Which Department Should Get Data Infrastructure First? A Prioritization Framework

You've decided to invest in data infrastructure. You've got budget approved. You're ready to move from manual spreadsheets to automated pipelines.

But here's the reality: you can't fix everything for everyone all at once.

So the question becomes: Which department gets automated reporting first?

This isn't just a scheduling question; it's a strategic decision that determines whether your data project builds momentum or stalls out.

Here's a framework for making that choice.

Why This Decision Matters

The temptation is to try to do everything simultaneously:

• Automate finance reporting
• Build operations dashboards
• Create sales analytics
• Implement customer success metrics
• Add inventory tracking

This almost always fails because:

• Scope becomes unmanageable - Too many stakeholders, too many requirements
• Timeline extends indefinitely - Six months becomes twelve months becomes never
• Resources get spread thin - Everything gets 30% effort instead of 100%
• No one sees value early - Takes too long to deliver anything useful
• Political capital depletes - Leadership loses patience before results appear

The better approach:

Start with one department. Prove value fast. Build momentum. Then expand.

But which department?

Three Strategic Approaches

Approach 1: Start with Finance (The Safe Choice)

The logic:

Finance touches everything. If you automate financial reporting, you create a foundation that benefits the entire company.

Plus, finance leaders are typically:

• Analytical by nature
• Comfortable with data
• Influential with leadership
• Budget holders for these projects

When this works:

• CFO/Controller is your executive sponsor
• Month-end close is a painful, manual process
• Financial reporting accuracy is a major concern
• You need to demonstrate ROI in concrete dollar terms
• Other departments will follow finance's lead

When this doesn't work:

• Finance is actually well-organized already
• The real pain is in operations, not accounting
• Finance team is resistant to change
• You need quick wins to build organizational buy-in

Case Study:

A 400-person health tech company started with finance. Their month-end close took between eight to ten days. We automated the core financial reporting in 6 weeks.

Results:

• Close dropped to below four days
• CFO became the project's biggest advocate
• Finance team had time to actually analyze trends
• Other departments saw the value and requested automation

Why it worked:

The CFO had credibility across the organization. When she said "this transformed how we work," everyone listened.

Approach 2: Start with Operations (The High-Impact Choice)

The logic:

Operations makes decisions daily; sometimes hourly. They benefit most from real-time or near-real-time data.

Plus, operations improvements are:

• Immediately visible
• Measurable in concrete terms
• Understood by everyone in the company

When this works:

• Operations is drowning in manual reporting
• Daily/weekly decisions are being made on gut feel
• You need to demonstrate immediate business impact
• The ops leader is vocal and influential
• You can measure clear before/after metrics

When this doesn't work:

• Operations data is extremely messy
• Too many systems to integrate quickly
• Operational processes are constantly changing
• No clear decision-maker in operations

Use Case:

A 90-person distribution company started with warehouse operations. Their inventory visibility was terrible; they would discover stockouts after customer orders were placed.

They built a simple operational dashboard showing:

• Current inventory levels
• Orders scheduled for today
• Items running low
• Purchase orders in transit

Results:

• Stockouts dropped 60% in the first month
• Warehouse manager became the project champion
• Sales team noticed improved order fulfillment
• Company avoided $40k in expedited shipping costs

Why it worked:

The impact was immediate and visible. Everyone saw the difference within weeks.

Approach 3: Start with the Squeaky Wheel (The Momentum Choice)

The logic:

Start with whoever is most vocal about needing better data; even if they're not the "obvious" strategic choice.

Why this works:

The squeaky wheel will:

• Engage deeply in the project (they actually care)
• Provide detailed requirements (they've thought about this)
• Use what you build (they've been waiting for it)
• Talk about the success (they're already vocal)

That last point is critical:

Your data project needs internal advocates. People who say "this changed how I work" in meetings, at lunch, in Slack channels.

The squeaky wheel becomes your marketing department.

When this works:

• Someone specific has been asking for better data for months/years
• They're influential or well-connected in the organization
• Their needs are achievable (not pie-in-the-sky requests)
• You have the data sources they need
• Success would be clearly visible

When this doesn't work:

• The squeaky wheel's requests are actually unrealistic
• They're not respected in the organization
• Their needs require data you don't have access to
• They're squeaky but won't actually use what you build

Case study:

A 450-person hardware manufacturing firm had a VP of global customer service who had been asking for customer health metrics for two years.

She wasn't the obvious first choice; finance or project delivery seemed more strategic. But she was:

• Vocal about the need
• Specific about what she wanted
• Willing to engage in the project
• Well-respected by leadership

We started with her team.

Results:

• She became the project's biggest champion
• Talked about it in every leadership meeting
• Other departments asked "when can we get this?"
• Built organizational momentum for broader rollout

Why it worked:

She had been waiting for this. She used it immediately. And she told everyone about it.

The momentum effect is real.

The Decision Framework

Ask yourself these five questions:

Question 1: Who has the most painful manual process right now?

Not "who should strategically go first" but "who is hurting most?"

Look for:

• Hours spent per week on manual work
• Frequency of errors
• Missed deadlines
• Complaints about reporting burden

The pain level predicts engagement level.

Question 2: Who has the cleanest, most accessible data?

Harsh reality:

Some departments have data scattered across 8 systems with no common identifiers. Others have 2-3 core systems that are well-structured.

Start with the easier win.

You can always tackle the complex department second, after you've proven value and built credibility.

Ask:

• How many data sources do they use?
• Are those sources accessible to you?
• Do they have unique identifiers that connect?
• Is the data structure reasonably clean?

Question 3: Who can clearly articulate what success looks like?

Red flag phrases:

• "We just need better visibility"
• "We want to be more data-driven"
• "We need dashboards"

Green flag phrases:

• "I need to see inventory levels every morning by 8 AM"
• "I need to close books in 5 days instead of 12"
• "I need to know which projects are over budget this week"

Specific, measurable outcomes equal to higher success probability.

Question 4: Who will actually use what you build?

The enthusiasm test:

Will they:

• Participate in requirements discussions?
• Review mockups and provide feedback?
• Test the dashboard before launch?
• Check it daily once it's live?

If they're not willing to invest time upfront, they won't use it after launch.

Question 5: Who has influence to spread the word?

Honest assessment:

When this person talks about the project's success, will others listen?

Look for:

• Cross-functional relationships
• Respect from leadership
• Natural communicators
• People who attend lots of meetings

One influential advocate is worth five passive users.

The Scoring System

Rate each potential department on these five factors (1-5 scale):

In this example: Finance scores highest (21), but Operations is close (20) with higher pain and engagement.

The decision:

If finance is the safe political choice → Start with Finance
If you need fast momentum → Start with Operations
If Customer Success VP has been asking for years → Start with the Squeaky Wheel

There's no single "right" answer; it depends on your organization.

Common Mistakes to Avoid

Mistake 1: Starting with whoever has the budget

Just because finance is paying for it doesn't mean they should go first.

Better approach: Start where the impact is clearest, then use that success to justify expansion.

Mistake 2: Starting with the CEO's favorite department

Leadership attention doesn't guarantee user engagement.

Better approach: Start where the actual users will benefit and use the system daily.

Mistake 3: Starting with the most complex department

"If we can solve for operations, we can solve for anyone!"

Reality: You'll spend 6 months on complexity, deliver nothing, and lose credibility.

Better approach: Start with a win. Build credibility. Then tackle complexity.

Mistake 4: Letting the data team decide based on technical ease

"Sales data is easiest to access, so let's start there."

Problem: Sales might not actually need or want automated reporting.

Better approach: Start where the business need is strongest, even if technically harder.

Mistake 5: Trying to start with multiple departments "to be fair"

This dilutes everything.

Better approach: Pick one. Succeed. Then expand. Speed matters more than fairness.

The Phased Rollout Strategy

Once you pick your first department, here's how to expand:

Phase 1: The Pioneer (Weeks 1-8)

Department: Your chosen first department
Goal: Prove the concept, build a success story
Deliverables:

• 2-3 core automated reports
• Basic dashboard
• Documentation
• Training for key users

Success metric: Daily usage by 80%+ of department

Phase 2: The Early Adopter (Months 3-4)

Department: The second most painful or most vocal
Goal: Show it's replicable, not a one-off
Deliverables:

• Apply learnings from Phase 1
• Standardize approach
• Build momentum

Success metric: Two departments actively using and advocating

Phase 3: The Foundation (Months 5-6)

Department: Usually finance if you didn't start there
Goal: Create the single source of truth foundation
Deliverables:

• Centralize core data sources
• Build company-wide metrics
• Standardize definitions

Success metric: Consistent numbers across departments

Phase 4: Scaling (Months 7-12)

Remaining departments: Based on demand and priority
Goal: Achieve company-wide adoption
Deliverables:

• Self-service capabilities
• Department-specific dashboards
• Advanced analytics where needed

Success metric: 90%+ of data needs met by automated system

Making the Call

If you're still unsure, ask these three questions:

Question 1: "If we could only automate one thing, what would have the biggest immediate impact?"

Not the biggest strategic impact; the biggest felt impact in the next 30 days.

Question 2: "Who in this company has been asking for better data the longest?"

Sometimes the answer is obvious. They've been waiting. Start there.

Question 3: "Which success story will be easiest to tell?"

You need to build political capital for the next phase. Which department will give you the clearest before/after story?

A simulated Decision

Here's how a company can make this call:

140-person e-commerce company

The candidates:

• Finance: Wanted automated month-end close (pain equal to 8/10, influence equal to 9/10)
• Operations: Needed real-time inventory visibility (pain equal to 10/10, influence equal to 6/10)
• Marketing: Asked for customer analytics for 2 years (pain equal to 6/10, influence equal to 8/10)

The decision: Start with Operations

Why:

• Pain level was highest (costing them real money in stockouts)
• Success would be immediately visible
• Timeline was shortest (4 weeks to see results)
• Would generate momentum for finance and marketing phases

The results:

• Operations went live in 5 weeks
• Stockouts dropped 65% in first month
• Operations manager talked about it constantly
• Finance said "we're ready when you are"
• Marketing got excited seeing the success

Starting with operations created organizational momentum that made the next phases easier.

Your Action Plan

Step 1: List Your Candidates (15 minutes)

Which 3-4 departments are possibilities?

Step 2: Score Each Department (30 minutes)

Use the five-factor scoring system above.

Step 3: Talk to Stakeholders (1 week)

Have conversations with potential first departments:

• "What's your biggest reporting pain right now?"
• "If I could automate one thing for you, what would it be?"
• "How would you measure success?"

Step 4: Make the Decision (Don't overthink it)

Pick based on:

1. Highest pain + highest engagement + clear success metrics equal to Start here
2. When in doubt, go with the squeaky wheel
3. Trust your gut on who will be the best advocate

Step 5: Communicate the Plan (Important!)

Tell other departments:

• "We're starting with [Department] because [reason]"
• "We'll have results in 6-8 weeks"
• "Then we're expanding to [Department 2]"
• "Your turn is coming in [timeframe]"

Setting expectations prevents political problems.

The Bottom Line

There's no universally "right" first department.

But there are wrong ways to make the decision:

• Starting with whoever has the budget
• Starting with whoever is politically safe
• Starting with whoever is technically easiest
• Trying to start with everyone at once

The right way:

Start where the pain is highest, the engagement is strongest, and the success story will be clearest.

Then use that momentum to expand.

Remember: You're not just building data infrastructure. You're building organizational buy-in.

The department that goes first isn't just getting automated reporting; they're becoming your marketing team for the next phase.

Choose wisely. Start fast. Build momentum.

Trying to decide which department should get automated reporting first? We help mid-sized companies prioritize implementation phases based on organizational dynamics, not just technical considerations.

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