Why Your Company Needs a Data Roadmap Before You Invest in Data Infrastructure

You're considering investing in data infrastructure. Maybe:

• Automating your reporting
• Building dashboards
• Centralizing your data
• Hiring a data analyst

But here's the question nobody asks:

What business outcomes are you trying to enable?

Most companies skip this question and jump straight to solutions:

• "We need Power BI or Tableau."
• "We should hire a BI developer."
• "Let's build a data warehouse."

Six months and $100k later:

• You have infrastructure that doesn't align with your business priorities.
• Reports nobody asked for.
• Dashboards that don't drive decisions.

Here's why that happens—and how to avoid it.

The Problem: Building Without Direction

Scenario 1: The Reactive Approach

Month 1: Finance complains about manual reporting
→ Build automated finance reports

Month 3: Operations needs dashboards
→ Build operations dashboards

Month 6: Sales wants pipeline tracking
→ Build sales analytics

Month 9: CEO asks about customer retention
→ Realize you have no customer data infrastructure

Result:

• $80k spent on disconnected solutions
• No single source of truth
• Each department has their own data silo
• Strategic questions still can't be answered

You built three separate solutions instead of one foundation.

Scenario 2: The Technology-First Approach

Month 1: Decide to "invest in data infrastructure"

Decisions made:

• Buy Snowflake ($30k/year)
• Buy Tableau ($15k/year)
• Hire consultant to set it up ($40k)

Month 4: Infrastructure is built

Month 5: Leadership asks: "Now what? What should we do with this?"

Nobody knows.

You built infrastructure with no plan for how it connects to business goals.

What Both Approaches Miss

A roadmap that connects:

Business Goals → Data Needs → Infrastructure Decisions

Without this connection:

• You build what's urgent, not what's important
• You invest based on tools, not outcomes
• You can't measure success or ROI
• You can't justify continued investment

Why Organizations Need a Data Roadmap

Reason 1: Not All Data Needs Are Equal

Without a roadmap, all requests feel equally important:

• Finance needs automated reporting
• Operations needs real-time dashboards
• Sales needs pipeline analytics
• Marketing needs attribution tracking

All sound reasonable. But which drives your business goals?

With a roadmap based on business priorities:

This year's number one goal: Increase customer retention by 15%

Priority 1 data needs:

• Customer health metrics
• Churn indicators
• Usage patterns

Priority 2 data needs:

• Operational efficiency reporting
• Financial automation

Priority 3 data needs:

• Marketing attribution
• Sales pipeline analytics

Now you can sequence investments to support your most important goals first.

Reason 2: Data Infrastructure Is Expensive

Typical investment over 18 months:

• Infrastructure build: $50-80k
• Tools and licenses: $15-30k/year
• Ongoing maintenance: $10-20k/year
• Potential hire: $100-140k/year

Total: $200k+ over 18 months

Without a roadmap:

You can't answer:

• What business value are we getting for $200k?
• Is this the right investment vs. alternatives?
• How do we know it's working?

With a roadmap:

Investment: $200k over 18 months

Expected returns:

• Reduce customer churn by 3 points → $400k retained revenue
• Automate reporting → 80 hours/month saved → $120k/year value
• Improve decision speed → $50k in opportunity capture

ROI is clear and measurable.

Reason 3: You'll Make Better Build vs. Buy Decisions

Without a roadmap:

"We need better reporting. Should we hire someone or outsource?"

You're deciding HOW before you know WHAT.

With a roadmap:

Our data needs for the next 12 months:

• Automate core financial reporting (one-time build)
• Create operations dashboards (one-time build)
• Ongoing ad-hoc analysis (recurring need)

Now you can decide:

• One-time builds → Outsource the infrastructure
• Recurring analysis → Consider fractional or project-based help
• Both → Build infrastructure, then assess if you need ongoing analytical help

The roadmap tells you what to buy.

What a Good Data Roadmap Looks Like

Component 1: Business Priorities

Start here, not with data:

This year's strategic priorities:

• Grow revenue by 25%
• Improve customer retention
• Increase operational efficiency

These drive everything else.

Component 2: Key Decisions That Support Priorities

For each priority, identify critical decisions:

Priority: Grow revenue by 25%

Key decisions:

• Which markets/segments to target
• Which products to push
• What pricing adjustments to make
• Which sales strategies are working

Priority: Improve customer retention

Key decisions:

• Which customers are at risk
• What drives churn
• When to intervene
• What retention strategies work

Priority: Increase operational efficiency

Key decisions:

• Where are bottlenecks
• What processes are inefficient
• How to optimize resource allocation

Component 3: Data Needed for Those Decisions

For each decision, identify required data:

Decision: Which customers are at risk?

Data needed:

• Usage patterns over time
• Support ticket frequency/severity
• Payment history
• Engagement metrics
• Contract renewal dates

Current state:

• Usage data: Available but not centralized
• Support data: In separate system
• Payment data: In accounting system
• Engagement metrics: Not tracked

Gap: Need to centralize and connect these data sources

Component 4: Quarterly Implementation Themes

Don't try to do everything at once.

Q1 Theme: Revenue Growth Foundation

• Centralize sales and customer data
• Build revenue segmentation
• Automate pipeline tracking

Enables decisions: Market/segment targeting, product focus

Q2 Theme: Customer Health Infrastructure

• Integrate usage, support, and payment data
• Build customer health scoring
• Create at-risk customer alerts

Enables decisions: Churn prevention, retention strategy

Q3 Theme: Operational Efficiency

• Automate manual reporting
• Build operations dashboards
• Implement process tracking

Enables decisions: Bottleneck identification, resource optimization

Q4 Theme: Refinement and Advanced Capabilities

• Build on Q1-Q3 foundation
• Add predictive capabilities if warranted
• Optimize based on learnings

Component 5: Success Metrics

How you'll measure if it's working:

Q1 Success Metrics:

• Sales team can segment customers by value (yes/no)
• Revenue reports automated (save 12 hours/month)
• Pipeline visibility improved (measured by sales manager satisfaction)

Q2 Success Metrics:

• Can identify at-risk customers 60 days early (yes/no)
• Customer success team receives weekly health alerts
• Churn rate decreases by 2 points

Q3 Success Metrics:

• Monthly reporting time reduced by 70%
• Operations has real-time visibility into key metrics
• Decision-making speed improves (measured by surveys)

These metrics connect data investment to business outcomes.

How to Build Your Data Roadmap

Step 1: Clarify Business Strategy

Work with leadership to answer:

• What are our top 3 business priorities this year?
• Which one matters most?
• What does success look like (specific, measurable)?

Output: Clear strategic priorities

Step 2: Map Critical Decisions

For each priority, identify:

• What decisions need to be made?
• Who makes them?
• How often?
• What information would help?

Output: List of decisions organized by priority

Step 3: Assess Current Data State

For each decision, evaluate:

• Do we have this data today?
• Is it accessible?
• Is it reliable?
• What's missing?

Create a simple matrix:

Output: Clear view of what you have vs. what you need

Step 4: Prioritize and Sequence (1-2 hours)

Create quarterly themes based on:

• Business impact (supports Priority 1 goals)
• Feasibility (can we do this in 3 months?)
• Dependencies (what needs to come first?)

Output: 12-month roadmap with quarterly focus areas

Step 5: Define Success Metrics (1 hour)

For each quarter, specify:

• What capabilities will we have?
• What decisions will be enabled?
• How will we measure success?
• What's the expected business impact?

Output: Clear success criteria for each phase

Value: Prevents $50k-100k in wasted infrastructure spending

Common Roadmap Mistakes

Starting with Tools

Wrong: "We should buy Tableau and Snowflake"

Right: "We need to track customer health. What infrastructure enables that?"

Tools are the HOW, not the WHAT.

No Connection to Business Goals

Wrong: "Q1: Build data warehouse. Q2: Create dashboards. Q3: Hire analyst."

Right: "Q1: Enable customer retention decisions. Q2: Automate financial reporting. Q3: Build operational visibility."

Frame around outcomes, not activities.

Too Ambitious

Wrong: "This year we'll automate everything, build predictive models, and become data-driven"

Right: "This year we'll focus on our #1 priority (retention) and automate our most painful process (month-end close)"

Focus wins. Attempting everything guarantees nothing gets done well.

No Success Metrics

Wrong: "Build better reporting"

Right: "Reduce reporting time from 80 hours/month to 15 hours/month by Q2"

If you can't measure it, you can't manage it.

What Happens Without a Roadmap

The Cost of Reactive Building

Year 1:

• Build finance reports ($25k)
• Build sales dashboard ($20k)
• Build operations tracking ($30k)

Year 2:

• None of these systems talk to each other
• Need to rebuild with common foundation ($60k)
• Total spent: $135k

With a roadmap

Year 1:

• Build integrated foundation ($50k)
• Add finance, sales, ops capabilities incrementally ($30k)

Year 2:

• Build on existing foundation ($20k)
• Total spent: $100k

Savings: $35k and 6 months

The Bottom Line

Most companies build data infrastructure backwards:

Pick tools → Build infrastructure → Hope it's useful

The right sequence:

Clarify business strategy → Map data needs → Build what supports your goals

A data roadmap:

• Connects infrastructure to business outcomes
• Prevents wasted investment
• Enables better build vs. buy decisions
• Provides clear success metrics
• Ensures ROI

Before you invest $100k+ in data infrastructure, spend some time building a roadmap.

It is the difference between infrastructure that drives business value and infrastructure that just looks impressive.

Start with strategy. Build with purpose.

Thinking about investing in data infrastructure but not sure where to start? We help mid-sized companies build data roadmaps that connect infrastructure decisions to business strategy—so you invest in what matters, not just what's available.

Let's talk →