Transforming Marketing Data into Business Growth: Key Insights and Strategies

Marketing leaders and data professionals often grapple with a familiar challenge: how to transform marketing data into tangible business growth. During a recent episode of The Lights on Data Show, I had the privilege of speaking with Kasper Bossen-Rasmussen, founder and CEO of Accutics, about this very topic. Together, we explored key takeaways for addressing the common hurdles faced by organizations striving for data clarity and consistency and how data governance can be that answer for transforming marketing data into business growth.

Here are the 4 key takeaways from our conversation:

1 .Establishing a global naming convention and data structure

A global naming convention and shared data language are not just fundamental—they are transformative for marketing success. Organizations without a unified structure often operate in silos, with teams relying on inconsistent definitions and metrics. This fragmentation can lead to inefficiencies, misaligned strategies, and a lack of trust in the data being used to drive decisions.

Adopting a global naming convention and shared language delivers tangible benefits:

• Improved Collaboration Across Teams: Whether it’s social media campaigns, paid advertising, or global marketing initiatives, a unified naming convention and structure ensures that everyone speaks the same data language. This alignment fosters smoother communication and more cohesive strategies across departments, markets, and geographies.
• Enhanced Trust in Decision-Making: Decisions informed by standardized, trustworthy data build confidence at every level of the organization. When stakeholders can rely on consistent definitions and metrics, it removes ambiguity and empowers more effective, data-driven choices.

Organizations that embrace this approach experience remarkable outcomes. For example, Kasper Bossen-Rasmussen shared that out of the companies he’s worked with, those with a global structure and conventions for naming and tracking data outperform their competitors by an impressive 155% or more. This level of success is linked to their ability to create a shared foundation that eliminates silos and enables better measurement, reporting, and optimization.

Real-world examples of global structure in action

Creating a global naming convention and data structure starts with standardizing key definitions. Consider two practical examples that highlight why this is crucial:

• Video Views: A "video view" can mean different things depending on the platform. For example, Facebook might count a view after three seconds, while YouTube requires ten seconds. Without a clear, standardized definition, marketing teams could report inconsistent metrics, making it impossible to evaluate campaign performance accurately.
• Clicks: Similar challenges arise with "clicks" in digital marketing. Platforms like Facebook distinguish between "clicks to a company page" and "outbound clicks" to external websites, while other platforms may lump these together. If teams don’t align on what constitutes a click or how it’s categorized, reporting becomes fragmented, and insights are compromised.

Practical implementation steps

To establish a global naming convention:

• Align stakeholders from different teams and regions to agree on consistent definitions for key metrics like video views, clicks, impressions, and conversions.
• Document these standards in a centralized repository that is easily accessible to everyone involved.
• Implement tools or platforms that enforce these standards in real-time, ensuring compliance across campaigns and channels.

The business case for standardization

Standardization ensures that all teams are working from the same playbook, reducing miscommunication and streamlining analysis. It also builds trust in the data, enabling faster and more confident decision-making. Organizations with this alignment see a direct impact on their success, not only in terms of efficiency but also in their ability to outpace competitors.

By creating a global data structure and naming conventions, organizations lay the groundwork for accurate measurement, meaningful insights, and sustained marketing success. This is a proven strategy for unlocking the full potential of your marketing efforts and outperforming the competition.

2. Recognize data quality as a continuous process

Data quality is not a one-time project; it is an ongoing journey that requires continuous effort and commitment. Organizations that excel in marketing data governance understand this and adopt a dynamic, iterative approach to improve and maintain data quality over time.

Rather than attempting to tackle everything at once, successful companies focus on building a strong foundation and expanding from there. They treat data quality as an evolving process and overall program without and end date, ensuring that each step adds value and aligns with their business needs.

Key recommendations for enabling data quality

1. Start simple and build gradually: Organizations should begin with foundational models and expand incrementally. Instead of overwhelming teams with complex frameworks, focus on creating a manageable structure that teams can adopt and adapt. For example, start by standardizing critical attributes, such as campaign names and key metrics, and scale up to more advanced tracking and governance as maturity grows.

2. Assign clear ownership: Data quality requires accountability. Designating a dedicated role (for example a "taxonomy owner" or "naming governance lead") ensures that someone is responsible for maintaining and enforcing standards. This role can act as a data steward, overseeing updates, ensuring consistency, and formalizing changes to the data governance framework.

3. Prioritize stakeholder alignment: Stakeholder management is critical for long-term success. Aligning different teams (ex: marketing, analytics, finance, etc.) on what data needs to be tracked and why is essential. Without this alignment, efforts can become fragmented, leading to inconsistent data and miscommunication across the organization. Kasper shared a valuable insight: “companies using fewer attributes, but tracking them consistently outperform those with more attributes tracked inconsistently”. For instance, a company that meticulously tracks five key attributes across all campaigns will achieve better results than one attempting to manage 70 attributes but using them inconsistently.

4. Focus on activation, not just reporting: Data is about more than just generating reports. It’s about activating the data and using it to make informed decisions, optimize campaigns, and drive business outcomes. Reporting is a means to an end, but the real value of data lies in its ability to inform strategy and guide actions. For this to be taken advantage of, good data quality needs to be there.

Practical implementation steps

• Define what matters: Begin by identifying the most critical data attributes your organization needs to track. Avoid overloading the system with unnecessary details so track only what is actionable. For example, if your team doesn't use geographic segmentation to make decisions, exclude it from your taxonomy.
• Create a shared data language: Develop a common understanding across stakeholders of how data should be collected, categorized, and used. This ensures everyone is on the same page, from marketing teams to external agencies.
• Embed processes and automate: Manual processes, such as managing naming conventions in spreadsheets, can lead to errors and inconsistencies. Automate wherever possible to ensure adherence to standards in real-time.
• Measure and iterate: Establish metrics to assess data quality over time, such as compliance scores or data completeness percentages. Use these insights to refine processes and close gaps.

The business case for data quality

Investing in ongoing data quality creates a ripple effect across the organization. By aligning on key attributes and ensuring consistent usage, companies can build trust in their data, streamline workflows, and improve decision-making. Additionally, this commitment enables teams to adapt to changing business needs and technologies, keeping their data practices relevant and robust.

By embedding data governance into daily workflows and aligning it with strategic objectives, organizations can transform data quality from a technical requirement into a competitive advantage. This approach fuels better insights and empowers teams to take decisive action with confidence.

3. Elevate data governance to fuel innovation

Data governance serves as the foundation for advanced analytics, AI, and any initiative that relies on data. Without it, even the most sophisticated tools can produce unreliable results, leading to missed opportunities and flawed decision-making. High-quality data, enabled by robust governance practices, powers accurate models, actionable insights, and scalable innovation.

Organizations that prioritize data governance early set themselves up for success, ensuring their data is not only reliable but also capable of driving value across multiple initiatives.

Key principles for data governance as an innovation enabler

1. Tie data governance to business value: The connection between data governance and business outcomes must be clear and measurable. For example:

• Improvements in data quality lead to more accurate reporting, better audience targeting, and increased campaign efficiency.
• Consistent data reduces the time spent on manual tasks like data cleaning, freeing up resources for strategic work. Kasper shared an inspiring story of a client who saved an hour of data preparation every time a dashboard was viewed (so make sure you listen to this podcast episode) . This is already translating into significant productivity gains. 

2. Establish a strong foundation early: Data governance is about ensuring data can be trusted and utilized effectively. This is especially critical before deploying AI or machine learning tools. Without high-quality data, AI models risk being trained on inconsistent or inaccurate inputs, resulting in flawed outputs. As the saying goes, "Bad data leads to bad AI."

The role of consistency and scale

An illustrative example of data governance in action is the use of URL tracking parameters for marketing attribution. For accurate insights, organizations need consistent tracking codes that can:

• Attribute conversions to the correct channels, campaigns, or creative assets.
• Differentiate between key metrics like inbound clicks (e.g., from ads) and outbound clicks (e.g., to external websites).

While creating a standardized system might seem simple in theory, it becomes exponentially complex at scale. Consider a global organization with hundreds of campaigns running across multiple teams and regions. If tracking parameters aren’t standardized, inconsistencies can quickly multiply, leading to data silos and unreliable metrics.

This highlights the importance of thinking at scale:

• Ensure naming conventions and governance frameworks are scalable across departments and geographies.
• Automate processes to minimize errors, particularly for repetitive tasks like generating tracking codes or validating campaign data.

How data governance fuels AI and advanced technologies

AI and machine learning thrive on high-quality, structured data. For example:

• An email automation platform can drive conversions more effectively if attribution data from consistent tracking codes is properly fed into analytics systems.
• AI models for campaign optimization rely on well-governed data to determine where to allocate budgets or identify underperforming channels.

AI’s dependence on data governance has brought renewed attention to the importance of getting the foundation right. Many organizations are now realizing that they need robust governance frameworks to fully leverage AI’s potential, shifting their focus from flashy tools to solid infrastructure.

Practical steps to elevate data governance

• Implement consistent tracking: Develop standardized naming conventions and tracking parameters that work across all platforms and teams. This ensures data flows smoothly into analytics systems and supports accurate attribution.
• Measure and communicate ROI: Demonstrate the impact of data governance through measurable results, such as increased campaign efficiency or reduced time spent preparing data. For instance, a marketing team that improves compliance with naming conventions can unlock more actionable insights with less effort.
• Leverage automation: Use tools like Accutics to enforce marketing data governance standards, validate compliance in real-time, and integrate data across platforms for seamless reporting and analysis.
• Think long-term: Establish data governance frameworks that can scale as your organization grows. By planning this from the start and including all relevant stakeholders, you can avoid costly rework down the line.

The bigger picture: data governance as a catalyst for innovation

Data governance can be a strategic enabler. By creating a foundation of trust and consistency, it empowers organizations to embrace innovation confidently, whether through AI, analytics, or other data-driven initiatives.

In the end, data governance and data quality fuel everything that relies on data. When done well, it supports existing processes and opens the door to entirely new opportunities. Organizations that invest in data governance early and consistently are better positioned to lead in an increasingly data-driven world.

4. Set marketing department as the standard bearer

Marketing departments are often uniquely positioned to lead the charge in data governance. With their fast-paced nature and reliance on vast amounts of data from multiple channels, marketing teams frequently adopt innovative data practices out of necessity. This adaptability makes marketing a natural candidate to set the standard for data governance, paving the way for organization-wide transformation.

When marketing departments embrace data governance, the ripple effect can drive efficiency, collaboration, and cultural shifts across the entire organization.

Why the marketing department can lead the way

1. A data-heavy function: Marketing operates at the intersection of multiple data sources: social media, paid advertising, email campaigns, and analytics platforms. This dependency on large volumes of data makes data governance essential to ensure consistency and reliability. For example, marketing teams must manage:

• Attribution data: Tracking clicks, conversions, and impressions to understand campaign performance.
• Audience segmentation: Using data to target the right customers with the right message at the right time.
• Budget allocation: Relying on accurate data to prioritize high-performing channels and initiatives.

2. A fast-moving environment: Marketing’s rapid pace and need for immediate results push teams to adopt structured processes that simplify operations. Implementing data governance frameworks not only supports these demands but also demonstrates how governance can be agile and impactful, inspiring other departments to follow suit.

Success stories: the impact of marketing-driven governance

A compelling example shared by Kasper Bossen-Rasmussen illustrates the transformative power of marketing-led governance. One Accutics client achieved a 97% compliance score across media channels, a feat that underscores the value of consistent data governance practices. This accomplishment led to:

• Significant time savings: Teams reduced the time spent on manual data aggregation and cleaning, enabling them to focus on strategic decision-making.
• Enhanced decision-making: With clean, reliable data, the organization was able to confidently analyze campaign performance and allocate resources more effectively.

Such success stories highlight how marketing departments can set the tone for data governance across the organization, proving its value through measurable outcomes.

Practical steps to position marketing as the standard bearer

1. Engage stakeholders early: Involve key stakeholders across departments to ensure alignment on shared standards and goals. For example, when defining metrics like “customer conversion rate”, have the marketing team collaborate with sales and product teams to incorporate their perspectives and requirements. Focus on the marketing data domain, but include all relevant stakeholders.

2. Implement scalable solutions: The marketing team should adopt tools that enforce data governance at scale, such as Accutics, which standardizes naming conventions, validates compliance, and integrates data across platforms for seamless reporting. You can start with marketing data governance and evolve into organization-wide data governance.

3. Showcase measurable wins: Demonstrate the ROI of data governance by highlighting tangible outcomes, such as reduced time spent on data preparation or improved campaign performance. Use these success stories to build momentum and encourage adoption in other departments.

4. Foster a data governance culture: Embed governance into marketing workflows so it becomes second nature for teams. Reinforce its importance through ongoing training, clear communication, and visible leadership support.

How Accutics empowers marketing data governance

Accutics stands out as a transformative platform designed to simplify and enhance the data governance journey for marketing teams. By addressing the complexities of managing marketing data across multiple channels and stakeholders, Accutics enables organizations to establish a strong governance foundation and unlock the full potential of their data.

Key features of Accutics

1. Standardization: At the heart of Accutics lies its ability to create configurable taxonomies that ensure consistent naming conventions and metadata usage. This standardization eliminates the chaos of siloed and inconsistent data, enabling teams to:

• Align on a shared data language across campaigns, channels, and teams.
• Streamline the creation of tracking parameters and ensure uniformity in reporting.

By automating and embedding these processes, Accutics ensures that governance becomes a seamless part of the marketing workflow.

2. Validation: Accutics goes beyond standardization by actively validating compliance with established conventions. Automated compliance checks:

• Identify gaps and inconsistencies in real time.
• Provide actionable insights to improve adherence to governance frameworks.
• Build trust in the data being used to inform decisions.

For example, Accutics allows marketing teams to track compliance scores across media platforms, offering transparency into the reliability of their data.

3. Integration: The platform integrates seamlessly with marketing tools and dashboards, enabling a holistic view of performance data. This connectivity allows organizations to:

• Link tracking and performance data with analytics tools.
• Generate unified reports that span top-funnel metrics (like clicks and impressions) to conversion data.
• Simplify workflows by automating data aggregation and analysis.

Final thoughts

In today’s data-driven marketing landscape, data governance is a strategic imperative. Without a shared data language and naming conventions, consistent processes, and clear ownership, organizations risk falling behind in a competitive marketplace.

Accutics makes the journey to effective marketing data governance achievable by providing the tools and support to create a scalable, reliable foundation. By adopting platforms like Accutics, marketing teams can lead the way in transforming data into a true driver of business growth.

A heartfelt thank you to Kasper Bossen-Rasmussen for sharing his expertise and insights on The Lights on Data Show. Until next time, let’s keep putting the lights on data!