Dads Talk Data

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Being data-driven isn’t about tools, dashboards, or technology stacks.

In this episode, we talk about data culture — and why people, leadership, and shared understanding matter more than any platform. We discuss what successful data-driven organizations have in common, how teams become motivated (not just informed) by data, and why ownership and clear outcomes are the real foundations.

Technology enables.
 Data informs.
 But culture is what actually makes it work.

If you’ve ever wondered why great tools still don’t lead to better decisions, this episode is for you.

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Roughly half of marketing activity can’t be tracked properly — yet we still argue about attribution models.

In this episode, we talk about Adobe Mix Modeler (AMM) and why marketing mix modeling is suddenly relevant again. Not as a replacement for attribution, but as a way to answer a much more practical question:
Where should you spend your next marketing dollar?

We discuss how AMM works, why it doesn’t care about UTMs or cookies, and how combining aggregated data with real events can surface impact you’d otherwise miss.

Attribution is still useful. But when tracking breaks down, this is often where the real conversation starts.

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This episode is a K/C-asper overload — in the best way. We’re joined by Kasper Rasmussen, co-founder of Accutics, to unpack a concept that sounds fluffy… until you realize it’s the missing ingredient behind almost every “data-driven” ambition: a shared data language.

We break down what it actually means in practice (hint: it’s not just a taxonomy or a spreadsheet). A shared data language connects everything from briefing and naming conventions inside platforms, to URL tagging, reporting structures, documentation, governance, and—most importantly—ownership.

We also tackle the uncomfortable truth behind why data trust keeps collapsing in large organizations: the numbers will never match across platforms, and that’s not a bug — it’s two different truths used for two different purposes. The real problem is when companies don’t agree on which truth belongs where, and end up arguing instead of optimizing.

If you work in a multi-market setup, with agencies, hundreds of campaigns, and “data-driven” as a board-level slogan — this is your blueprint for turning it into reality.

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In this episode, we unpack one of the most misunderstood words in modern marketing: maturity.

Based on real-world insights from an Adobe Target event, we talk about why most organizations are stuck at “one-to-many” personalization — and why that might not actually be a failure. We explore the personalization maturity ladder, what’s really holding teams back from moving up (spoiler: it’s not the tech), and why lack of executive sponsorship, siloed data, and unclear strategy beat “missing features” every time.

We also challenge the idea that one-to-one personalization is always the end goal. Sometimes, the problem isn’t execution — it’s that no one has agreed on what success actually looks like. Buying the tool turned out to be the easy part. Adoption, outcomes, and organizational alignment? That’s where things fall apart.

As always, we circle back to a familiar theme: you don’t get fit by buying a gym membership — and you don’t get mature by buying software.

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In this episode, we take on one of marketing’s most uncomfortable topics: ROI — and why the way we calculate it is fundamentally broken.

We talk about why marketing numbers get recalculated (or quietly dismissed) the moment they hit the CFO’s desk, and why attribution debates, channel wars, and “soft metrics” aren’t the real problem. The real issue is data quality — or rather, the lack of it. We unpack a simple but uncomfortable idea: if only 50% of your data is trustworthy, then only 50% of your reported ROI should be trusted as well.

From paid media tracking to campaign tagging and analytics foundations, we argue that data quality isn’t a hygiene task or an IT problem — it’s a core input to the ROI formula. Fix that, and marketing suddenly becomes a lot harder to argue with. Ignore it, and no spreadsheet will ever save you.

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In this longer CMO edition, we’re joined by Frederik Grønlund Scholten, CMO (VP Marketing) at TV 2 Denmark, to talk about what actually matters when you step into a new CMO role.

We dig into why the first 90 days shouldn’t be a template exercise - but a deliberate phase of listening, learning the culture, and building trust across the organization. Frederik shares his view of the CMO as the company’s connector and orchestrator, and why aligning value creation across teams is the only real way to avoid “data wars” and silo truth.

We also get concrete on the mechanics: evidence over opinions, the long-term vs short-term balance (60/40), why emotional branding works when it’s tied to business logic, and why most companies don’t need more martech - they need to get serious about use cases, outcomes, and the tech they already paid for. 

We wrap with leadership lessons, team-building, and why “hurry up slowly” might be the most underrated career advice in marketing.

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In this episode, we explore the shift from “web analytics” to what we’re calling experience analytics - and why moving from Adobe Analytics to Customer Journey Analytics (CJA) shouldn’t be treated as a simple one-to-one migration.

We talk about the real value of CJA: stitching online and offline data to finally see the full customer journey across web, call center, and physical stores. But we also get honest about what it takes to succeed: clear use cases, organizational maturity, shared ownership across teams, and a solid identity strategy for stitching data together.

Our takeaway is simple: don’t upgrade for the sake of features or renewals. Upgrade with a plan - and treat it as a business transformation, not a technical migration.

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In this episode, we challenge one of the most overused (and unhelpful) phrases in data: “garbage in, garbage out.”

We talk about how a negative data narrative can actually sabotage data-driven ambitions, and why constantly focusing on what’s broken creates friction instead of progress. Instead, we argue for a mindset shift: focusing on the data we trust - our gold data - and building from there.

We discuss why data should be prioritized early in projects (not rushed in at the end), why “big data” is often the wrong goal, and how trusted data is the real foundation for ROI, AI, and business impact. The takeaway? Stop collecting everything. Start collecting what matters - and talk about data as an enabler, not a problem.

Garbage in, garbage out is officially dead. Long live gold in, gold out.

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In this episode, we dive into Adobe’s new AI agents in Customer Journey Analytics and what they actually mean for businesses - beyond the hype.

We talk about how AI agents can democratize analytics, automate reporting, and free up time for real analysis. But we also get brutally honest about the elephant in the room: data quality, governance, and trust. Because AI doesn’t fix bad data - it just scales the problem.

We discuss AI as a workflow enabler vs. AI as a business driver, why most AI projects die in pilot, and why executives, not tools, need to take ownership if AI is going to create real business value. Also: one of us is officially declared a “grumpy old man” when it comes to AI. We stand by it.

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Why do different teams in the same company end up with different “truths” in the data - and why does it turn into a political battle?

In this episode, we unpack “data wars”: the clash between optimization truth (marketing performance) and business truth (actual outcomes). We talk about why marketing, IT, data teams and executives often operate in silos, why dashboards don’t add up, and how “single source of truth” is more myth than reality.

We dive into shared data language, integration challenges, compliance concerns, and why business value - not tools or politics - should be the starting point. And yes, there are dad jokes. Data quality starts with honesty.