Analytics to Action

In the world of data analytics, having technical skills is only half the battle. What’s often overlooked, but equally important, is business comprehension—understanding how the business operates, how it generates revenue, and where its pain points lie. This understanding can significantly amplify the impact a data analyst has within an organization.

One key aspect of business comprehension is recognizing the flow of customers through a company and identifying how each stage of the workflow ties into different departments. For small and medium-sized businesses, many of these operational segments are often outsourced to third-party IT and SaaS (Software as a Service) vendors. These external services, while offering a solution, can also create potential bottlenecks in critical parts of the business.

For example, many companies rely on SaaS vendors for:

• Revenue collection (payment collection systems)
• Customer engagement and scheduling (appointment software)
• Business marketing (website platforms and marketing automation)
• Customer support (helpdesk and communication tools)

As a data analyst, one of your most valuable contributions is ensuring that these SaaS tools are performing as promised. While these tools often provide dashboards and OLAP (Online Analytical Processing) tools for reporting, it’s crucial to look beyond the surface to verify the data they’re presenting.

Digging Deeper into SaaS Metrics

In my experience, I’ve often found myself diving into SaaS metrics to verify whether their reported performance aligns with the reality of how the business functions. Here’s a flow that helps identify hidden inefficiencies:

1. Does the SaaS product have a supervisor or admin dashboard?
   • Check the metrics displayed—are they meaningful for how your specific business uses the software?
2. Are these industry-standard metrics representative of your business needs?
   • While SaaS vendors often use standard metrics, they may not reflect how your business actually operates.
3. Do you need more detailed metrics?
   • Consider if there’s enough granularity. For instance, is the data segmented by new vs. existing customers?
4. Is there a data definitions document for the metrics provided?
   • SaaS vendors usually release these, and it’s critical for understanding exactly what’s being measured.
5. Do the aggregate metrics match the raw data?
   • For percentage-based metrics, double-check that the numerator and denominator align with your expectations.
6. Do the current metrics match historical trends?
   • Look for anomalies in recent data—do they match long-term trends, or is there something off?
7. Are there any anomalies or potential glitches in the data?
   • Missing values, duplicate entries, or zeros where there shouldn’t be can indicate issues with the software itself.

Why These Discoveries Matter: Even Small Problems Can Have Big Impact

Often, SaaS vendors offer solutions for business bottlenecks that directly affect every single customer. When a problem with a SaaS product occurs—even if it impacts only 10% or even less than 5% of users—it’s still a big deal. Every small issue has the potential to snowball into a major operational challenge. As analysts, it’s often our role to spot these problems, even if they’re not on anyone’s radar.

In my experience, all of the biggest impacts I’ve made have come from identifying unassigned problems. These were issues no one else was looking for, but because I combined my data hard skills with business comprehension, I was able to find them. More importantly, they weren’t part of my official roadmap or responsibilities—after all, these were unknown issues.

The Crucial Step: Communicating the Problem and a Path Forward

While uncovering these issues is valuable, one of the hardest parts of the process is communicating them to stakeholders or department heads. When you bring forward a previously unidentified issue, it’s often bad news—which makes it even more critical to approach it carefully.

Sometimes, it’s best to sit on the issue for a bit and continue digging deeper for a potential solution or a roadmap to find one. When you do present a business-critical problem, you can pair it with good news: 1) we’re now aware of it, and 2) we have a roadmap for fixing it.

The goal is to position yourself as a problem solver, not just a problem identifier. No one wants to be the person who’s always pointing out what’s wrong without suggesting how to make things right. By providing both the issue and a potential solution, you’re showing that you’re committed to the long-term success of the business.

Finding Issues Below the Surface

One example that stands out is a project I worked on involving a contact center SaaS. This software handled all customer interactions, making it a critical part of the business’s operations. While at first glance everything seemed fine, after digging into the raw data, I found an issue that was impacting 10% of users—a problem that went unnoticed in the vendor’s dashboard. This discovery led to important conversations with both internal teams and the vendor, potentially improving not just how our company used the product but also influencing future product updates.

It’s these kinds of findings that can turn a data analyst’s work from routine reporting into high-impact problem-solving. While this process isn’t something typically covered in school, it’s something I’ve naturally uncovered through my experience on the job.