To date, much of my career has involved helping brands and agencies connect their disparate data and turn it into intelligence. With recent developments in AI, it would be normal to assume that this goal has become easier to achieve.
Whilst AI has heightened expectations for marketing teams, many still grapple with fundamental challenges. Evidence suggests that almost half of marketing data isn’t ready to support the AI ambitions of marketing teams. Data is still silo-ed, lives in too many places, definitions don’t match, and trust in reporting remains shaky.
At DMEXCO recently, I joined Mariama Kamanda, Associate Director of Data and Analytics at Barilla Group, to explore how marketers can move from scattered facts to genuine intelligence. Our goal wasn’t to talk about tools. It was to show how to build the kind of foundation that enables AI to actually work. In this article, I’d like to share some of the insights we raised from our session.
Understanding the journey: data, information, knowledge, intelligence
Marketers know what raw data looks like. It’s disorganized, inconsistent, and spread across many platforms and sources. The real challenge is turning that disparate data into something usable. The framework I use to explain this evolution is simple: data, information, knowledge, intelligence.
It starts with getting the basics right. Identifying the data that you really need. It's tempting to want to bring all of your data together because having more data feels like the right starting place. But starting with the end business goal in mind, work back and identify the sources and types of data that you really need. Then, connecting and cleaning your data is critical so that you can trust what you’re looking at.
From there, you create information by harmonizing and visualizing that data, making it consistent and easy to interpret. The next step is knowledge. Understanding not just what happened, but why. This is done by combining analytics with business context. Finally, intelligence is where those insights start to power proactive and forward-looking decisions. Overall, this approach allows teams to act faster and with more confidence as they can trust the data they are making decisions on.
Intelligence is ultimately about making proactive, strategic, and often automated decisions that amplify the impact of the teams you already have. Don’t worry about solving every business use case at once. Think progress over perfection. Every organization starts from a different place, but the process is similar. Data, information, knowledge, intelligence.
Before marketers can move through those stages, though, they need to tackle the biggest obstacle of all: fragmentation. As described by Mariama.
Breaking silos and fixing the foundation
Mariama clarifies that the most essential part of this process for her is addressing data fragmentation. “When it comes to putting the right types of data together to tell one homogenous or cohesive story, it’s very difficult.” Marketing data sits in one system, sales in another, and no one can see the full picture. I see this all the time in my own work. Most marketing teams underestimate how much hidden complexity sits in their data until they try to connect it.
Mariama also flags a newer challenge: AI readiness. “I need good data to have insights and then overlay it with the AI. Because if everything is really bad downstream, how can I possibly tell a story?” I couldn’t agree more. AI readiness starts long before you introduce AI. It begins with the quality, structure, and accessibility of your data. Without that foundation, even the most advanced models will fail to deliver useful insights.
That tension led Barilla to focus first on standardizing its data collection. The project began with marketing mix modeling and quickly exposed how inconsistent naming and ownership can slow everything down. “If you’re working for a global company and you’re working with multiple agencies, the way in which you name one campaign that might be the same in every single region is different.”
The fix was methodical. “Every campaign has a standardized taxonomy irrespective of brand, irrespective of region.” Within a year, Barilla had connected its global datasets and could finally analyze ROI consistently. That consistency changed how people made decisions.
Turning clean data into confident decisions
Standardization unlocked something more important than dashboards: alignment. As Mariama puts it, “The reality is, data should really be a catalyst to give you a solution and an answer to a business problem.” When everyone works from the same source of truth, decisions become consistent and defensible.
She credits the impact to both process and communication. To get buy-in, her team treated internal alignment like a campaign. They framed the changes in familiar, marketing-friendly language, showing how better naming and taxonomy would make everyone’s work easier and the results more measurable. As she explains, “We marketed back to them.”
Once the purpose was clear, teams understood why consistency mattered, and compliance followed naturally. With reliable data in place, the analytics team could move from describing results to explaining causes, and eventually predicting them.
With the data foundation in place, the next challenge was ensuring those improvements stuck and that people across the business could use them confidently.
The principles that keep transformation real
From experience, sustainable change depends on clarity and culture. Mariama’s first tip, “starting with the business problem, and not necessarily starting with the technology,” is one every marketer should live by. It avoids the trap of chasing features that don’t solve real issues.
Her second piece of advice gets to the heart of transformation, which is creating space for people to learn. “Do people feel safe enough to even say that they don’t know? Because if they say they don’t know, it’s easier to foster that culture, foster the trust, and then you can move on to things like literacy and curiosity.” I completely agree with Mariama that sustainable change depends on clarity and culture. The technology can only take you so far; real transformation happens when teams understand why change matters and feel safe to ask questions and make mistakes.
And as AI becomes more embedded in day-to-day workflows, bringing your data and marketing teams closer together is critical to continue to innovate, and for both teams to understand the end business goals and value.
Keeping control as AI takes over more of the work
AI can help automate analysis and speed up reporting, but it doesn’t replace human judgment. Its real value lies in helping teams move faster, not in removing them from the process. As Mariama clarifies, “It’s not a replacement. It should always be a process enhancer or a catalyst.”
That requires structure and ownership. AI models need the same discipline as any other part of the data stack. Now more than ever, marketing teams need to be aware of the value of clear data governance, maintenance, and oversight. And as Mariama warns, accuracy still depends on the inputs. “If you put rubbish in, you’re going to get rubbish out, and then it becomes quite dangerous.”
For me, this is what modern data governance looks like. Guardrails are no longer just about quality control. They’re about accountability. AI can scale good practices or amplify poor ones, depending on how it’s managed.
Final thoughts: progress over perfection
The biggest takeaway from this session was a sense of pragmatism. What Mariama described at Barilla reflects what I’ve seen across countless organizations. There’s no instant leap from data chaos to AI-driven marketing. Progress happens gradually, and at each point of the journey, teams have to make choices. Those choices start small, like naming campaigns consistently, and gradually grow into building trust in the data and evangelizing the benefits. Over time, they add up to the confidence marketers need to act fast without guessing.
For teams ready to take the next step, the companion ebook From Data to Intelligence: A Practical Guide to Turning Marketing Data into Strategic Advantage expands on every stage we discussed. It’s a practical roadmap for moving from scattered facts to genuine intelligence, free to read, no form required.
About Mariama Kamanda
Associate Director of Data and Analytics at Barilla Group. Mariama specializes in building data infrastructure and culture for global consumer brands. Her team connects marketing, sales, and customer data to measure ROI, enable predictive modeling, and foster data literacy across the organization.
