who may be more comfortable with servers than business KPIs. This is akin to letting your equipment supplier run the restaurant. Lack of Executive Engagement: A CEO who's afraid of data is like a restaurant owner who's never tasted the food. You can't lead what you don't understand. Disconnected KPIs: Tracking vanity metrics that don't indicate the health of your business. It's like judging a restaurant's success by how shiny the cutlery is. Unclear Accountability: When everyone (and no one) is responsible for data quality. This is the data equivalent of "too many cooks spoil the broth." Over-engineering: Building a data infrastructure fit for a tech giant when you're still a small startup. It's like equipping a food truck with a Michelin-star kitchen. Ignoring Data Governance: Treating data like a free-for-all buffet instead of a carefully curated menu. Without proper governance, your data can quickly become a health hazard. Viewing Data as a Cost Center: Failing to see data strategy as a profit driver and growth enabler. This is like seeing your ingredients as just an expense, rather than the foundation of your culinary creations. Technology Obsession: Debating Snowflake vs. Databricks before you even know what insights you need. It's like arguing over gas vs. electric stoves when you haven't decided on your menu. Technology Obsession : Debating Snowflake vs. Databricks before you even know what insights you need. It's like arguing over gas vs. electric stoves when you haven't decided on your menu. Technology Obsession Misplaced Ownership: Handing the entire data strategy to the CTO, who may be more comfortable with servers than business KPIs. This is akin to letting your equipment supplier run the restaurant. Misplaced Ownership : Handing the entire data strategy to the CTO, who may be more comfortable with servers than business KPIs. This is akin to letting your equipment supplier run the restaurant. Misplaced Ownership Lack of Executive Engagement: A CEO who's afraid of data is like a restaurant owner who's never tasted the food. You can't lead what you don't understand. Lack of Executive Engagement : A CEO who's afraid of data is like a restaurant owner who's never tasted the food. You can't lead what you don't understand. Lack of Executive Engagement Disconnected KPIs: Tracking vanity metrics that don't indicate the health of your business. It's like judging a restaurant's success by how shiny the cutlery is. Disconnected KPIs : Tracking vanity metrics that don't indicate the health of your business. It's like judging a restaurant's success by how shiny the cutlery is. Disconnected KPIs Unclear Accountability: When everyone (and no one) is responsible for data quality. This is the data equivalent of "too many cooks spoil the broth." Unclear Accountability : When everyone (and no one) is responsible for data quality. This is the data equivalent of "too many cooks spoil the broth." Unclear Accountability Over-engineering: Building a data infrastructure fit for a tech giant when you're still a small startup. It's like equipping a food truck with a Michelin-star kitchen. Over-engineering : Building a data infrastructure fit for a tech giant when you're still a small startup. It's like equipping a food truck with a Michelin-star kitchen. Over-engineering Ignoring Data Governance: Treating data like a free-for-all buffet instead of a carefully curated menu. Without proper governance, your data can quickly become a health hazard. Ignoring Data Governance : Treating data like a free-for-all buffet instead of a carefully curated menu. Without proper governance, your data can quickly become a health hazard. Ignoring Data Governance Viewing Data as a Cost Center: Failing to see data strategy as a profit driver and growth enabler. This is like seeing your ingredients as just an expense, rather than the foundation of your culinary creations. Viewing Data as a Cost Center : Failing to see data strategy as a profit driver and growth enabler. This is like seeing your ingredients as just an expense, rather than the foundation of your culinary creations. Viewing Data as a Cost Center Now that we've identified these common nightmares, it's time to channel our inner Gordon Ramsay and turn this data disaster into a well-oiled, insight-generating machine. In the following sections, we'll dive into the step-by-step process of fixing your data strategy, ensuring that you're serving up actionable insights instead of a data nightmare. Fixing the Data Strategy: A New Recipe for Success Now, let's roll up our sleeves and fix this data strategy disaster. Here's a step-by-step recipe for success: Start with the CEO's Order (The CEO's Role) Imagine you're opening a new restaurant. Before you hire chefs or buy equipment, you need to decide what kind of food you're serving. Similarly, the CEO needs to clearly articulate what "dishes" (insights) they need to run the business effectively. Action Item : Use the 5 W's framework to define your key performance indicators (KPIs): Action Item Why: Understanding the purpose behind each KPI What: Aligning KPIs with specific actions Where: Mapping KPI impact across the organization When: Determining the right frequency for monitoring and action Who: Assigning clear ownership and responsibility Why: Understanding the purpose behind each KPI What: Aligning KPIs with specific actions Where: Mapping KPI impact across the organization When: Determining the right frequency for monitoring and action Who: Assigning clear ownership and responsibility For example, instead of just tracking "user engagement," get specific with the WHY: Use this template: "We measure [KPI] because it tells us [insight], to achieve [business objective]." We measure time on site because it tells us how long our users are spending their time on the platform, to achieve our business goals to increase the time spent by users by 15% by the end of this year from 3 minutes and 25 seconds to 3 minutes and 56 seconds. Assign the Head Chefs (Ownership and Accountability) Once you know what dishes you're serving, it's time to assign your head chefs. In the data world, this means finding the right owners for each KPI. Action Item : For each KPI, assign an owner who is: Action Item A subject matter expert who understands the business context, for example finance person will know best about the revenue metric why it is correct or not, and how it’s being used. A product manager who can treat the KPI as a "product" to be maintained and improved each KPI becomes a product, yes, it won’t have regular sprints in most cases, but it has a product mentality each time things are changing, and it has a rollout plan, change strategy, and end of life process. A subject matter expert who understands the business context, for example finance person will know best about the revenue metric why it is correct or not, and how it’s being used. A subject matter expert who understands the business context, for example finance person will know best about the revenue metric why it is correct or not, and how it’s being used. A product manager who can treat the KPI as a "product" to be maintained and improved each KPI becomes a product, yes, it won’t have regular sprints in most cases, but it has a product mentality each time things are changing, and it has a rollout plan, change strategy, and end of life process. A product manager who can treat the KPI as a "product" to be maintained and improved each KPI becomes a product, yes, it won’t have regular sprints in most cases, but it has a product mentality each time things are changing, and it has a rollout plan, change strategy, and end of life process. Remember, in small companies or startups, resources are often limited. You may not have enough people to run all tasks related to data. This means you need to be very focused and specific on what you need to see and how you get the information you need, especially when product development and revenue activities often take higher priority. Remember, in small companies or startups, resources are often limited. You may not have enough people to run all tasks related to data. This means you need to be very focused and specific on what you need to see and how you get the information you need, especially when product development and revenue activities often take higher priority. Design Your Kitchen (Data Architecture and Technology) Now that you know what you're cooking and who's in charge of each dish, it's time to set up your kitchen. This is where you'll think about data architecture and technology as well as pipelines. Action Item : Before choosing tools, map out your data flow: Action Item Identify data sources (your "ingredients") Determine data volumes (how much are you "cooking"?) Plan data transformations (your "cooking processes") Decide on data storage (your "pantry" and "refrigerator") Choose visualization tools (your "plating" and "presentation") Identify data sources (your "ingredients") Determine data volumes (how much are you "cooking"?) Plan data transformations (your "cooking processes") Decide on data storage (your "pantry" and "refrigerator") Choose visualization tools (your "plating" and "presentation") Remember, you don't need an industrial-sized freezer if you're only serving fresh, local ingredients. Similarly, don't invest in heavy-duty data processing tools if your data volume doesn't warrant it. Remember, you don't need an industrial-sized freezer if you're only serving fresh, local ingredients. Similarly, don't invest in heavy-duty data processing tools if your data volume doesn't warrant it. Implement Data Governance (Your Kitchen Rules) Every good kitchen has rules to ensure food safety and quality. In the data world, this is your data governance. Action Item : Embed governance into your architecture: Action Item Set up data validation and quality checks at each stage of your data pipeline Implement clear data access protocols Create a data dictionary so everyone speaks the same "language" Establish a process for updating and deprecating data sources Set up data validation and quality checks at each stage of your data pipeline Implement clear data access protocols Create a data dictionary so everyone speaks the same "language" Establish a process for updating and deprecating data sources Start Small, Iterate Often Remember, even the best chefs start with simple dishes before creating complex menus. Begin with a few key KPIs, get them right, and then expand. Gordon Ramsay's kitchen is a symphony of precision. For his Beef Wellington, he begins with a rigorous selection process for the beef, opting for a prime cut with optimal marbling. The pastry, a labor of love, is meticulously layered, ensuring a crisp exterior and flaky interior. But it's the duxelles that truly define his Wellington. A blend of finely chopped mushrooms, shallots, and herbs, it's cooked down to an intense umami-rich paste. Ramsay often mentions the importance of balancing flavors, ensuring that the richness of the beef is complemented by the earthy depth of the duxelles. The final touch is a delicate layer of pâté, providing a luxurious mouthfeel. With each element meticulously prepared, the Wellington is assembled with a surgeon's precision, ready to be transformed in the oven into a culinary masterpiece. Gordon Ramsay's kitchen is a symphony of precision. For his Beef Wellington, he begins with a rigorous selection process for the beef, opting for a prime cut with optimal marbling. The pastry, a labor of love, is meticulously layered, ensuring a crisp exterior and flaky interior. But it's the duxelles that truly define his Wellington. A blend of finely chopped mushrooms, shallots, and herbs, it's cooked down to an intense umami-rich paste. Ramsay often mentions the importance of balancing flavors, ensuring that the richness of the beef is complemented by the earthy depth of the duxelles. The final touch is a delicate layer of pâté, providing a luxurious mouthfeel. With each element meticulously prepared, the Wellington is assembled with a surgeon's precision, ready to be transformed in the oven into a culinary masterpiece. Action Item : Choose 3-5 core KPIs to focus on initially. Build out the entire process for these, from data collection to visualization. Get feedback, refine, and only then move on to more complex metrics. Action Item The Secret Sauce: Data as a Profit Driver Here's a crucial ingredient many organizations miss: data should be viewed as a profit unit, not a cost center. In most companies, people tend to think about the data strategy and its outcome as a sunk cost. But in reality, having data means that you can drive the business forward. It's your only way to discover trends, understand your users, and find your money drivers and money wasters. Think of data as a plate of hummus. It should be as simple and accessible as a hummus dish, but as complex and value-driving as the layers of flavors in a well-prepared hummus. It's a strategy everyone needs to take care of and understand, especially the CEO! I am a member of a group of self-cooks who have been trying for years to break the secret of some of their top hummus restaurants and how they drive the umami effect into the hummus that makes them so unique The Taste of Success: A Culture of Data-Driven Decision Making When you have a clear data strategy and an understanding of the KPIs they use, the team becomes much more focused. They know how they're being measured, and they can easily evaluate their efforts. They have something to be proud of when it works well or a story to tell when it breaks. Imagine a CMO and their team discussing what the CEO needs to see about their activity. Instead of just listing metrics and KPIs that create no action or feeling by the CEO, they're now able to present insights that drive decisions and show a clear understanding of how they're being measured and evaluated by the executive team. Your Call to Action: From Data Chaos to Data Cuisine If you're a C-suite executive struggling with your data strategy, it's time to step back and reassess. Are you focused on the right metrics? Have you assigned ownership? Is your technology supporting your needs or overwhelming them? Remember, a successful data strategy isn't about having the fanciest tools or the biggest data lake. It's about creating a system that consistently delivers the insights you need to run and grow your business. Ready to turn your data strategy from a messy kitchen into a Michelin-star operation? Let's talk. Reach out to discuss how I can make your organization truly data-driven, focusing on what truly matters for your success. After all, in the world of data, as in cooking, simplicity, quality, and precision trump complexity every time. Bon appétit!