By Omar Abdala, Chief Data Scientist, Lotame
The most important marketing shifts all have the same thing in common: data. And the companies that have benefited the most from these shifts also share something in common: they worked on solving hard data problems before their peers did.
Early on, mobile was often dismissed as a niche channel. Identity resolution was barely discussed outside of data teams. AI was thought to be too far in the future to think about seriously. Yet each of these things now sit at the center of how marketers understand customers, activate audiences, and measure performance.
The lesson is clear: you can’t leverage data assets that you don’t have, and the most valuable competitive advantages data can bring are almost always built long before the market recognizes their importance.
Hard data problems are hard
As a data scientist I speak from firsthand experience that hard data problems are hard. They don’t have obvious answers. They’re not something you can solve in a single whiteboard session. They take time, investment, and specialized expertise to resolve. And at the outset, it’s rarely clear how much effort or resources they’ll ultimately require.
That’s why so many organizations gravitate towards “just enough” solutions. Just enough insight to hit this quarter’s KPIs. Just enough measurement to get us through this campaign. Just enough infrastructure to support today’s business needs.
The hardest problems in marketing are rarely the ones generating headlines. More often, they’re the foundational challenges underneath everything else: identity, interoperability, measurement, and data infrastructure.
These problems are expensive, time-consuming, and often invisible to end users. That’s exactly why many organizations avoid them. But history shows that companies willing to tackle these challenges early are often the ones best positioned when the market changes.
Experience compounds faster than most companies realize
One of the biggest advantages of solving difficult problems early is the experience gained along the way.
Organizations that began building identity infrastructure, data connectivity, measurement frameworks, or AI capabilities years before they become industry priorities accumulated knowledge that competitors cannot instantly replicate. Every challenge solved creates operational understanding. Every implementation reveals new limitations and opportunities. Every iteration improves performance.
Over time, these learnings compound. When a market shift finally arrives, organizations that have already spent years developing the underlying capabilities are rarely starting from scratch. They’re refining systems, improving models, and building on a foundation they’ve already established. That advantage can be difficult to see from the outside, but it becomes increasingly valuable as the market evolves.
Data patterns are built over time
In my experience, the answer to “what kind of data do we need” is often “more than we think”. There are times when you should leverage third-party data, and there are definitely times when proprietary first-party data is what’s needed. In many cases, the greatest value comes from combining multiple data sources to create a more complete view of customers and performance.
The key is recognizing that data advantages compound over time. The more history an organization has, the more context it can apply to future decisions. Long-term datasets capture changes in consumer behavior, economic cycles, media consumption patterns, and purchasing trends that simply aren’t visible in shorter time horizons.
Identity provides a useful example. Long before cookie deprecation became an industry-wide concern, organizations were already wrestling with the challenge of understanding consumers across devices and environments. The companies that invested early weren’t reacting to a trend. They were solving a difficult problem that eventually became critical for everyone else.
The same principle applies today across measurement, interoperability, and customer intelligence. The organizations building these capabilities now are creating advantages that will continue paying dividends years from today.
The AI opportunity depends on foundational data
The industry’s latest focus is AI. But AI doesn’t eliminate the need for strong data foundations. In many ways, it amplifies their importance.
As organizations deploy AI across planning, analytics, activation, and measurement, the quality of the underlying data becomes even more critical. AI can accelerate decision-making, but it cannot compensate for fragmented or bad data, disconnected systems, weak identity foundations, or incomplete customer understanding.
At its core, marketing has always been about understanding and predicting human behavior. AI has the potential to dramatically improve how organizations do that. But its effectiveness depends on the quality, scale, and interoperability of the data supporting it.
Organizations that have already invested in solving these foundational challenges will be better positioned to realize AI’s value. Those that haven’t may discover that the real obstacle isn’t the technology itself, it’s the infrastructure beneath it.
Better data creates better outcomes
When organizations solve hard data problems, they create a powerful flywheel. Better data supports better models. Better models produce better insights. Better insights improve customer outcomes. Better outcomes generate more opportunities to learn and improve.
Over time, the gap widens. For data companies, long-term investments in identity infrastructure and adaptable data strategies help organizations navigate changing technologies, shifting consumer behavior, and increasing industry fragmentation.
For brands, customer understanding becomes a strategic asset that compounds year after year. Competitors can replicate campaigns. They can copy messaging. They can adopt similar technologies. What they can’t easily replicate is years of accumulated customer learning embedded within data assets, systems, processes, and organizational knowledge.
Looking ahead
The lesson isn’t that every difficult problem is worth solving. It’s that some of the most valuable competitive advantages emerge long before the market recognizes their importance.
The organizations investing today in identity, interoperability, measurement, and AI-ready data infrastructure are positioning themselves for whatever comes next. By the time everyone agrees a problem matters, the leaders have usually been working on it for years.
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