The Hard Truth About Data/Business Partnership Most Leaders Miss
The Hard Truth About Data/Business Partnership Most Leaders Miss
Aligning Data Teams with Business Outcomes: A Lesson I Wish I'd Learned Earlier
Aligning Data Teams with Business Outcomes: A Lesson I Wish I'd Learned Earlier
Aligning Data Teams with Business Outcomes: A Lesson I Wish I'd Learned Earlier
Growing businesses hit a data wall at exactly the wrong moment - precisely when they need insights most to break through to the next level.
This pattern repeats with alarming consistency. You've outgrown your basic tools but enterprise solutions feel overwhelming and unnecessarily complex.
The result? Critical business decisions based on fragmented, unreliable data that's manually cobbled together by your most valuable team members. This isn't just inefficient - it's actively preventing your growth.
Today, we're diving into three critical components every growing company needs to consider when evolving their data infrastructure:
How fragmented data silently kills revenue growth (and how to spot the warning signs)
The middle path between basic tools and enterprise-grade complexity
Building data systems that scale with your business, not against it
Let's jump in.
If you're a growth-stage company struggling with manual reporting, unclear attribution, or data scattered across multiple systems, here are the resources you need to dig into to overcome these challenges:
Weekly Resource List:
Banking on Better Data Integration (5 min read) See how regional Fulton Bank transformed from fragmented customer systems to a unified view across 15+ platforms, automating processes and delivering personalized customer experiences that smaller institutions typically can't match.
Utilities Transformation Through Data Analytics (3 min read) Discover how traditional utility companies are leveraging data insights to optimize asset management, improve workforce deployment, and create innovative business models beyond conventional service offerings.
Preparing Your Data Infrastructure for AI (60 min talk) This comprehensive webinar scheduled for August explains what "AI-ready data" actually means and outlines the critical components organizations need for effective data management that drives competitive advantage.
Aligning Data Teams with Business Outcomes: A Lesson I Wish I'd Learned Earlier
One lesson I wish I'd learned earlier in my data leadership career: tie your data teams to business outcomes from day one.
For years, I approached data projects with a heavy technical lens. Build the best architecture. Create the cleanest data pipelines. Design the most comprehensive data warehouse. While these technical foundations matter, I discovered that without explicit business alignment, even brilliant technical work often fails to deliver meaningful impact.
Let's break down what changed my approach and how you can avoid making the same costly mistakes:
Start with shared accountability
When data teams and business teams pursue separate objectives, misalignment is inevitable. This creates a service provider/customer dynamic rather than a true partnership.
Instead, create shared OKRs that both teams are measured against. When your data analysts have skin in the game on marketing conversion rates or customer retention metrics, they approach problems differently. They shift from "building what was requested" to "achieving the business outcome."
For example, rather than tasking your data team with "building a customer dashboard," make them jointly responsible with the sales team for "increasing customer retention by 15%." This transforms the dashboard from a deliverable into a means to a measurable end.
Quantify expected value early
Too many data projects begin with vague promises of "better insights" or "improved decision-making." These fuzzy objectives make it impossible to determine if your initiatives are succeeding.
Before writing a single line of code, work with business partners to establish baseline metrics and projected improvements. What specific needle are you trying to move? By how much? Within what timeframe? This creates clarity around what "success" actually means and enables both teams to course-correct if progress stalls.
In my experience, the most successful projects start with statements like: "Currently, our customer acquisition cost is $150. With better channel attribution data, we believe we can reduce this to $120 within six months."
Implement phased value delivery
The traditional approach to data projects often requires months of groundwork before delivering any value. This creates a dangerous disconnect between effort and outcome.
Rather than waiting for the "perfect" end-to-end solution, identify quick wins that demonstrate tangible business value. Can you deliver a simplified version of the final solution that addresses 80% of the need in one-third of the time? These early victories build trust and create momentum for more ambitious initiatives.
At Crawford McMillan, we've found that a series of 4-6 week delivery cycles, each producing measurable business value, outperforms longer timelines with bigger promises nearly every time.
Focus on adoption, not just delivery
The most elegant dashboard in the world provides zero value if business teams don't use it. The same goes for data models, APIs, and analytics tools.
Incorporate change management, training, and user adoption metrics into your definition of success. Track not just technical completion, but active usage, satisfaction, and most importantly, whether the solution is actually driving the intended business outcomes.
This means getting out from behind the screens and spending time with the people who will actually use what you're building. Understanding their workflows, pain points, and what constitutes a win in their world is invaluable for creating solutions that drive real change.
Challenge unrealistic expectations
Part of business alignment means having honest conversations about what's possible.
When executives demand "AI" solutions without addressing fundamental data quality issues, it's your responsibility to reset expectations. When marketing wants "real-time" dashboards but their attribution model is fundamentally flawed, pushing back isn't being difficult – it's being responsible.
These conversations aren't easy, but they prevent months of wasted effort and damaged credibility. The best data leaders I know are masters at navigating the balance between aspiration and reality, finding creative paths forward that acknowledge constraints while still driving progress.
The goal isn't technical elegance - it's creating measurable business impact. Without this alignment, even the most sophisticated data platform becomes an expensive exercise in disconnected perfection.
Growing businesses hit a data wall at exactly the wrong moment - precisely when they need insights most to break through to the next level.
This pattern repeats with alarming consistency. You've outgrown your basic tools but enterprise solutions feel overwhelming and unnecessarily complex.
The result? Critical business decisions based on fragmented, unreliable data that's manually cobbled together by your most valuable team members. This isn't just inefficient - it's actively preventing your growth.
Today, we're diving into three critical components every growing company needs to consider when evolving their data infrastructure:
How fragmented data silently kills revenue growth (and how to spot the warning signs)
The middle path between basic tools and enterprise-grade complexity
Building data systems that scale with your business, not against it
Let's jump in.
If you're a growth-stage company struggling with manual reporting, unclear attribution, or data scattered across multiple systems, here are the resources you need to dig into to overcome these challenges:
Weekly Resource List:
Banking on Better Data Integration (5 min read) See how regional Fulton Bank transformed from fragmented customer systems to a unified view across 15+ platforms, automating processes and delivering personalized customer experiences that smaller institutions typically can't match.
Utilities Transformation Through Data Analytics (3 min read) Discover how traditional utility companies are leveraging data insights to optimize asset management, improve workforce deployment, and create innovative business models beyond conventional service offerings.
Preparing Your Data Infrastructure for AI (60 min talk) This comprehensive webinar scheduled for August explains what "AI-ready data" actually means and outlines the critical components organizations need for effective data management that drives competitive advantage.
Aligning Data Teams with Business Outcomes: A Lesson I Wish I'd Learned Earlier
One lesson I wish I'd learned earlier in my data leadership career: tie your data teams to business outcomes from day one.
For years, I approached data projects with a heavy technical lens. Build the best architecture. Create the cleanest data pipelines. Design the most comprehensive data warehouse. While these technical foundations matter, I discovered that without explicit business alignment, even brilliant technical work often fails to deliver meaningful impact.
Let's break down what changed my approach and how you can avoid making the same costly mistakes:
Start with shared accountability
When data teams and business teams pursue separate objectives, misalignment is inevitable. This creates a service provider/customer dynamic rather than a true partnership.
Instead, create shared OKRs that both teams are measured against. When your data analysts have skin in the game on marketing conversion rates or customer retention metrics, they approach problems differently. They shift from "building what was requested" to "achieving the business outcome."
For example, rather than tasking your data team with "building a customer dashboard," make them jointly responsible with the sales team for "increasing customer retention by 15%." This transforms the dashboard from a deliverable into a means to a measurable end.
Quantify expected value early
Too many data projects begin with vague promises of "better insights" or "improved decision-making." These fuzzy objectives make it impossible to determine if your initiatives are succeeding.
Before writing a single line of code, work with business partners to establish baseline metrics and projected improvements. What specific needle are you trying to move? By how much? Within what timeframe? This creates clarity around what "success" actually means and enables both teams to course-correct if progress stalls.
In my experience, the most successful projects start with statements like: "Currently, our customer acquisition cost is $150. With better channel attribution data, we believe we can reduce this to $120 within six months."
Implement phased value delivery
The traditional approach to data projects often requires months of groundwork before delivering any value. This creates a dangerous disconnect between effort and outcome.
Rather than waiting for the "perfect" end-to-end solution, identify quick wins that demonstrate tangible business value. Can you deliver a simplified version of the final solution that addresses 80% of the need in one-third of the time? These early victories build trust and create momentum for more ambitious initiatives.
At Crawford McMillan, we've found that a series of 4-6 week delivery cycles, each producing measurable business value, outperforms longer timelines with bigger promises nearly every time.
Focus on adoption, not just delivery
The most elegant dashboard in the world provides zero value if business teams don't use it. The same goes for data models, APIs, and analytics tools.
Incorporate change management, training, and user adoption metrics into your definition of success. Track not just technical completion, but active usage, satisfaction, and most importantly, whether the solution is actually driving the intended business outcomes.
This means getting out from behind the screens and spending time with the people who will actually use what you're building. Understanding their workflows, pain points, and what constitutes a win in their world is invaluable for creating solutions that drive real change.
Challenge unrealistic expectations
Part of business alignment means having honest conversations about what's possible.
When executives demand "AI" solutions without addressing fundamental data quality issues, it's your responsibility to reset expectations. When marketing wants "real-time" dashboards but their attribution model is fundamentally flawed, pushing back isn't being difficult – it's being responsible.
These conversations aren't easy, but they prevent months of wasted effort and damaged credibility. The best data leaders I know are masters at navigating the balance between aspiration and reality, finding creative paths forward that acknowledge constraints while still driving progress.
The goal isn't technical elegance - it's creating measurable business impact. Without this alignment, even the most sophisticated data platform becomes an expensive exercise in disconnected perfection.
Growing businesses hit a data wall at exactly the wrong moment - precisely when they need insights most to break through to the next level.
This pattern repeats with alarming consistency. You've outgrown your basic tools but enterprise solutions feel overwhelming and unnecessarily complex.
The result? Critical business decisions based on fragmented, unreliable data that's manually cobbled together by your most valuable team members. This isn't just inefficient - it's actively preventing your growth.
Today, we're diving into three critical components every growing company needs to consider when evolving their data infrastructure:
How fragmented data silently kills revenue growth (and how to spot the warning signs)
The middle path between basic tools and enterprise-grade complexity
Building data systems that scale with your business, not against it
Let's jump in.
If you're a growth-stage company struggling with manual reporting, unclear attribution, or data scattered across multiple systems, here are the resources you need to dig into to overcome these challenges:
Weekly Resource List:
Banking on Better Data Integration (5 min read) See how regional Fulton Bank transformed from fragmented customer systems to a unified view across 15+ platforms, automating processes and delivering personalized customer experiences that smaller institutions typically can't match.
Utilities Transformation Through Data Analytics (3 min read) Discover how traditional utility companies are leveraging data insights to optimize asset management, improve workforce deployment, and create innovative business models beyond conventional service offerings.
Preparing Your Data Infrastructure for AI (60 min talk) This comprehensive webinar scheduled for August explains what "AI-ready data" actually means and outlines the critical components organizations need for effective data management that drives competitive advantage.
Aligning Data Teams with Business Outcomes: A Lesson I Wish I'd Learned Earlier
One lesson I wish I'd learned earlier in my data leadership career: tie your data teams to business outcomes from day one.
For years, I approached data projects with a heavy technical lens. Build the best architecture. Create the cleanest data pipelines. Design the most comprehensive data warehouse. While these technical foundations matter, I discovered that without explicit business alignment, even brilliant technical work often fails to deliver meaningful impact.
Let's break down what changed my approach and how you can avoid making the same costly mistakes:
Start with shared accountability
When data teams and business teams pursue separate objectives, misalignment is inevitable. This creates a service provider/customer dynamic rather than a true partnership.
Instead, create shared OKRs that both teams are measured against. When your data analysts have skin in the game on marketing conversion rates or customer retention metrics, they approach problems differently. They shift from "building what was requested" to "achieving the business outcome."
For example, rather than tasking your data team with "building a customer dashboard," make them jointly responsible with the sales team for "increasing customer retention by 15%." This transforms the dashboard from a deliverable into a means to a measurable end.
Quantify expected value early
Too many data projects begin with vague promises of "better insights" or "improved decision-making." These fuzzy objectives make it impossible to determine if your initiatives are succeeding.
Before writing a single line of code, work with business partners to establish baseline metrics and projected improvements. What specific needle are you trying to move? By how much? Within what timeframe? This creates clarity around what "success" actually means and enables both teams to course-correct if progress stalls.
In my experience, the most successful projects start with statements like: "Currently, our customer acquisition cost is $150. With better channel attribution data, we believe we can reduce this to $120 within six months."
Implement phased value delivery
The traditional approach to data projects often requires months of groundwork before delivering any value. This creates a dangerous disconnect between effort and outcome.
Rather than waiting for the "perfect" end-to-end solution, identify quick wins that demonstrate tangible business value. Can you deliver a simplified version of the final solution that addresses 80% of the need in one-third of the time? These early victories build trust and create momentum for more ambitious initiatives.
At Crawford McMillan, we've found that a series of 4-6 week delivery cycles, each producing measurable business value, outperforms longer timelines with bigger promises nearly every time.
Focus on adoption, not just delivery
The most elegant dashboard in the world provides zero value if business teams don't use it. The same goes for data models, APIs, and analytics tools.
Incorporate change management, training, and user adoption metrics into your definition of success. Track not just technical completion, but active usage, satisfaction, and most importantly, whether the solution is actually driving the intended business outcomes.
This means getting out from behind the screens and spending time with the people who will actually use what you're building. Understanding their workflows, pain points, and what constitutes a win in their world is invaluable for creating solutions that drive real change.
Challenge unrealistic expectations
Part of business alignment means having honest conversations about what's possible.
When executives demand "AI" solutions without addressing fundamental data quality issues, it's your responsibility to reset expectations. When marketing wants "real-time" dashboards but their attribution model is fundamentally flawed, pushing back isn't being difficult – it's being responsible.
These conversations aren't easy, but they prevent months of wasted effort and damaged credibility. The best data leaders I know are masters at navigating the balance between aspiration and reality, finding creative paths forward that acknowledge constraints while still driving progress.
The goal isn't technical elegance - it's creating measurable business impact. Without this alignment, even the most sophisticated data platform becomes an expensive exercise in disconnected perfection.
Still reading? Book a call to grow your business into uncharted territory!
If you want to achieve ground-breaking growth with Enterprise-grade business intelligence as a key part of your success, then you're in the right place.
Still reading? Book a call to grow your business into uncharted territory!
If you want to achieve ground-breaking growth with Enterprise-grade business intelligence as a key part of your success, then you're in the right place.
Still reading? Book a call to grow your business into uncharted territory!
If you want to achieve ground-breaking growth with Enterprise-grade business intelligence as a key part of your success, then you're in the right place.