Building the data foundation for next-generation digital experiences

Only 13 per cent of advertisers today feel confident they understand which content resonates with their audiences across channels. That’s an alarmingly small number – because without that insight, teams can’t ensure positive digital experiences or optimise campaign performance.

 

As AI makes it possible to produce hundreds of variations of the same campaign, each tailored and targeted to different audiences, this problem only gets more urgent.

 

So what’s the secret of that lucky 13 per cent? A strong foundation of data standards. These organizations can track consistent content metadata at scale, tracing every asset’s performance across campaigns. This is no small feat, but it’s the make-or-break difference between brands that can use data to optimise digital experiences and those who are left simply hoping for the best.

 

The strategic advantage of organised data

 

Most companies manage content across a sprawling ecosystem of content libraries, marketing automation platforms, social channels and analytics tools. Instead of seeing this complexity as a burden, companies with standardised data wield it as an advantage: they’re capable of creating experiences their competitors simply can’t match.

 

When data flows seamlessly across systems with consistent metadata, teams get a crystal-clear view of what visuals or messaging drive results.

 

Take a product launch campaign: organised data reveals which specific technical explainer video drives the highest engagement on LinkedIn with each audience segment, or what combination of lifestyle imagery and headline generates the greatest conversions on Instagram.

 

Armed with these insights, teams can optimise in real-time, continuously refining which messages and visuals create the most engaging customer experiences while maximising campaign performance.

 

Meanwhile, teams without organised data waste time and budget recreating content that already exists, simply because they can’t find it in their digital asset management (DAM) – a problem standardised metadata solves instantly. The result: lower production costs, faster campaign launches and creative teams focused on innovative ideas instead of reinventing the wheel.

 

Finally, when market conditions inevitably shift, companies with well-structured data can pivot fast. Think about an influencer partner getting caught in a PR scandal, after which you need to pull every piece of content featuring them across 15 channels – or a competitor launching a product that makes your “industry-leading” claims outdated overnight. Companies with proper data standards can instantly identify every affected asset and make updates within hours instead of days, reducing the risk of delivering a less-than-optimal brand experience.

 

Why AI success depends on data excellence

 

Organised data is a competitive advantage today, but as AI adoption accelerates, it will become non-negotiable.

Data scientists have a favourite saying about AI: garbage in, garbage out. The same applies here. Without standardised data, AI only amplifies existing problems.

 

Consider the challenge of AI-powered personalisation. When an AI system recommends personalising email subject lines for different audience segments, it relies on rich metadata about past performance, audience behaviour and content categorisation to make intelligent choices. Without this contextual foundation, AI becomes an expensive random generator rather than a strategic asset.

 

Then there’s brand safety: when AI creates content automatically, how do you ensure it meets your brand guidelines? Companies need detailed metadata covering brand standards, approval workflows and content governance – all built on organised data foundations.

 

The stakes intensify as AI enables content creation at unprecedented scale. Modern campaigns can include hundreds of variations across different audiences, channels and contexts. But here’s the reality check: only one in four marketers are very confident in their ability to tag and track AI-generated content. This creates a massive blind spot where companies invest in AI capabilities but lose visibility into what’s actually working – or possibly putting their brand reputation at risk.

 

Building your data standards foundation

 

Whether a brand embraces AI today or plans for it tomorrow, the benefits of data standards are clear: organisations implementing comprehensive data standards report an average 33 per cent increase in ROI across campaign optimisation and operational efficiency.

 

Brands such as Vanguard and Colgate-Palmolive are achieving data standardisation at enormous scale after struggling with inconsistent tagging and siloed reporting. But implementing data standards needs to be treated as a strategic initiative, not just a tech project.

 

A leading US mortgage provider shows how it’s done. After years of inconsistent data across its brand portfolio, it took a systematic approach: stakeholder interviews, gap analysis and a complete taxonomy redesign, all completed in just six weeks. As a result, the mortgage broker had a clear implementation roadmap for consistent naming conventions and effective governance.  

 

This kind of work becomes exponentially more valuable as AI capabilities advance. Every properly tagged asset and standardised taxonomy becomes training data for future AI systems. Companies building these foundations now will have richer datasets when next-generation AI tools arrive – positioning them to deliver highly relevant, personalised digital experiences.

 

The path forward

 

As customer expectations rise and AI capabilities explode, the competitive advantage goes to companies that deliver consistently excellent, measurable digital experiences. That foundation starts with data standards.

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Ready to transform your data into a competitive advantage? Download Claravine’s Ultimate Guide to Data Standards to discover how leading organisations are building data foundations that power exceptional digital experiences.