Zero to One: How Real Businesses Win With Data

Many small businesses run on instinct. But a single, well-chosen metric can unlock surprising gains—in revenue, alignment, and decision speed. Here’s how to start.

This is the first post in what I hope will become a growing series. If you don’t know me (hi!) I’m Sean and I’m a data person currently working in client analytics at BDC—my background spans engineering, consulting, and (of course) data. I started this blog to break down how real businesses can make data work—not just in theory, but on the ground.

The idea for this piece came from a conversation with my sister. She runs HR as a one-person department at a multi-location agricultural equipment dealership in Alberta (we’ll call them Ag Co.).

Her business is successful, well-run, and growing—but when I asked how they track performance across branches, she said something I’ve heard more than once:

“Honestly? We mostly go by feel.”

That struck me—because places like hers are exactly the kind that could benefit from a simple, well-structured data project. But without a clear trigger or pain point, it’s easy to stay in that “gut feel” mode.

And they’re not alone. In Canada, 98% of employer businesses are small (fewer than 100 employees), and globally, about 70% of SMEs haven’t adopted formal analytics, relying instead on spreadsheets, habit, and intuition (ISED Canada, 2024; Straits Times, 2023).

This post is for them. If you run a business that’s doing well—but think data is too complex, expensive, or just not for you—this post lays out what it really means to go from zero to one.

If you’re wondering what that first step might look like, we’ll break it down—one practical stage at a time. Where to begin, what to track, and how small shifts can drive real results.

Step 1: You Don’t Need a Platform—You Need a Question

One of the most common misconceptions I see—especially in small and mid-sized businesses—is the belief that you need a system before you can start. But meaningful data work doesn’t begin with tools. It starts with a question worth answering:

• Which sales reps are generating the most margin—not just volume?

• Which products sit unsold the longest?

• Are technician hours actually translating into revenue?
  
  

At Ag Co., a leader once asked: “How much money did this employee make us last year?” But no one could answer—not because the data wasn’t there, but because no one had structured or connected it.

This is incredibly common. A global review by the OECD found that the real barrier to analytics adoption in SMEs isn’t data availability—it’s knowing how to start turning raw numbers into insight (OECD, 2022).

“The main barrier to analytics in SMEs isn’t data access—it’s knowing where to start.”
— OECD SME & Entrepreneurship Papers, 2022

Step 2: What Does “Zero to One” Look Like?

“Zero” doesn’t mean doing nothing. It means your business is running on gut feel, email threads, siloed spreadsheets, and ad-hoc knowledge sharing. It’s not broken—it’s at risk.

“Zero to one” means introducing just enough structure to make part of your operation measurable, repeatable, or explainable. Not everything—just one thing that matters.

Here’s a simple framework:

Most SMEs operate in the first two stages—and that’s completely fine (Younis et al., 2022; Mohammad et al., 2020). The goal isn’t to jump to advanced analytics. It’s to match your level of structure to the decisions you’re already making.

At Ag Co., sales reps manage hundreds of customers—but entirely from memory or fragmented excel sheets. They have an ERP, but:

“I’ve never heard them once say, ‘I entered my data in the pipeline.’ It’s not like that.”

And that’s the real issue. The tool exists—but no one’s using it, because there’s no shared reason to. That’s where a lot of data efforts go wrong—not with the tech, but with the why.

Research backs this up: analytics only drives value when it’s embedded in how decisions get made. Adoption for compliance—checking a box or logging activity—doesn’t move the needle (Younis et al., 2022).

“SMEs gain the most from data when leadership is involved in shaping how analytics is used—not just buying tools.”
— Younis et al., 2022

Step 3: Challenges and Opportunities—What I Observed

After talking with Rachel, here’s how I’d sum up the state of things—not just as a sibling, but as a data professional who’s worked with organizations at different stages of maturity.

🔻 Core Challenges

• Knowledge is siloed in people’s heads.

• Metrics exist, but they’re not connected.

• The ERP holds data—but it isn’t trusted or used consistently.

• Strategy is reactive.

These aren’t edge cases—they’re the norm. A 2020 review found that most SMEs are still operating at the early stages of data maturity, focused on descriptive (“what happened?”) or diagnostic (“why did it happen?”) questions. Few have progressed to predictive or prescriptive analytics—and that’s okay (Mohammad et al., 2020).

Right now, Ag Co. sits squarely in the descriptive phase. There’s valuable data—margin, labor hours, customer activity—but little of it is tied together, shared, or used to guide decisions.

✅ Practical Opportunities

• Track technician utilization.

• Link margin to sales rep activity.

• Introducing churn tracking.

• Capture relationship context.

Framing analytics as a progression—rather than a transformation—helps teams take the first step.

Step 4: A Parallel Case Study—Field Service Metrics in Action

A small HVAC company with four branches and around 40 technicians had been operating much like Rachel’s dealership—relying on verbal updates, instinct, and gut feel. No dashboards. No analysts. Just good people trying to keep up.

Then they made one change: they started tracking two simple metrics with data they already had:

1. Technician utilization rate (hours billed ÷ hours worked)

2. Quote-to-close ratio, by branch.

Within six months, those two numbers helped them:

• Spot underperforming routes

• Reassign technicians more effectively

• Double their quote follow-up rate
  
  

The result? A 12% lift in monthly service revenue and a 7-point increase in customer satisfaction—without hiring anyone new.

“We didn’t need a new system. We just needed to see what was already happening.”

This isn’t an outlier. Research shows that even simple metrics, when tied to meaningful questions, can unlock real business value (Mohammad et al., 2020; OECD, 2022).

Step 5: How to Start—Even Without a System

Still relying on spreadsheets, emails, or memory? That’s fine. You can start building insight from where you already are.

Here’s what I recommend:

Log what you already touch
Start capturing fields like lead source, quote date, or close outcome. Even partial data builds visibility.

Set a simple monthly review
Once a month, take 30 minutes to ask:

• What’s selling fastest?

• Who’s converting best?

• Where are we losing time or margin?
  
  

Protect what lives in people’s heads
Use a basic CRM or shared doc to document relationship context before it walks out the door.

“Even basic Excel dashboards can unlock insight—if they’re built around questions the business already cares about.”
— Adapted from Mohammad et al., 2020

Common Objections, Answered

If you’re still not sure this applies to your business, here’s what I hear most often:

“We’re too small for data.”
You don’t need a full system. You need one metric that helps you manage what already matters.

“We don’t have time or tools.”
Start with what you already use—email, Excel, whiteboards. Build structure around what’s familiar.

“It feels like more overhead.”
The goal isn’t to track everything—just to make a few decisions easier and faster.

“Analytics enables—but doesn’t guarantee—strategic thinking. The difference is whether insights are acted on.”
— OECD, 2022

Final Thought: Start With One Question

All of this starts with curiosity:

• What’s actually working?

• Where are we slipping?

• How do we repeat the best month?

You probably already have pieces of the answer. The next step is just connecting them.

“The question should not be: ‘How do we implement analytics?’ but rather: ‘What do we want to know—and can analytics help us find out?’”
— Rabe et al., 2023

Want help figuring out where to start? I offer free 15-minute discovery sessions. No pitch—just a conversation to see if there’s a win worth uncovering together.

Let’s talk!

References

1. OECD (2022). Data Analytics in SMEs: SME & Entrepreneurship Papers No. 28. OECD Publishing.

2. Mohammad, S. et al. (2020). Data Analytics in Small and Medium Enterprises: A Systematic Review. ResearchGate.
   Ogbuokiri, B. et al. (2015). Implementing Big Data Analytics for SME Regional Growth. IOSR Journal of Computer Engineering.

3. Younis, A. et al. (2022). Big Data Analytics Capability and Firm Performance: Evidence from SMEs. Businesses, 2(4), 38. MDPI.

4. Rabe, M. et al. (2023). Transforming Small Businesses through Analytics: Boosting Sales, Customer Engagement, and Brand Value. ResearchGate.

About the author

Sean Clarke works at the intersection of data science, business strategy, and analytics enablement. With a background in engineering, consulting, and applied analytics, he helps organizations turn raw data into practical decisions. This blog is where he shares lessons learned, project insights, and no-fluff takes on real-world data work.