You’ve been working on marketing reporting for years, using everything the modern data stack has to offer - a state of the art data warehouse (e.g., Snowflake), an ETL tool (e.g., Fivetran, Funnel, Adverity), and a BI tool (e.g., Looker, Tableau, PowerBI). After investing significant resources to model and harmonize data across channels, you've successfully built dashboards to answer most of the important questions from marketing teams.

The heavy lifting is done. Your 2025 roadmap focuses on key marketing data products - Marketing Mix Modeling, Incrementality, Attribution…and AI. Your reporting challenges are behind you.

But is marketing reporting really solved? Or is your team of data engineers, analytics engineers and analysts spending most of the time answering marketing requests and tickets? The diagram below likely represents the reality:

Data Teams: trapped in the cycle of reactive marketing reporting maintenance

Your approach is likely costing you millions of dollars per year - both in wasted ad spend and operational costs. Especially if you have large scale digital marketing operations (8 or 9-digit marketing budget, advertising across many channels and markets, while relying on complex attribution).

Many enterprises are discovering that their current approach to marketing reporting – dumping all 3rd party data into their data warehouse and attempting to “build” everything in-house – is fundamentally flawed.

The Status Quo approach

Most data teams today follow this approach to marketing reporting at scale:

1) Use Python scripts or connectors like Fivetran and Funnel to dump all marketing data into the data warehouse

2) Transform and model the data using dbt

3) Build dashboards in BI tools like Tableau or Looker

4) Maintain and update the pipelines, data models, metrics, dimensions and dashboards as needs change

Status Quo: approach to marketing reporting

This approach appears sound on the surface. It follows modern data stack best practices and gives data teams complete control over the data. It likely covers the needs of your senior leadership, but as soon as marketers ask for a filter that doesn’t exist or a dimension that wasn’t accounted for - the process breaks and needs to start over again.

What makes Enterprise Marketing reporting different?

Enterprise marketing reporting has unique characteristics that make it fundamentally different from other data problems and domains:

1) Third-party data sources: Unlike product analytics or internal ops systems, nearly all marketing data comes from external sources (Google, Facebook, TikTok, etc). Each platform has its own complex data structure and nomenclature. For instance, on Google Ads alone, there are five different types of campaign types, each with their own distinct attributes.

2) Marketing-specific business logic: The way campaigns, ad groups, and creatives are structured is defined by marketers, not data teams. This creates a knowledge gap where data teams must constantly learn and adapt to marketing-specific concepts.

3) Constant evolution: Marketing platforms frequently change their data structures, add new campaign types, and modify metrics. This requires continuous maintenance of data models and transformations.

4) Granular optimization needs: Channel managers need deep, granular data for day-to-day campaign optimization. This includes asset-level analysis (e.g., creative reporting, keyword analysis), and ways to slice and dice to uncover actionable insights.

Why this approach fails

While this approach works well for C-level static dashboards, it fails to serve the operational needs of channel managers and performance marketers due to: 

1. Loss of granularity and insights

To manage costs and complexity, data teams often aggregate data, resulting in:

• Limited insights below campaign level 

• Lost context and dimensions needed for tactical decisions

• Dashboards that serve executives but not channel managers

2. Rising costs

• Rising data warehouse costs as you store and process increasingly granular data

• Growing data team headcount needed to keep up with marketing requests, ad platforms updates, new campaign types, data models, etc 

• Accumulating technical debt as you build and maintain more dashboards

  

Status Quo - operating costs increase over time

3. Knowledge gap

Data teams struggle to keep up with marketing-specific business logic and constantly changing platform requirements. This leads to:

• Lengthy back-and-forth between marketing and data teams

• Increased time to deliver new insights

• Risk of misinterpreted data or incorrect modeling

  
  

Real-world symptoms

At this point, you're likely asking yourself - "might this apply to me too?". Look for specific signs.

From data teams' perspective:

1) You receive a constant stream of ad-hoc requests from marketing

2) Average response time for marketing data requests is 2-4 weeks

3) Your data tooling (warehousing + ETL) cost keeps rising or you have to compromise on data granularity

The reality of marketing analytics: never ending cycle of data requests and "quick questions"

From data consumers' (marketers) perspective:

4) Marketers have created their own shadow reporting systems in spreadsheets or Looker Studio / Data Studio for use cases like budget pacing and channel deep dives

5) Marketers can't access the granular data they need for optimization

Common requests when marketers want to explore data vs static dashboards

Curious to dive into signs and symptoms? Read a complete list here.

A better approach: Marketing Reporting as a product

The solution isn't to build everything in your data warehouse. Instead, enterprise marketing reporting requires a purpose-built product specifically designed to address its needs and use cases, while integrating with the modern data stack.

The key lies in automating the marketing data ingestion, processing, and harmonization process while enabling self-service for operational marketing reporting (channel managers and performance marketers). Simultaneously, harmonized marketing data exports to the data warehouse power C-level dashboards, MMM, Incrementality testing, cost pipelines for finance reporting, etc.

This way your data warehouse remains the single source of truth, without the high operational costs, both for tools (ETL + storage + compute) and labor (dedicated resources for maintenance). At the same time, your marketers have access to all granular insights without having to wait for your support.

Status Quo vs Marketing Reporting as a product

Other large, complex teams - such as Uber, Delivery Hero, HelloFresh and On - have made the switch and seen significant improvements.

Feedback from leading brands

Why this approach works better

1) Reduced costs: By processing marketing data outside your data warehouse, you:

• Eliminate expensive storage and compute costs for raw marketing data

• Eliminate marketing ETL tools cost (Fivetran, Stitch, Funnel, Adverity)

• Reduce data team resources needed for maintenance and dashboards

• Remove the need for continuous pipeline and tables updates

2) Granular data, deep insights: Purpose-built marketing reporting preserves the detailed data marketers need while making it accessible through a self-service interface designed to cater to specific marketing use cases (e.g., Excel-like exploration, ad previews next to all metrics, etc).

3) Built-in marketing logic: Marketing-specific business logic is handled automatically, eliminating the knowledge gap between data and marketing teams.

4) True self-service: Marketers can create their own reports and analyses without requiring data team involvement, while maintaining data accuracy and governance. This includes both dashboards and tables (like Excel).

Marketing reporting as a product is a much more affordable approach

The path forward

You don't need to build everything in house. By embracing marketing reporting as a product with a purpose-built platform like Clarisights:

• Your team can focus on strategic projects like attribution and incrementality measurement

• Marketers get faster access to the granular data they need

• Your organization saves significant costs on data infrastructure and maintenance

• Data governance is embedded to ensure data exploration without chaos

The key is recognizing that marketing reporting for such a scale, with its unique characteristics and requirements, deserves its own purpose-built solution rather than forcing it into a one-size-fits-all data warehouse approach.

Remember: The goal isn't to replace your data warehouse – it remains your source of truth for company-wide data. Instead, it's about using the right tool for the specific challenges of enterprise marketing reporting.

I know that even considering a switch in marketing reporting tools sounds scary! You've spent years building and improving your current system, and you've come a long way. But take a moment to ask yourself - does your setup truly serve you, or are you constantly working around its limitations? Now image your day-to-day without endless tickets and quick questions. Marketing reporting that frees up your time and lets you focus on MMM, Incrementality, AI, etc to drive real business impact.

The good news? Making the switch is easier than you think. Spoiler: We’ve helped some of the largest, most complex teams transition smoothly in just weeks. Learn more about the process here.