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Data readiness isn’t optional anymore. It’s your competitive edge.

A robotic hand sticks a finger through a pile of post it notes.

Illustration by Dave Cole / Getty / Shutterstock / The Current

In the rush to implement AI solutions in their campaigns, too many advertisers are ignoring a fundamental issue: data readiness. And it needs to be addressed well before the first ad is served. 

Blame it on the “checkbox mentality” that’s developed around programmatic in the last 15 years. Companies repeatedly adopt new tools without ensuring their data is actually usable, then wonder why their expensive investments are falling short.

Data readiness is foundational and essential within AI-powered advertising. It drives everything from audience segmentation to performance optimization and tracking.

Without properly structured data, retailers make multimillion-dollar real estate decisions based on flawed data that tracks consumer patterns, quick-service restaurants allocate inventory using incomplete purchase histories and financial services companies design products that miss the mark on customer needs. 

There are a few root causes for the dismal state of data unreadiness. In part, marketers are victims of the ease promised by automated campaign tools years ago.

Too many have underutilized their existing technology. They’ve spent millions licensing platforms across ad tech and martech yet typically use just 20% of their capabilities, because their data isn’t adequately prepared. 

Worse, advertisers believe their AI-powered insights are reliable and accurate. It is crucial that companies using AI to power campaigns seed the ground by making sure their data is properly organized. If not, the consequences extend beyond marketing efficiency to core business operations.

Coming prepared to the data-readiness party

For most marketers, data lives in silos — marketing here, sales there, customer service somewhere else entirely. The traditional divide between ad tech and martech purposes has served to exacerbate lagging data readiness. It has resulted in parallel data ecosystems that rarely communicate effectively.

We see it all the time. A major national brand recently approached us wanting to implement advanced identity solutions. They licensed a customer data platform, identity partners and various other platforms. But when we examined their data, we discovered it wasn’t in a state where it could be trusted, understood or functionally activated.

"Maybe we expect AI to serve as a magic wand that can fill any hole for us. It doesn’t work that way."

It’s akin to a team of caterers and party planners being handed an incomplete, disorganized grocery list for a dinner party. Some of the items are in French, emailed only to the pastry chef. Others are vague descriptions like “get a few bananas or apples.” Some are scattered across random sticky notes in different locations around the house. You can’t expect one person to shop effectively for the dinner. 

And yet, this kind of disorganized information is what marketers often present to their analytics teams. The result of such messy inputs is that AI outputs will be equally segmented, inaccurate and confusing.

This is more than a waste of campaign time and media spending. The implications of poor data readiness have wider implications for a brand. 

Maybe we expect AI to serve as a magic wand that can fill any hole for us. It doesn’t work that way.

Data readiness 101

Data readiness means your information is clean, structured and accessible so it can be immediately leveraged for analysis, decision-making and activation. At its core is a consumable data layer.

That’s what transforms raw data into a practical resource, making it instantly usable by all systems, teams and applications across your organization.  

Key elements include:

  • Standardization: Ensuring consistent formatting, naming conventions and structures
  • Governance: Establishing processes for data management, quality control and access
  • Permissible use: Ensuring data is collected, stored and activated in ways that align with privacy regulations and contractual permissions.
  • Quality assurance: Regularly verifying data accuracy and completeness
  • Identity resolution: Unifying data points to create holistic customer profiles
  • Interoperability: Creating seamless connections between different platforms 

Many advertisers mistakenly believe that cloud platforms like Snowflake automatically solve these issues. Simply housing data in one location isn’t enough. As we often tell clients, “Now your data is in Snowflake; it’s not going to sit there.” 

Connections and integrations are required to activate the data and make it useful.

The costs of neglecting data readiness can be severe and immediate. It’s not uncommon for a marketer to discover their data isn’t ready until they hit a wall — and then find it’ll take six to nine months to fix. That’s practically an eternity for a brand these days. 

Turning months of works into days or weeks

For advertisers that do prioritize data readiness, the transformation is real across their marketing operations.

Their AI initiatives deliver consistent, accurate results because they’re built on clean, comprehensive data foundations. They achieve that long-sought 360-degree customer view, as previously disconnected data flows seamlessly between departments, creating rich, unified profiles.

The returns on ad spend are equally compelling. 

Existing martech and ad tech investments, which often represent millions in annual spending, can now deliver their full potential rather than operating at a fraction of their capacity.

Integration timelines shrink from months to weeks or even days.

Most importantly, these organizations become nimble: When new tech emerges, they can implement them quickly without lengthy data preparation projects. That’s how competitive advantages are built.

Data readiness isn’t a one-time project; it’s an ongoing discipline that requires regular attention, refinement and long-term thinking.

The upfront investment may appear significant. But the returns in improved decision-making and operational efficiency are vast and rewarding.


This op-ed represents the views and opinions of the author and not of The Current, a division of The Trade Desk, or The Trade Desk. The appearance of the op-ed on The Current does not constitute an endorsement by The Current or The Trade Desk.