#037 - No Budget, No Insights
Why struggling companies can’t build great data teams
Hey there,
After 10+ data migrations across three continents, I have noticed something uncomfortable.
The organisations with the strongest balance sheets consistently build the most sophisticated data capabilities.
At a major bank, we had dedicated data quality teams and budgets for cutting-edge platforms. At a mid-sized company, we struggled to get approval for basic monitoring tools.
The difference wasn’t technical expertise. It was Maslow’s hierarchy playing out in real-time.
This Week’s Deep Dive:
The 5-stage data maturity model mirrors human psychology
Why “AI-first” strategies fail for cash-strapped organisations
A practical assessment you can use on Monday morning
Real patterns from my enterprise implementations.
Let’s break down the uncomfortable truth about data capability.
The Story: Two Migrations, Two Worlds Apart
I have worked on two strikingly similar projects.
Both were migrations. Both had identical technical complexity and timelines.
One at a major bank. One at a mid-sized retailer.
Completely different outcomes.
The Bank Project:
Dedicated teams for data quality
Automated testing pipelines
Redundant systems during migration
Performance issues? Additional resources approved in 24 hours
The Retailer Project:
Every decision went through three approval layers
One analyst wearing multiple hats as the “data quality team”
Extra processing power during critical migration? Two weeks for sign-off
Result:
Bank: Delivered ahead of schedule, zero data loss
Retailer: Six months over, lost two team members to burnout
What I realised: Organisations can’t skip levels in the data maturity pyramid.
Just like humans can’t skip basic needs.
The Data Maturity Hierarchy: 5 Stages Every Organisation Climbs
Here’s the framework that explains everything:
Level 1: Survival (Basic Data Collection)
“We need reports to operate”
What it looks like:
Spreadsheet-driven reporting
Manual data extracts
Basic storage systems
Reactive “reporting as requested”
Organisations here: Startups, struggling companies, resource-constrained teams
Investment required: $50K-$200K annually
Time to progress: 6-18 months with dedicated effort
I’ve seen companies spend years stuck in this position. They think they need AI when they actually need consistent month-end reporting.
Level 2: Security & Trust (Reliable Foundation)
“Our data needs to be accurate and safe”
What it looks like:
Data validation processes
Backup and recovery systems
Basic governance frameworks
Consistent data definitions
Organisations here: Established businesses with regulatory requirements
Investment required: $200K-$500K annually
Time to progress: 12-24 months
Real example: One financial services project spent eight months establishing data lineage before touching analytics. Worth every hour.
Level 3: Collaboration (Breaking Silos)
“Everyone needs access to the same truth”
What it looks like:
Cross-departmental data sharing
Self-service analytics tools
Standardized dashboards
Data literacy programs
Organisations here: Growing companies with multiple business units
Investment required: $500K-$1M annually
Time to progress: 18-36 months
Level 4: Insights & Recognition (Advanced Analytics)
“We’re making data-driven decisions”
What it looks like:
Predictive modeling
Real-time dashboards
ML-powered recommendations
Industry recognition for data practices
Organisations here: Market leaders, well-funded scale-ups
Investment required: $1M-$3M annually
Time to progress: 2-4 years
Key insight: This is where most “AI transformation” projects actually begin. Not where they’re sold to start.
Level 5: Innovation (Data-Driven Transformation)
“Data is our competitive advantage”
What it looks like:
AI-powered product features
Real-time personalization
Automated decision systems
New revenue streams from data
Organisations here: for example Netflix, Amazon, Google, and mature fintech companies
Investment required: $3M+ annually
Time to maintain: Continuous evolution required
The Uncomfortable Truth: Money Talks, Data Walks
Here’s what I have observed across 15 years:
Level 1-2 organisations ask: “What’s the cheapest way to get insights?”
Level 3-4 organisations ask: “How do we scale our data capabilities?”
Level 5 organisations ask: “How do we stay ahead of disruption?”
The Pattern:
Financially stable companies don’t just have better data.
They can afford to fail fast, learn quickly, and iterate.
At one of our enterprise clients, we tested three different approaches to forecasting simultaneously.
At a struggling company, we had to pick one approach and hope it worked.
Resource availability directly correlates with the progression of data maturity.
Your Monday Morning Assessment
Rate your organisation (1-5) on each dimension:
Financial Stability:
Budget approval timelines: Immediate (5) → 6+ months (1)
Risk tolerance: High (5) → None (1)
Team investment: Growing (5) → Shrinking (1)
Data Maturity:
Data quality: Automated validation (5) → Manual checking (1)
Analytics capability: Predictive models (5) → Excel reports (1)
Decision speed: Real-time (5) → Monthly reviews (1)
The Pattern:
Your financial score predicts your data maturity ceiling.
Organisations consistently score within 1 point between financial stability and data maturity.
I have never seen a Level 1 financial organisation sustain Level 4 data practices.
What This Means for Your Career
If you’re at a Level 1-2 organisation:
Focus on foundational skills: SQL, data quality, basic automation
Build systems that show immediate ROI
Document everything; you’re building credibility for future investment
If you’re at a Level 3-4 organisation:
This is where careers accelerate
Learn advanced analytics and cloud architectures
Lead cross-functional initiatives
Position yourself for the transition to Level 5
If you’re at a Level 5 organisation:
Stay ahead of the curve—real-time systems, AI integration
Share knowledge externally—speaking, writing, thought leadership
Consider consulting back to lower levels
The key insight:
Align your skill development with your organisation’s realistic trajectory.
Not their aspirational goals.
The Bottom Line
Data maturity follows a similar progression to human development.
Organisations trying to jump from basic reporting to AI are like startups trying to offer luxury products.
The foundation isn’t there.
Your next promotion depends on understanding where your organisation really sits on this pyramid.
Not where leadership thinks they are.
I have seen talented data professionals burn out trying to build Level 5 capabilities with Level 2 resources.
The Solution:
Match your ambitions to your organisation’s actual maturity level.
Then systematically help them climb the pyramid.
What level is your organisation really operating at?
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And whenever you’re ready, there are 3 ways I can help you:
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