Data teams must treat internal marketing teams as customers to reach data democratization

Since the rise of digital marketing, performance marketers and data teams have often found themselves at odds. Marketers need quick access to data and insights, while data teams require time for thorough analysis and want to ensure governance. This ongoing tension can hinder organizational efficiency and growth. The solution lies in data democratization—empowering marketers with direct access to data and easy exploration, while allowing data teams to focus on high-impact projects such as marketing measurement.

Let's start by understanding this conflict.

Understanding the data-marketing conflict

The ongoing conflict between performance marketing and data teams has numerous symptoms:

• Limited data access and exploration: Marketers can view dashboards on BI tools but cannot freely explore or manipulate data without the assistance of skilled data analysts. Any change in dashboards, metrics, filters, etc has to be carried out by the data/BI team;

  
  

• Endless tickets, frustration and stress: data teams get stuck with reactive ad-hoc maintenance work (reporting requests, pipelines, etc) taking away from analytics priorities (MMM, attribution, incrementality);

  
  

• More money spent on contracting marketing data analysts: without data democratization, organizations are left with little choice but to hire costly data analysts, trying to put all the data into use;

  
  

• Lack of trust in data: marketers need a single source of truth where they can reliably retrieve accurate data. Data teams own this reporting, becoming the single point of failure for analysis delivery and accuracy;

  
  

• Siloed teams: each team dedicates its energy solely to its own respective tasks and goals rather than to the company’s goals. In this situation, collaboration and information-sharing are reduced, ultimately impacting marketing performance;

The following diagram summarizes the main complaints and frustration from both teams:

Based on many conversations with marketing and data teams, this reality has intensified over the last years - let’s unpack why. 

What has changed in the marketing landscape?

As the marketing landscape has evolved, so too have the pressures on both marketing and data teams. Two fundamental shifts have intensified this conflict: the increase in marketing complexity and the economic downturn.

Both shifts not only put pressure on performance marketing teams, but also increase the workload for data teams. The former have more scrutiny than ever and ambitious targets to achieve. The latter need to work on critical data products (MTA, MMM, incrementality) as well as maintaining reporting and data pipelines.

How to resolve this conflict? Let’s first delve into the reality of each team.

Understanding the challenges of Marketing and Data Teams

1) Marketing teams

Marketers want to turn data into better decisions while exploring granular data and iterating and testing new hypotheses fast. However, most marketers (especially ICs and team leads) have given up on data support for day-to-day, weekly analysis or reports given long waiting times. This leads to considerable manual analysis and reporting in spreadsheets for deep dives, ad-hoc questions, and granular use cases (e.g., budget pacing). This results in missed optimization opportunities.

2) Data teams

Data folks are excited about working on measurement projects, given the business impact and interesting technical challenges. However, they're stuck with reactive ad-hoc maintenance work - both reporting and pipeline maintenance. They get many tickets for new metrics and dashboards, data modeling, QA, data pulls, etc. while managing API updates and broken pipelines. Most teams are overwhelmed with ad-hoc requests from marketing, spending between 25% and 75% of their time on reactive work.

The implications of these challenges

Having marketers with many ad-hoc requests is actually a positive sign of a healthy data-driven culture. Same applies to all the spreadsheets marketers build to test things quickly.

Why? It means marketers are curious and want to test new hypotheses. 

If marketers just look at the existing predefined dashboards, it’d mean they’re looking at data always from the same angle. In other words, they are not exploring data or testing anything new.

It's beneficial that data teams focus on marketing measurement, as they are uniquely positioned to drive better decisions around budget allocation and optimization.

In a nutshell, both teams are trying to maximize impact, but get stuck:

• Marketing teams struggle to get insights quickly. Many hypotheses go untested, leading to missed opportunities

• Data teams spend most of their time on reactive ad-hoc work, taking time away from the big marketing measurement projects

  
  

How to solve this?

Resolving the conflict: marketers own operational reporting

To resolve this conflict, leading companies empower marketers by enabling them to own operational reporting. This way, marketers gain the autonomy to explore data and derive insights quickly, without any dependencies. This also allows data teams to focus on high-impact projects like measurement and attribution.

Resolving conflict part 1: software

The company-wide modern data stack setup falls short for complex, specific use cases like performance marketing. The best teams embrace a unified marketing analytics platform that meets 4 criteria:

✔️Ability to consolidate all marketing data, blending channel, analytics and internal data (COGS, CLV, budgets and targets) down to the most granular level and to cover maintenance of all marketing data pipelines;

✔️Ability to cover all marketing use cases (monitoring and controlling, cross-channel, deep dives, creative optimization, budget pacing, etc) and enable end-to-end marketing analytics workflows;

✔️“Low floor, high ceiling” philosophy: accessible and easy for every marketer to use, yet sophisticated and flexible to enable sophisticated dashboards, deep dives and data exploration;

✔️Strong governance (reliable, curated metrics, clear permissions and version controls, centralized management);

Resolving conflict part 2: cultural shift

Many companies fail on their journey to data democratization. As their data and marketing teams lock horns, the conditions needed to get to democratization are not available, and data teams tend to retreat and entrench. 

Oftentimes data teams keep hiring more marketing analysts to answer more tickets. This usually alleviates some symptoms, but doesn't really solve the underlying challenge.

In a recent article, Benn Stancil explored how data teams can overcome their struggles by starting to treat internal teams like customers. With this mindset shift, data teams start to think about how they build out their products and, with it, their customer service and onboarding process. By viewing data with a go-to-market lens, data teams can see how they play a role in finding problem-solution fit and product-market fit. 

The key to this success is empathy. By viewing marketing teams with empathy, data teams will get a better understanding of:

• The outcomes marketers want to achieve

• How to build systems and processes that will help them achieve their goals

• Their pain points and obstacles

• The right solutions shapes for the right problem holes

In a fully democratized organization, marketers feel comfortable asking data-related questions, teams have the right tools in place to enable self-serve use, and there is a common understanding of data democratization as an ongoing process. The companies that are able to make this shift will not only keep the peace between their data and marketing teams, but will also be able to achieve significantly better marketing performance.

Outcomes of data democratization

• Marketing teams can effectively self-serve and get all insights quickly to drive performance

• Data team can focus on the most impactful initiatives and drive ROI (tip of the iceberg) vs spending most of their time reacting to requests

• Improved collaboration and communication between both teams, with less friction

Want to explore further this topic? Delve deeper into the road to data democratization.