Is Data Your Company's Second Product? (And Why It Matters for Infrastructure Decisions)

Most mid-sized companies think of data as a necessary evil; something you track because you have to, not because it creates value.

But there's a category of companies where data isn't just internal reporting. It's a strategic asset. Maybe even their second product.

Understanding which category you're in changes everything about how you should invest in data infrastructure.

Here's how to tell the difference; and what it means for your data strategy.

What "Data as a Second Product" Actually Means

The traditional view:

Your company makes widgets. Data exists to:

• Track widget sales (reporting)
• Manage widget inventory (operations)
• Calculate widget profitability (accounting)

Data is a byproduct of your real business.

The second product view:

Your company makes widgets. But the patterns in your data; who buys, when they buy, why they buy, what they buy next; are potentially as valuable as the widgets themselves.

Data is an asset that can create competitive advantage.

The Amazon Example

Amazon's primary product: Physical goods delivered to your door

Amazon's second product: The patterns of what people want, when they want it, and what they'll want next

How they use this second product:

• Product recommendations (35% of sales)
• Dynamic pricing algorithms
• Inventory optimization
• Supplier negotiations (data on demand trends)
• AWS services built on internal infrastructure
• Advertising platform powered by purchase data

Amazon doesn't just sell things. They sell things informed by their data advantage.

That data advantage is arguably more valuable than their logistics network.

How to Tell If Data Is Your Second Product

Ask yourself these questions:

Question 1: Does better data create competitive advantage?

Test:

If you had perfect information about customer behavior, market trends, or operational patterns, would it:

A) Help you run more efficiently
This is data as a tool. Valuable, but operational.

B) Let you do things competitors fundamentally can't
This is data as a product. Strategic advantage.

Example A (Operational):

A construction company uses data to:

• Track project timelines
• Monitor equipment utilization
• Calculate labor costs

This helps them run better, but doesn't create competitive advantage. Any construction company with decent processes can do this.

Example B (Strategic):

An insurance company uses data to:

• Predict claim likelihood more accurately than competitors
• Price policies more competitively
• Identify fraud patterns others miss

This creates defensible advantage. Better data equal to better pricing equal to more market share.

Question 2: Do you make product decisions based on behavioral patterns?

Operational data use: "We sold 1,000 units last month. Let's order 1,000 more."

Strategic data use: "Users who buy Product A typically buy Product B within 60 days. Let's bundle them and increase our average order value by 23%."

The difference:

Operational equal to reporting what happened
Strategic equal to predicting what will happen and acting on it

Use Cases:

Company A (Operational): E-commerce retailer

Uses data to:

• Track daily sales
• Monitor inventory levels
• Calculate margins

They're using data to run the business. Important, but not differentiating.

Company B (Strategic): E-commerce retailer

Uses data to:

• Predict which products will trend based on early signals
• Adjust pricing dynamically based on demand and inventory
• Recommend products based on browsing patterns
• Optimize shipping routes based on historical delivery data

Same industry. Different relationship with data.

Company B's data capabilities are a competitive moat.

Question 3: Would a competitor pay for your data?

The thought experiment:

If you anonymized your data and offered it for sale, would competitors buy it?

If yes: Your data has standalone value. It's a product.

If no: Your data supports your operations but isn't inherently valuable.

Examples:

Standalone valuable data:

• Retail purchase patterns (who buys what, when, why)
• Healthcare outcomes data (which treatments work)
• Logistics efficiency data (optimal routes, timing)
• Customer behavior data (usage patterns, preferences)

Operationally valuable but not standalone:

• Your employee timesheet data
• Your accounts payable aging report
• Your internal meeting notes
• Your equipment maintenance logs

Question 4: Is data collection part of your product strategy?

Operational approach: "We need to track revenue for accounting purposes."

Strategic approach: "We designed our product to capture usage patterns that will inform our next version."

Use Cases:

Company A: SaaS tool for project management

Tracks:

• User logins (for billing)
• Feature usage (for support)
• Error logs (for debugging)

This is operational data collection. Necessary but reactive.

Company B: SaaS tool for project management

Tracks:

• Which features correlate with customer retention
• What workflow patterns predict upgrades
• Which integrations drive highest satisfaction
• Where users get stuck before churning

Then uses this data to:

• Prioritize product roadmap
• Design onboarding flow
• Build predictive churn models
• Create targeted upsell campaigns

Company B treats data collection as product strategy, not just operational necessity.

The Strategic Data Maturity Spectrum

Level 1: Compliance & Reporting

What you track: What you legally have to (revenue, taxes, basic operations)

Why you track it: Because you must

Investment level: Minimum necessary

Example: Most small businesses, many mid-sized companies

Level 2: Operational Efficiency

What you track: Metrics that help you run better (inventory, productivity, costs)

Why you track it: To optimize operations

Investment level: Moderate, ROI-focused

Example: Most mid-sized companies, traditional industries

Level 3: Strategic Decision-Making

What you track: Patterns that inform strategy (customer behavior, market trends, competitive position)

Why you track it: To make better strategic choices

Investment level: Significant, seen as competitive advantage

Example: Growth-focused companies, data-aware industries

Level 4: Data as Competitive Moat

What you track: Everything that creates predictive advantage

Why you track it: Data capabilities are part of your defensible position

Investment level: Major, seen as core to business model

Example: Tech companies, data-driven businesses

Most mid-sized companies are at Level 2.

The question isn't whether to reach Level 4; it's whether you should.

When Data Should Be Your Second Product

You should invest heavily in data infrastructure if:

Signal 1: Your industry rewards prediction

Industries where this is true:

• E-commerce (predicting what customers want)
• Insurance (predicting risk)
• Logistics (predicting demand and routing)
• Healthcare (predicting outcomes)
• Finance (predicting market movements)
• SaaS (predicting churn and expansion)

Industries where it's less true:

• Traditional manufacturing
• Professional services
• Construction
• Most B2B service businesses

The test:

If you could predict tomorrow's demand with 100% accuracy, would it:

• Save you 5-10%? → Data is operationally valuable
• Give you 30-50% advantage over competitors? → Data is strategically valuable

Signal 2: Network effects exist in your data

The dynamic:

The more data you have → The better your product/service → The more customers you get → The more data you collect → The better your product/service

This is a flywheel where data creates competitive advantage that's hard to replicate.

Example:

Google Search:

• More searches → Better understanding of intent
• Better understanding → More relevant results
• More relevant results → More users
• More users → More searches

Your business has data network effects if:

• Serving more customers improves your service
• Historical data makes your predictions better
• Data from one customer benefits other customers
• Competitors can't easily replicate your data advantage

Signal 3: Data enables pricing power

Can better data let you:

• Charge more (because you deliver better outcomes)?
• Reduce costs significantly (operational efficiency)?
• Capture more market share (better product/service)?

Use Case:

Insurance company with better actuarial data:

• Can price policies more accurately
• Attracts better risks, avoids bad risks
• Achieves better loss ratios than competitors
• Creates virtuous cycle of profitability

Their data advantage directly translates to pricing power.

Signal 4: You're building features that depend on data

Questions to ask:

• Is your product roadmap dependent on having better data?
• Are you building ML/AI features that require significant data?
• Do customers choose you because of data-driven insights you provide?

If yes to any of these, data is strategic, not just operational.

When Data Is Just Data (And That's Fine)

You probably don't need to treat data as a second product if:

Reality Check 1: Your competitive advantage is elsewhere

Your advantage might be:

• Relationships and reputation
• Proprietary technology or IP
• Geographic presence
• Unique expertise or specialization
• Operational excellence
• Customer service

If better data doesn't strengthen these advantages, it's not your second product.

Use Case:

A boutique consulting firm's advantage is:

• Deep expertise in a niche
• Relationships with key clients
• Reputation for quality

Better data won't change their competitive position. Better people and relationships will.

They need operational reporting, not strategic data infrastructure.

Reality Check 2: Your market moves slowly

In fast-moving markets:

• Patterns change quickly
• Prediction creates advantage
• Real-time data matters

In slow-moving markets:

• Patterns are relatively stable
• Experience matters more than prediction
• Historical data is sufficient

Use Case:

Fast-moving: E-commerce fashion retailer

• Trends change monthly
• Inventory decisions need predictive data
• Real-time demand signals matter

Slow-moving: Industrial equipment manufacturer

• Products last 10-15 years
• Customer relationships span decades
• Quarterly reporting is sufficient

Different industries, different data needs.

Reality Check 3: Scale doesn't change your model

Data as second product typically requires scale:

The economics:

• High upfront investment in data infrastructure
• Returns come from applying insights across large customer base
• Network effects require volume

If you're serving 50 customers extremely well, data probably isn't your second product.

If you're serving 50,000 customers reasonably well, it might be.

What This Means for Your Infrastructure Investment

If Data Is Operational (Most Mid-Sized Companies)

Investment approach:

Focus on ROI-driven improvements:

• Automate manual reporting (save time and reduce errors)
• Centralize data for consistency (eliminate conflicting numbers)
• Build dashboards for decision-making (speed up decisions)
• Achieve "good enough" data quality (not perfect)

Budget guideline: 1-3% of revenue

Timeline: 3-6 months to see value

Success metric: Hours saved, decisions made faster, errors reduced

Your data strategy from this blog series:

• Data Maturity Model → Level 2-3 is sufficient
• Scoping for ROI → Focus on clear payback
• Which Department First → Start with biggest pain
• Good Enough Data → Don't chase perfection

This is the right strategy for 80% of mid-sized companies.

If Data Is Strategic (Growth-Stage or Data-Driven Companies)

Investment approach:

Focus on competitive advantage:

• Build for scale and flexibility (not just current needs)
• Invest in data quality and governance (it's a product)
• Hire data team (not just outsource)
• Treat infrastructure as long-term asset
• Enable data science and advanced analytics

Budget guideline: 5-10% of revenue

Timeline: 12-24 months to build foundation

Success metric: Competitive advantages created, product improvements, market share gains

Different strategy entirely:

• You need a data team, not just infrastructure
• You're building capabilities, not just reports
• You need real-time data, not daily updates
• Data quality standards are much higher

This is right for maybe 5-10% of mid-sized companies.

The Dangerous Middle Ground

The mistake many companies make:

They're actually Level 2 (operational) but invest like they're Level 4 (strategic).

What this looks like:

• Hire a data scientist before they have clean data
• Buy enterprise data warehouse before they have basic reporting
• Invest in ML/AI before they've automated basic processes
• Build for scale they won't reach for 5 years

The result:

• Overspend on infrastructure
• Under-deliver on value
• Create complexity they can't maintain
• Lose credibility when ROI doesn't materialize

Case Study:

A 90-person B2B services company:

• Hired a data scientist ($150k)
• Bought Snowflake + Tableau ($40k/year)
• Tried to build predictive models

The reality:

• They still closed books manually in Excel
• Department heads still asked for basic reports
• The "predictions" didn't change any decisions
• Data scientist left after 18 months

They spent $300k+ trying to be Level 4 when they needed Level 2.

After the data scientist left, they hired us to:

• Centralize basic data sources
• Automate core reporting
• Build simple dashboards

Cost: $60k one-time
Value: 80 hours/month saved, reliable numbers

This was the right investment for their actual business needs.

The Decision Framework

Ask yourself honestly:

Question 1: Does better data create competitive advantage or operational efficiency?

If operational: Invest like Level 2 (ROI-focused, practical)
If competitive: Consider Level 3-4 (strategic, long-term)

Question 2: What would a 10x improvement in data capabilities do for us?

If the answer is: "We'd save a lot of time and make better decisions"
→ Data is operational

If the answer is: "We'd fundamentally change our business model and capture market share"
→ Data might be strategic

Question 3: Are our competitors investing heavily in data capabilities?

If no: You probably don't need to either (unless you want to create new advantage)

If yes: You need to understand why and decide if you're competing on data

Question 4: Can we articulate specific competitive advantages data would create?

Vague answers:

• "We'd be more data-driven"
• "We'd have better insights"
• "We'd make better decisions"

→ These suggest operational value, not strategic

Specific answers:

• "We could predict churn 60 days earlier and save 15% of our customer base"
• "We could price 10% more accurately and improve margins by 3-5 points"
• "We could reduce inventory costs by 20% with better demand forecasting"

→ These suggest potential strategic value

The Right Question to Ask

Not: "Should we invest in data infrastructure?"

But: "Is data our second product, or is it infrastructure for our first product?"

If it's infrastructure for your first product:

• Invest pragmatically
• Focus on ROI and efficiency
• Build for today's needs with modest future proofing
• This is what we help most companies do

If it's actually your second product:

• Invest strategically
• Focus on competitive advantage
• Build for scale and future capabilities
• You probably need a different partner (and higher budget)

How to Know You're Making the Right Choice

You're probably making the right choice if:

For Operational Investment:

• You can articulate clear ROI (time saved, errors reduced)
• Payback period is 12-18 months
• You're solving current pain points
• You're building for known use cases
• Budget is 1-3% of revenue

For Strategic Investment:

• You can articulate competitive advantages gained
• You're thinking 3-5 years out
• You're building capabilities, not just solving pain
• You're enabling product strategy
• Budget is 5-10% of revenue
• Leadership sees this as core to business model

The mistake is mixing these approaches:

• Operational needs + Strategic investment equal to Overspend
• Strategic needs + Operational investment equal to Under-deliver

The Bottom Line

Most mid-sized companies' data is not their second product; and that's perfectly fine.

You need:

• Operational reporting that works
• Automated processes that save time
• Reliable data for decisions

You don't need:

• Data scientists and ML engineers
• Enterprise data warehouse infrastructure
• Real-time streaming architectures
• Predictive modeling capabilities

Unless your competitive advantage actually comes from data.

The key is honest self-assessment:

Are you Amazon; where data patterns create competitive advantage?

Or are you a successful business that needs good data infrastructure to run well?

Both are valid. Both are valuable. But they require very different investments.

Most companies should optimize for operational excellence, not strategic data advantage.

Know which you are. Invest accordingly.

Trying to figure out if you need strategic data infrastructure or practical operational reporting? We help mid-sized companies make honest assessments of their data needs and build infrastructure that matches their actual business model; not their aspirations.

Let's talk →