That line may sound like an incredible stretch, but consider the shifts that have happened in professional hockey over the last decade and a half. Improved technology has made data collection in sports more common, and teams have – often begrudgingly – gotten better at aggregating and reading out on stats. What differentiates the winners, however, is using that information to make decisions faster and more consistently than their competitors.
And that shift isn’t unique to sports.
Today, every industry is operating in a period of rapid change: AI acceleration, shifting platforms, tighter privacy expectations, economic uncertainty, and constant pressure to do more with less. In that environment, the companies that win aren’t necessarily the ones with the most data, but rather the ones who can translate data into action with confidence.
As a data engineer at TEAM LEWIS (and a former NHL writer), I see the same pattern playing out across marketing, customer experience, and sports: when data becomes operational, not just informational, it becomes a defining competitive advantage.
From hockey blogs to front offices: a quick personal story
Before I worked in data engineering, I covered the NHL semi-professionally from 2008–2014. I ran Stanley Cup of Chowder (SB Nation’s Bruins site), and I was part of a broader community of writers and bloggers who largely enjoyed applying data analytics to their storytelling in various ways:
- Evaluating draft strategy beyond “the eye test”
- Identifying undervalued players
- Diagnosing team weaknesses with evidence
- Separating what was happening from what people thought was happening
One of the strongest voices in the blogger space during that time was Eric Tulsky, a chemist by trade who wrote for SB Nation’s Broad Street Hockey (Philadelphia Flyers). He has always been an advocate of using analytics to paint a broader picture of what was happening on the ice, even when some of the more advanced analytics for the time (and the people writing about them) were frequently dismissed by NHL front offices and the mainstream media.
In the mid-2010s, things started to shift. Teams began creating designated analytics roles, and in many of those cases hired former bloggers with proven experience. The Carolina Hurricanes hired Tulsky in one of these roles in 2015; after clearly proving the value of his work for several years, he was moved to a team management role in 2020. In 2024 he was made the General Manager of the Hurricanes. Tulsky spent two years applying these skills to the reconstruction of a very average NHL team – and on June 14, Tulsky and the Hurricanes won the Stanley Cup for the first time since 2006.
This journey is proof positive of what happens when an organization takes data-backed storytelling seriously; analytics – and the data behind it – has moved from “interesting” to “essential.”
The real takeaway: insight isn’t the finish line
Hockey didn’t change because teams suddenly discovered math. It changed because teams built the capability to do three things with consistency:
- Capture signals(what happened?)
- Interpret signals(why did it happen?)
- Act on signals(what should we do next?)
Many organizations are strong at step one. Plenty are improving at step two.
But the true differentiator right now is step three.
Business doesn’t benefit from the “why” until it changes direction: reallocating spend, shifting messaging, adjusting channel mix, refining audience strategy, improving conversion paths, fixing operational issues, or changing what you prioritize.
In other words: the value isn’t “a dashboard.” The value is a better decision made more rapidly.
Why a strong data layer is the unsung MVP
The piece that doesn’t get enough attention in the rush towards AI and automation is good data.
If your underlying data is inconsistent, unclear, or untrustworthy, speed doesn’t help; you simply get to the wrong conclusion faster.
That’s why the most important work often happens underneath the surface to implement a strong data layer that creates trust.
For clients, “a strong data layer” typically means:
- Clear, consistent definitions(What is a lead? What counts as qualified? What’s included in revenue?)
- Standardized transformations(so numbers don’t change depending on who pulled the report)
- Lineage and auditability (so you can explain where metrics came from)
- Repeatable pipelines(so reporting isn’t a recurring scramble)
- Governance that’s practical(enough structure to build confidence, without slowing the business down)
When this foundation is in place, teams spend less time debating the numbers and more time acting on them.
That’s the business equivalent of hockey’s shift from “basic points numbers + opinion” to measurable, repeatable decision-making.
Translating data into stories people can act on
Even with a strong data foundation, one challenge remains: most stakeholders don’t make decisions based on technical nuance. They make decisions based on what they understand and trust.
A major part of modern data work is translation. This means turning complex signals into a narrative that makes sense to the “common person,” without oversimplifying what matters.
In practice, that means building a workflow from:
raw events → validated metrics → clear story → action → measurement → iteration
This is where analytics becomes genuinely useful. It helps you answer questions that matter:
- What changed, and where?
- What’s driving it?
- What’s the impact on pipeline or revenue?
- What should we do next week—differently?
The goal isn’t to drown teams in numbers, but rather to give them a clearer window into what’s happening and the confidence to respond.
AI acceleration makes data engineering more important, not less
AI can help teams move faster. It can surface patterns. It can summarize performance. It can even suggest next steps.
AI also increases the cost of poor data.
If definitions are inconsistent, AI can confidently reinforce the wrong story. If your inputs are messy, the outputs will be messy; they will just be packaged more persuasively. If the organization can’t trace metrics back to their source, it becomes difficult to defend decisions when leadership asks, “Where did this come from?”
A simple way to put it:
AI raises the ceiling. Data engineering raises the floor.
And in a high-velocity environment, the floor is what keeps teams stable.
What this means for clients right now
Most organizations we speak to aren’t short on data. They’re short on:
- Confidence in the numbers
- Consistency across platforms and teams
- Speed in getting answers stakeholders trust
- Clarity on what actions to take
- Repeatability in reporting and decision-making
When those gaps exist, it shows up in predictable ways: reporting becomes manual, dashboards multiply, definitions drift, and stakeholders stop trusting what they’re seeing.
The opportunity is to build a data foundation and operating rhythm that supports the business—not just the reporting function.
Because in today’s market, being “data-driven” isn’t about having more charts. It’s about being able to respond with agility when something changes.
Let’s make data actionable—not just available
If your team is navigating any of the following:
- Reporting that’s still heavily manual
- Dashboards that don’t align across teams
- Multiple sources telling different “truths”
- Pressure to apply AI without confidence in upstream data
- A need to turn insights into faster, clearer actions
TEAM LEWIS can help you build the data layer and reporting foundation that turns analytics into operational advantage – even if the Stanley Cup isn’t in your KPIs.
If you’d like to talk about your current setup and what it would take to make your data more trustworthy, more usable, and more actionable: get in touch.
Contact TEAM LEWIS to start a conversation about building a modern data foundation that keeps pace with change.
