From Data Chaos to Product Decisions

A few years ago, I found myself staring at one of the messiest datasets I had ever worked with. I was building the Global Opportunity and Velocity Index, or GOVI, to help identify Adobe's next international growth opportunities. The goal sounded simple. Figure out where Creative Cloud products like Photoshop, Illustrator, and Firefly had the greatest potential for growth.

The reality was anything but simple.

The dataset pulled together information from dozens of countries, multiple product lines, subscription trends, usage behavior, market indicators, and internal business metrics. Millions of rows. Different definitions across regions. Missing values. Duplicate records. Outliers that looked absurd until you realized they represented real user behavior.

At first glance, it felt like a data cleaning problem. Most people think dirty data is about fixing null values, standardizing formats, and removing duplicates. Those things matter. They are table stakes. The real challenge begins after the obvious issues are gone.

A clean dataset can still tell the wrong story.

That lesson changed the way I think about extracting insights from data.

When I work with Creative Cloud data today, I rarely focus on cleanliness as the end goal. I focus on trustworthiness. Those are not the same thing.

Take international usage data for Photoshop or Illustrator. On the surface, a spike in activity might look like a strong growth signal. A traditional analysis could identify that market as a priority and move on.

I have learned to be skeptical.

Sometimes that spike comes from a short-term promotion. Sometimes it comes from a small group of power users who behave very differently from the broader market. Sometimes it comes from measurement differences between regions. If I treat every increase as a meaningful signal, I end up building recommendations on noise.

This is where the real work starts.

I spend a lot of time looking for sources of bias that are hidden inside otherwise clean datasets. Outliers are one example. They can distort averages and create false narratives about market behavior. Another example is survivorship bias. If I only analyze highly engaged Creative Cloud users, I miss the people who tried a product once and never came back. Those users often reveal more about growth opportunities than the loyal customers.

The questions become more interesting than the metrics.

Why are users adopting Illustrator in one country but not another?

Why do Firefly users convert at a higher rate in one market despite lower overall awareness?

Why does engagement remain strong in a region with relatively low subscription penetration?

The answers rarely sit on the surface.

Once I trust the data, I start looking for patterns. This is my favorite part of the process because it feels less like reporting and more like detective work.

Every dataset tells a story. The challenge is figuring out which story matters.

One approach I use is comparing growth levers across markets that appear similar at first glance. Two countries may have nearly identical subscription rates, product penetration, and economic conditions. Most analyses stop there.

I want to know why one market is accelerating while the other is standing still.

That is where hidden opportunities often emerge.

I remember working through a set of Creative Cloud adoption trends where several international markets showed healthy subscription growth. Leadership was already paying attention to those regions. They were obvious success stories.

One market kept catching my eye for a different reason.

Its growth numbers looked average. Nothing stood out in the headline metrics. Most dashboards would have ignored it.

When I broke the data down further, I noticed a different pattern. New users were adopting multiple Creative Cloud products faster than almost any other region. Photoshop users were moving into Illustrator. Illustrator users were experimenting with newer offerings. Engagement depth was increasing even though overall subscriber growth remained modest.

That distinction mattered.

Most teams were focused on acquisition. The deeper signal was ecosystem adoption.

The market was not simply growing. It was maturing.

Without digging into behavioral patterns, that trend would have remained invisible. The traditional metrics suggested a stable market. The user behavior suggested an emerging growth engine.

Those are the moments I look for.

The goal is not finding a chart that moves up and to the right. The goal is understanding why it moves.

Pattern recognition becomes powerful when it reveals something unexpected. A surprising trend forces people to rethink assumptions. That is often where the biggest opportunities are hiding.

Finding the pattern is only half the job.

The other half is turning that pattern into action.

This is where many analyses fail.

I have seen brilliant research die inside a slide deck because nobody explained why the insight mattered. Data teams often assume the numbers speak for themselves. They do not.

Leadership does not need another dashboard.

Leadership needs a decision.

When I present findings, I focus heavily on the "so what" question. Every insight must connect directly to a product, growth, or investment decision.

If I discover that Firefly adoption is accelerating among users who already engage heavily with Photoshop, I do not stop there. I ask what Adobe should do differently because of that information.

Should onboarding experiences become more connected?

Should product messaging emphasize creative workflows instead of standalone features?

Should investment shift toward markets where cross-product engagement is already emerging?

Those recommendations are where data becomes strategy.

I have learned that framing matters as much as analysis. Executives are constantly evaluating tradeoffs. They are deciding where to invest resources, where to accelerate, and where to pull back. Raw findings create options. Clear recommendations create direction.

When I communicate insights, I try to reduce complexity without losing nuance. I focus on the behavior driving the outcome. I explain what is happening, why it is happening, and what action should follow.

The strongest recommendation is usually the one that feels obvious after you hear it.

That clarity comes from doing the hard work earlier in the process. It comes from questioning the data before trusting it. It comes from looking beyond averages and searching for behavioral signals. It comes from treating patterns as clues rather than answers.

The datasets I work with today are larger than ever. Creative Cloud products generate enormous amounts of information across countries, customer segments, and user journeys. The volume continues to grow.

The fundamentals remain unchanged.

Every analysis starts with a messy collection of signals. Somewhere inside that mess is a story about customer behavior. Somewhere inside that story is an opportunity to build a better product, reach a new market, or unlock growth.

My job is not to collect the data.

My job is to find the signal worth acting on.