What Makes An Analytics Workflow “Mature”?

When I think about maturity in analytics workflows, I think about a system that’s reliable, scalable, and trusted across the organization. A mature workflow doesn’t just meet today’s needs—it grows with the business, adapting to new challenges and consistently delivering value. It’s about more than just tools or technology. Maturity is a combination of people, processes, and data coming together to ensure that analytics workflows are both efficient and impactful.

So, how do you know when your workflow has reached that level? Let’s break it down.

Why Are We Still Answering the Same Questions?

If you’re constantly fielding the same ad hoc requests—like “What’s our ARR?”—it’s a sign your workflow has room to grow. A mature analytics workflow solves this problem by enabling stakeholders to find answers themselves. Instead of spending time recalculating or pulling the same data over and over, a scalable system ensures that the information is easily accessible.

It’s not just about building a model that works today—it’s about creating systems that scale alongside the organization.

Think about how much time you could save if stakeholders didn’t have to come to you for the basics. Maturity here means shifting from reactive to proactive. It’s not just about building a model that works today—it’s about creating systems that scale alongside the organization.

A mature data team should not be fielding the same ad-hoc requests to begin with. A platform like Sigma helps data teams distribute key metrics to stakeholders and ensure that stakeholders can run their own ad-hoc analysis. 

Who Knows Where the Data Is?

One of the biggest obstacles to a mature workflow is poor data accessibility. If someone new in sales or marketing doesn’t know where to find the data they need—or even what’s available—it creates inefficiency. A mature system makes data discoverable and usable for everyone in the organization.

Accessibility isn’t just about making data available; it’s about making it intuitive. People shouldn’t need to be data experts to explore the metrics relevant to their work. When data is easy to find and interact with, you empower teams to move faster and make better decisions.

Can Everyone Agree on the Numbers?

Building trust in data takes time, but losing it takes seconds. Imagine if two teams calculate ARR and come up with different numbers—$10 million versus $12 million. A discrepancy like that can undermine confidence across the organization.

Building trust in data takes time, but losing it takes seconds.

A mature analytics workflow prevents this by standardizing metrics and processes. When everyone uses the same definitions and calculations, you eliminate confusion and build trust. Trust isn’t just about the data itself—it’s about the systems and processes behind it.

For example, in Sigma, you can build trust by defining key metrics that the rest of the business can leverage. You can endorse workbooks so that users can easily identify the source of truth. You cut down on security and governance risks by ensuring that business users aren’t going to a dashboard just to download data to their desktops, which quickly becomes outdated data.

How Often Do You Talk to Stakeholders?

Let’s face it: analytics workflows don’t become mature in isolation. Collaboration is essential at every stage. From requirements gathering to testing and iteration, working closely with stakeholders ensures the system meets their needs and evolves with them.

Stakeholders aren’t just users—they’re partners in the process. By involving them early and often, you avoid misalignment and make sure the final product delivers exactly what they need. And when stakeholders feel heard, they’re more likely to trust and rely on the workflows you’ve built.

Iteration isn’t just about fixing problems—it’s about improving continuously.

In Sigma, you can use Live Edit to work alongside stakeholders. And because Sigma is so flexible for different skillsets, you can even write SQL in the same workbook where your business stakeholder is working in Excel-like functions. You can reference and build off each other’s work, while working in the toolset that fits your experience best.

Are You Ready to Keep Improving?

Here’s the truth: your workflow isn’t ever “done.” Maturity is about iteration. Once a model is live, the work doesn’t stop. Stakeholder needs will change, business goals will evolve, and the data itself might shift. A mature workflow is one that can adapt.

Iteration isn’t just about fixing problems—it’s about improving continuously. By keeping your workflow flexible, you ensure that it stays relevant and valuable over time.

Better Workflows, Better Decisions

A mature analytics workflow frees you up to focus on what matters. Instead of solving the same problems repeatedly, you’re building systems that are reliable, scalable, and trusted across the organization.

For me, maturity is about creating workflows that empower teams to make better decisions. It’s a process of refining, improving, and collaborating, but when you get it right, it transforms how analytics drives business outcomes.

So, ask yourself: is your workflow scalable? Accessible? Trusted? Collaborative? If the answer isn’t a resounding “yes,” take a closer look at where you can level up. A mature workflow doesn’t just meet needs—it anticipates them. And when you reach that point, you’re not just building analytics systems—you’re building the foundation for smarter decisions company-wide.