This article recounts the key takeaways from our recent marketing analytics roundtable. Led ...
This article recounts the key takeaways from our recent marketing analytics roundtable. Led by Tommaso Lucentini, we reveal the top challenges faced by data and marketing teams in 2025 as well as some solutions.
On Wednesday, 19th March, 2025, specialist data and analytics recruiter, Tommaso Lucentini, held a roundtable discussion for leaders in the sector. Together, they explored some of the biggest challenges in marketing analytics and brainstormed solutions. This article reveals the key takeaways from their conversation.
In the data sector, this period is marked by unprecedented change, with teams still reeling from the introduction of General Data Protection Regulation (GDPR). Marketing analytics today is defined by reduced data certainty, rising complexity, and the need for teams to become both technical experts and skilled communicators.
Where teams once relied on near-complete datasets, they’re now forced to work with fragmented information. For instance, research from etracker from 2023 showed that the average cookie consent rate fell from 46% to 34% within one year. Analysts have been left with reduced data points and incomplete pictures of the customer journey. This makes accuracy and reliability harder to achieve.
First-party data is becoming the foundation of measurement, but many businesses lack the infrastructure for data collection or correct strategy to inform decisions.
Additionally, legal restrictions further complicate data sharing between brands and agency partners. Even within organisations, data silos persist, with teams reluctant to share data insights due to fear of negative exposure.
One of the biggest challenges facing data analytics teams is communicating with senior stakeholders and external teams. A gap has been created between what analytics teams can deliver and what stakeholders expect.
Finance leaders and Chief Marketing Officers continue to demand definitive answers to inform decisions. Meanwhile, analysts are grappling with uncertainty and incomplete attribution models and cannot provide clear information.
To overcome this, Analysts must be able to explain complex data limitations to non-technical stakeholders. To do so simply, you must have a combination of technical rigour, commercial understanding, and the ability to guide senior stakeholders throughout.
Data analytics jobs have changed. There is more emphasis than ever before on soft skills, particularly strong communication skills.
In response, marketing analytics teams are changing quickly. Historically, there were clear roles:
Now, these skills are converging. Companies need people who can understand data, build models, and explain results in a simple way that helps the business make data-driven decisions.
Positively, analytics teams are becoming more visible in organisations. Our roundtable attendees reported the following scenarios:
As the industry shifts, there are three key data roles that companies must invest in to be successful.
To understand the right direction to take, strong leadership is key. One example shared during the discussion was a company that, eight years ago, didn’t even know what its top products were.
When leadership brought in a Chief Data Officer they made data a crucial part of the company’s goals. It took time, investment in technology, and teaching non-technical teams how to use data, but the company eventually became truly data driven.
Data quality and governance are becoming increasingly popular. Instead of fixing problems after they occur, companies are hiring talent to manage data quality from the start. This allows them to ensure everything is correct, consistent, and compliant.
Finally, there’s a growing need for what some call “Analytics Translators.” These are people who may not be writing complex code every day but understand both data and business needs. They help turn business questions into data projects and make sure that insights are easy to act on.
Looking ahead, teams will likely be smaller but more focused. It's impossible to ignore the impact that generative artificial intelligence (GenAI) and machine learning (ML) tools are having on teams.
Currently, AI is acting as an advanced assistant, automating repetitive or low-value tasks. This allows Analysts to spend more time on strategy, advising the business, and planning for different scenarios. As these tools become more advanced, Analytics Leaders will need to act more like consultants, working even closer with other teams and helping shape decisions.
In a world where everyone has had to become more data-driven, it’s crucial for analytics team to maintain strong relationships with the wider organisation and third-party agencies. As mentioned above, these relationships have become somewhat strained where data collection has become increasingly complex.
In fact, data siloes are still very common and they’re compromising business and marketing efforts.
Despite best efforts, the relationship between analytics teams, creative teams, media agencies, and finance departments is still a challenge. As a result, siloes (where teams or partners don’t share data or information) remain a common problem. These siloes slow progress, making it harder to make the right decisions.
Different teams have their own targets and KPIs. Sometimes, they avoid sharing their data because they fear it will make them look bad or lead to difficult questions. This behaviour hurts the whole business. Instead of working together towards a common goal, each team focuses only on their own area.
Agency representatives at the roundtable also shared their frustration. It's common for brands to withhold their important data, even from their agency partners, due to legal concerns or fear of losing control. Without this data, agencies can’t fully optimise campaigns. As one participant put it, “We’re asked to improve performance, but we don’t get the full picture.”
The roundtable participants agreed that fixing siloes starts with clear, shared company goals. If everyone knows what success looks like for the whole business, not just their department, they’re more likely to work together. To achieve this, you need:
Data quality remains one of the biggest struggles for analytics teams. The roundtable discussion made it clear that many organisations still contest with messy, inconsistent data. Teams often spend hours manually cleaning spreadsheets, merging data from different platforms, and trying to make sense of mismatched metrics. This isn’t just time-consuming; it also leads to frustration and delays in delivering insights.
A key issue is ownership. The people who input the data, such as Media Planners or Campaign Managers, are often not the ones who have to use it for reporting or analysis. Mistakes made at the data entry stage can cause major problems later, leaving Analysts to untangle errors that could have been avoided.
Participants agreed that good governance needs to be built in at the source. One practical solution mentioned was the use of measurement frameworks.
Frameworks clarify what success looks like, how data should be captured, and how different teams - both internal and external - will report on results. Having everyone agree on these frameworks upfront reduces confusion later.
The exploration of artificial intelligence in on marketing analytics has evolved from curiosity to a necessity. Our roundtable attendees discussed a broad range of examples where GenAI and ML tools are influencing workflows.
There are clear opportunities that AI presents. For example, in its current state, AI often acts as an advanced assistant, automating repetitive or low-value tasks. However, the current environment is still one of uncertainty when it comes to artificial intelligence.
The Data Leaders in the room agreed that AI-generated outputs are currently “book smart, not street smart.” AI can generate summaries and automate structured tasks but lacks the business context and interpretative nuance that human Analysts bring.
From an engineering perspective, the roundtable revealed that AI is providing up to a 30% improvement in development efficiency. Automated code checking, faster data pipeline creation, and task orchestration are key benefits. However, the consensus was clear: AI does not replace the need for human interpretation.
A big thank you to our roundtable attendees for your time and thoughts. If you missed out and want to join us at our next data event, be sure to register your interest in 3Search Events.
It's clear from their conversations that marketing analytics teams must evolve to continue to deliver business intelligence that improves the customer experience. If you’re expanding your data team to prepare your business, reach out to our expert recruiters for support. We speak to hundreds of candidates every week and can help you access the talent required for business success.
