From friction to flow: A marketer’s guide to partnering with your data team

One of the most important collaborations in customer experience is the partnership between data and marketing teams. After all, there’s no “data-driven marketing” without data. In my own experience as eCommerce and marketing leader, my data partners have been some of my most important collaborators, helping my team and I to both understand our customers better and mobilize those insights into true 1:1 experiences.

And yet, I’ve also witnessed marketing / data relationships that were strained and ineffective. Marketing teams that felt slowed down by data team timelines, and data teams that felt reduced to “report pullers,” saddled with tactical asks instead of being viewed as strategic partners. 

The stakes of this misalignment are high: missed opportunities, slower decisions, wasted spend. In a world where customer expectations evolve daily and competitive advantage is often won by speed AND precision, no company can afford friction between these two critical functions.

The good news is that it doesn’t need to be this way. There are a handful of blockers that get in the way of an effective partnership, and all are addressable. This article explores the "why" and the "what" at the heart of this: Why these cross-functional alignments happen and What marketers can do to address those factors. Along the way, we’ll look at real-world examples, practical frameworks, and the role that modern martech tools (from CDPs to AI) can play in making the partnership hum.

Why marketing and data teams are often misaligned  

To fix the disconnect between data and marketing, we first need to understand the root causes. In my experience there are a handful of common ones.  

1. Differing mandates  

On the surface, marketing and data should be perfectly aligned: both exist to leverage insights that improve the business. But in practice, their priorities diverge. Marketing focuses on speed and iteration, using rapid testing cycles to capitalize on evolving markets and gauging success in base growth and revenue. Data teams focus on precision and structure, with success measured in accuracy and stability.

This creates the classic “fast vs. accurate” tension. Marketing pushes to launch in days. data prefers more time for model validation and testing protocols. Both are right in their context, but without alignment, each can feel the other is an obstacle.

2. Timeline expectations

Modern marketers live in days and weeks, driven by aggressive campaign calendars and test-and-learn cycles. data teams often work in weeks and months, driven by structural design, thorough validation, and robust governance. Ironically, those same safeguards are what ensure the data reliability that marketing depends on.

When expectations collide, frustration builds. Marketers complain data is too slow. Analysts see marketing as reckless. Without a shared rhythm, collaboration stalls.

3. Language barriers

Every business discipline has its own lexicon. Marketing talks brand, funnels, and storytelling. data teams talk models, regressions, and confidence intervals. Each side’s jargon can feel opaque to the other, creating artificial barriers when they try to compare notes.

The problem isn’t just language; it’s cultural. Marketing often prioritizes creativity and narrative. data prioritizes precision and statistical truth. Neither is wrong, but the disconnect can foster a sense that the two teams are talking past each other.

4. The “request vs partnership” trap

One of the biggest sources of tension is how requests are framed: “Can you pull me a list?” or “Can you generate a report?” Unsurprisingly, analysts feel undervalued when reduced to servicing tickets instead of helping shape strategy.

The irony? Your data team can be a powerful strategic partner when you include them in the planning. Most data professionals want to solve complex problems, not just extract CSVs. And their deep knowledge of the data can help unlock insights you didn’t think to ask for.

Why all of this matters  

The cost of an ineffective marketing / data partnership goes beyond tense relationships and messy email threads; it results in lost opportunities and real business impact. Even mild misalignment limits what the partnership could achieve.

Poor targeting can waste ad spend, leaving growth on the table. Campaigns fall behind competitors because targeting analyses take too long. New datasets get delayed because requirements are either undefined or over-defined to the point of paralysis.

Ultimately, trust erodes. And with it, go both speed and collaboration

How to build a high-performing partnership  

The good news is that this relationship doesn’t have to be a tug-of-war. With the right practices, the marketing / data partnership can become a powerful strategic engine.

So let’s look at some of these practices:

Shift the relationship from service desk to strategic partner

The first, and perhaps most important, mindset adjustment is for marketers to avoid treating the data team as a service desk. Instead:

Learn data as a second language

In an age of data-driven decision-making, modern marketers need to be as comfortable with data concepts as they are with brand concepts. The more you understand how data works and how to talk about it, the easier it will be to frame your questions and requests in a way that the data team can respond to. And once that happens, barriers give way to partnership.

For example, a marketer may be interested in testing different creatives, but they’ll benefit from understanding test cell sizing and the parameters of statistical significance to ensure that the data team can analyze the results with the reliability and timeliness they’re looking for.  

Or a marketer may be looking to quantify the stages of the customer journey, from email open through purchase. But to achieve that, their analyst partner may need to combine (or “join” in data-speak) a few different “event tables.” Just understanding that extra layer of complexity allows you to talk through the challenges and get to a solution. It may even result in modifications that will make similar analysis easier in the future. 

In its simplest form, marketers think in “stories” (profiles, audiences, journeys) and Analysts think in “structures” (tables, rows, events). But data is foundational to both. And an expanded vocabulary builds on that foundation.

Define clear roles and expectations

In arenas from sports to the military, high-functioning teams are built on an understanding of roles (who does what) and expectations (when and where they do it).

Business is no different, which underscores the importance of marketing and data teams being aligned on each other’s roles and expectations.

Who drives test definition? Segmentation rules? Data governance? Without clarity on these, both sides will duplicate effort, or worse, expect the other team to do it and drop the ball.

Equally important is aligning on SLAs, or service level agreements, which is a fancy way of saying “define timelines for deliverables.” In our world of multi-tasking and competing priorities, “as soon as possible” is simply not sufficient for either a request or a promise. Rather, effective teams establish expectations around when requests will be reviewed and engage in a respectful negotiation around practical timelines for both sides.

Align on the metrics that matter

At the end of the day (or quarter), business outcomes are what matter, and both teams should rally around the goals and metrics that drive those outcomes. That means structuring analytics around key metrics like customer acquisition cost (CAC), lifetime value (LTV), average order value (AOV), and average revenue per user (ARPU). Other metrics like impressions, click-through rates, and conversion are important diagnostic tools, but both teams should agree on where they fit in the hierarchy of campaign analysis.

Equally important, both teams should agree on the one primary “success metric” for a campaign or program. In some cases, it may be conversion, but in others it may be AOV. Crisp analysis and decision-making rely on a clear “north star” metric that will determine success for each campaign, test, or pilot program.

And lastly, both teams should agree on a “source of truth” for analysis and results. Even if the marketing team runs its own analysis, the data should come from a source that has been blessed by its data partners. Nothing erodes trust faster throughout the organization than two analyses or dashboards that show different numbers.

Build rituals and celebrate wins

Effective collaborations don’t happen overnight. They require structure and repetition to build muscle memory and reinforce behaviors. Here are a few elements that I’ve found to be effective tools in the marketing / data partnership:

• Weekly syncs between marketing and data: These ensure that key projects are moving forward, provide a forum for surfacing and resolving blockers, and enable reprioritization when required.
• Joint retros for big campaigns: Tying inputs to outcomes is one of the most effective ways to motivate people. Sharing the revenue or customer engagement that came from the data team’s segmentation schema or pro forma analysis both motivates them and crystallizes the marketing partnership. 
• Co-presenting results to leadership: When marketing and data teams are aligned in their presentations to the executive team, it sends the signal that they are in it together and have each other’s backs. As an added bonus, it gives the executive team greater confidence in the numbers being shared. 
• Celebrate wins together: Strong collaborations are powered by collective wins. By celebrating successful outcomes together it strengthens the bonds between both the teams and the individuals among them.

Where modern martech can help

Technology can’t fix a poor relationship, but it can provide the kind of sound foundation that good partnerships are built upon. And there are three key capabilities to consider when powering the marketing / data partnership.

CDPs as the bridge between marketing and data

A CDP (customer data platform) unifies customer data into a single source of truth, transforming raw warehouse data into marketing-ready datasets and unified customer profiles. In doing this, it centralizes, cleans, and standardizes multiple data sources, resulting in a database that both teams can trust.

This “democratizes the data” by making it more accessible to the marketing team, without sacrificing the rigors of data governance the Data team requires. Marketers can define and size segments without pulling analysts into every request. Analysts can empower marketers by setting up datasets applicable for multiple use cases, rather than fielding tickets for every segmentation request.

For example, marketers want to launch a win-back campaign. Without a CDP, back-and-forth is required to define “churned.” With a CDP, “churned” is a standardized audience segment, and is adjustable with additional filters and variables set up by the data lead and controlled by the marketer.

This shift turns conversations from “Can you pull me a list?” to “How should we define and govern this segment together?”

Engagement platforms leveraging CDP inputs

Once a CDP is established as the source of truth for customer data, marketers can set up event triggers that automatically send audience segments and supporting data to engagement platforms that drive marketing-defined campaigns and customer communications. These platforms can drive activation on paid media (Meta, Google, Tradedesk), CRM channels such as email and SMS, and even direct mail and Customer Care outreach. 

If the engagement platform is rich enough with channel integrations, it can even enable multi-channel communications and customer journeys. And with a CDP (like Simon AI) supplying rich data feeds, the emails, SMS messages and other touchpoints in those journeys can be highly customized. 

For example, a customer browsing a product without purchasing triggers a CDP (Simon AI) to launch a follow-up email via an engagement platform, like Braze, that is personalized with customer data, product info, and customer offer (based on predicted customer value). After a day without a reply, Braze automatically triggers a follow-up SMS (as part of the journey set up by marketing) with options for the customer to either purchase at a special price (based on predicted customer value from the CDP) or text with a customer care rep. And if the customer chooses to text with a customer care rep, Braze automatically triggers a follow-up text from the customer care team’s platform, establishing direct rep-to-customer communication.

And all of that happened without any custom data feeds or targeting criteria from the data team. The result? Marketers move faster, Data teams focus on higher-value analyses, and customers receive a true 1:1 experience.

Agentic AI frees up data teams further

The newest evolution is Agentic AI: AI agents embedded within CDPs and engagement platforms. These platforms exponentially accelerate the customization and scale of 1:1 customer journeys.

Agentic AI can auto-build high-intent segments based on campaign goals, prep data for personalization, and draft entire cross-channel campaigns (timing, copy, visuals) for marketer review and approval.

This shift empowers both sides. The data team gains time to focus on higher-order work like modeling LTV frameworks or refining marketing spend attribution. Meanwhile, marketers are able to envision campaigns around myriad customer moments, delivering micro-journeys at a previously impossible scale.

A practical framework for marketers

To make all this tangible, here’s a quick checklist marketers can use to build a stronger partnership with their data teams.

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The power of partnership

Marketing and data teams are better together, but only when it’s a true partnership. And marketers can take the first steps to make that happen. That means investing in the relationship, not just the requests. Share context. Learn about data. Build trust.

Because the best marketing stories aren’t just crafted with creative copy or eye-catching visuals. They’re written in partnership with the data team, where friction gives way to flow, and collaboration drives growth.