Early in a company’s life, metrics often feel like a luxury. Teams are laser-focused on building, shipping, and selling, and data takes a backseat. But as a company grows, the absence of meaningful metrics starts to hurt. Questions like “What’s working?” and “Where should we invest?” become harder to answer. Gut instincts, once the foundation of decision-making, suddenly feel insufficient. 

The path to metrics maturity unfolds in stages. Each stage builds on the last. Metrics maturity is a framework for assessing the current state of the analytics function in your business and guiding the progression to the next stage. Each stage of metrics maturity represents a shift in how data is collected, interpreted, and applied. 

In this blog, we’ll walk through the stages of metrics maturity, helping you pinpoint your current stage, identify what’s holding you back, and map out the path to unlock your next wave of growth.

Here’s what this guide will do

1. Help you figure out where you are. You need to know your current state to know what’s next.

2. Show you what great looks like. A lot of companies don’t know what “good” analytics look like. Get a vision for what you’re aiming for.

3. Give you a plan to get there. You don’t have to reinvent the wheel. There’s a clear path to building a strong metrics foundation, and this guide will lay it out.

Who this is for

If you’re the kind of person who obsesses over what’s working and why, you’ll find this useful.

• For founders. Early on, you don’t have the luxury of wasting time or money. Metrics help you focus on the things that matter.

• For engineers and builders. If you’re designing systems, you need to think about the long term. Analytics is part of the core infrastructure.

• For decision-makers / leaders. A good metrics system tells a story. Your job is to make that story clear.

Understanding Metrics Maturity Levels

🤠 Level 0: The Wild West- Ad Hoc and Absent Metrics

At the beginning of the journey, most organizations operate in a state of data chaos. Metrics, if they exist at all, are scattered, inconsistent, and unreliable. Teams rely on intuition to guide their decisions, and while that works for a while, it’s not scalable.

Take a small startup, for example. They’re focused on shipping a minimum viable product and don’t have time—or bandwidth—to think about tracking user behavior. They can’t answer basic questions like, “How many people used our product today?” or “What features drive engagement?”

Without evidence, decisions are guesses, and there’s no way to measure whether they’re effective. To escape this stage, organizations need to start simple: pick one or two key metrics aligned with business objectives and start tracking them. Even a manual spreadsheet can be enough to get moving. The goal is to establish a habit of collecting and reviewing data.

🧱Level 1: Building Blocks- Basic and Reactive Metrics

As organizations grow, they begin collecting data in earnest, but often in silos. Marketing might track website traffic in Google Analytics, while sales keeps leads in a CRM. Reporting is manual, time-consuming, and frequently inconsistent. It’s common to hear teams argue over whose numbers are “right.”

Let’s say you’re tracking top-of-funnel website visits but failing to tie that traffic to customer conversions or revenue. Reporting takes days and involves hunting down data from different teams, leaving little time for actionable insights.

The challenges at this stage stem from fragmentation. Metrics are useful, but only within isolated teams. To move forward, organizations need to centralize their data. Most importantly, teams must standardize definitions. What, exactly, counts as a “conversion”? Aligning on these basics prevents endless debates and wasted time.

🔧Level 2: Gaining Traction- Proactive and Organized Analytics

When organizations begin treating data as a strategic asset, the shift is palpable. At this stage, companies define clear KPIs that are aligned with business goals. Data flows from a central source, and teams share dashboards that provide consistent, reliable insights.

Consider a growing e-commerce company tracking metrics like customer acquisition cost, average order value, and cart abandonment rates. These numbers inform decisions on ad spend, promotional offers, and website design. Teams are no longer reacting to problems: they’re anticipating them.

This level of maturity requires proper investment in tools and processes. BI tools are necessary, and governance policies help with consistency across the org. Training is super important at this stage, too. Not everyone is naturally data-savvy, and dashboards only work if people know how to interpret them. 

🌟Level 3: Predictive and Data-Driven Decision-Making

Proactive analytics eventually evolve into predictive capabilities. Metrics are no longer a static reflection of the past; they are tools for shaping your future. You want to be able to identify potential challenges or opportunities before they occur, enabling early intervention. For example, at this level you might use churn prediction models to identify at-risk customers based on behavior, such as reduced usage or customer service interactions. Metrics are used to trigger personalized outreach campaigns, like offering discounts or additional support, before a cancellation occurs.

Achieving this level of maturity requires significant technical investment. Organizations must build feedback loops so that predictions lead to actions, and actions improve the models. Make sure predictions lead to measurable actions, and the outcomes of these actions continuously improve the models. For example, retention campaign results feed back into churn models to refine their accuracy.

Cultural Prerequisites

Culture eats tools/technology for breakfast. You can have the best infrastructure and super high spend, but if your team doesn’t trust data- or worse, doesn’t care about it- you’re on square 0. 

• Leadership sets the tone. If your exec team isn’t asking for data in every meeting, no one else will either. It starts at the top. “What does the data say?” If the answer is “we don’t know,” that’s fine for now. But it sets the expectation that knowing is the goal.

• Kill the “I Know Better” mindset. Every company has at least one loud voice who prefers intuition over numbers. That might even be the founder. A data-driven culture doesn’t mean ignoring gut instincts. Frame it as curiosity: “If this is true, the data should back us up. Let’s find out.”

• Align on shared goals. Most companies think they’ve done this, but they haven’t. Teams optimize for their own metrics: marketing chases clicks, sales chases leads, product chases features. No one asks how those metrics connect to the bottom line. If your goals don’t roll up to a single, shared objective, like revenue or retention, you’re not aligned. Fix that first.

• If your data lives in a hundred spreadsheets scattered across Slack, you’re not ready to scale. You need a central source of truth—Snowflake, BigQuery, Redshift, take your pick. That’s your foundation.

• You don’t need every possible event logged on day one. Start with what actually moves the needle. That might be daily active users, churn rate, and trial-to-paid conversion. Add complexity as you grow, not before.

• Dirty data is worse than no data because it destroys trust. You need a process to standardize and validate what comes into your pipeline. If you’re too small to hire a dedicated data engineer, that’s fine: do it yourself. Trust me, future-you will thank you.

• Who owns the pipeline? The dashboards? The KPIs? Without clear ownership, things fall apart. For example, the data team might own infrastructure, but product owns the definition of “active user.” Write this down. Misaligned ownership creates chaos.

• Governance isn’t sexy, but it’s necessary. Set up rules for access and reporting: who can update dashboards, who gets edit permissions, and who can pull raw data. A free-for-all will burn you later when someone “accidentally” redefines churn.

• Make sure your team knows how to use the tools you already have. Teach them how to read a dashboard, interpret a trend, or run a basic SQL query. This isn’t optional. A team that doesn’t understand data will always default to instinct.

Final Thoughts

Metrics are a foundational investment that compounds over time. Start where you are, fix what’s broken, and design for scale. One common mistake is trying to skip steps — investing in advanced tools without addressing foundational issues like clean data or clear metrics definitions. Another is drowning in vanity metrics: just because you can track something doesn’t mean you should. Focus on metrics that drive meaningful decisions. Treat this as you would any core system in engineering: debug, optimize, and iterate until it works for your entire organization.

Try Preswald today!

https://github.com/StructuredLabs/preswald