What’s pushing enterprise marketers to the data layer?
Modern marketers aren’t just running campaigns. They’re constantly navigating operational complexities, data sprawl, and ever-rising customer demands. They also, understandably, have less tolerance than ever for tools that don’t earn their keep.
But according to Gartner, marketing teams are now using just 33% of the martech capabilities they’ve already paid for – down from 42% in 2022 and 58% in 2020.
This rise of “shelfware” likely goes hand-in-hand with the martech scene evolving at record-breaking speeds. Teams don’t have time to properly utilize everything available to them, and not just in terms of flashy new features. There’s also been a fundamental shift happening in how industry leaders approach data, security, and cross-functional strategy.
The most successful teams aren’t the ones buying the most tools. The brands winning right now are those building leaner, faster infrastructure that actually delivers on flexibility and scale. Here are four specific martech trends that forward-thinking teams should be watching and planning around.
1. Bringing the tools to the data – no more moving data into the tools
For years, enterprise tech stacks have relied on piping data between a patchwork of solutions – ESPs, CDPs, analytics dashboards, personalization engines. But every time data moves, it gets stale. Delays creep in. Complexity multiplies. Storage and compute costs increase. Governance becomes a nightmare.
A new standard is (finally) gaining real traction though: activating data where it already lives. Market data backs this up. The CDP Institute cites that composable, warehouse-native CDP vendors saw 7.8% organic employment growth in the past year – nearly 6x the 1.3% industry average – and more than a quarter of CDPs now support warehouse-centric architecture. This isn’t a niche approach anymore. It’s where the growth is.
With the widespread adoption of cloud data warehouses like Snowflake, Databricks, and BigQuery, leading brands are actively flipping the script. Instead of shipping data out to various marketing tools, more enterprise teams are bringing their martech directly to their central data cloud.
It used to take 2+ weeks for a big box national retailer to launch new campaigns – but now they deploy personalized messaging 24x faster by connecting directly to BigQuery, pulling in accurate pricing and inventory data up to the minute.
When marketing campaigns can query live data at the source, it reduces lag, sync jobs, and data discrepancies. Messaging reflects real-time context. There’s less friction between tools, leading to more cohesive experiences across channels. IT overhead drops. And marketing teams gain faster, self-serve access to insights that drive smarter engagement.
Not needing to move data to use it is a major operational unlock, not just a technical one.
2. Protecting privacy without killing personalization
It makes sense that eliminating data movement is more efficient. But it’s also more secure – and the cost of getting this wrong keeps climbing.
A recent report from IBM revealed the global average cost of a data breach hit a record $4.99 million in 2026, up 12% year over year. Customer PII remains the most frequently compromised data type, showing up in 52% of those breaches. Every time personally identifiable information (PII) travels to another platform, that exposure – and the compliance headache that comes with it – grows. That’s why enterprise brands are rethinking how they manage, store, and activate sensitive customer data. The goal is to keep PII where it belongs: in the governed data warehouse, out of third-party systems.
But here’s the rub: marketers still need to personalize experiences. That’s where a data-first approach wins again. By activating customer data at the source, marketers can use PII without exposing or transferring it outside their organization’s secure environment. Granular permissioning keeps access tight. Sensitive fields can even be redacted so marketers never see raw PII but can still use it in tailored messaging. IT and data teams stay in control. Marketers stay effective.
Chick-fil-A has always taken customer privacy seriously – but they also hold themselves to a high standard when it comes to personalized experiences. For example, their martech admins only see a string of letters and numbers instead of customer email addresses, and each data point can be encrypted differently. At the same time, they’ve executed tailored campaigns with location-based dynamic content that resulted in a 20% increase in conversions and 5x more mobile app memberships.
This isn’t just good data governance. It’s a trust-building differentiator. Brands that honor privacy and deliver real personalization are earning deeper loyalty in a climate where the cost of breaking that trust has never been higher.
3. Working in tandem across marketing and data teams (finally)
Rifts between teams don’t cut it anymore, and the data shows just how much they’re still costing organizations. Only 56% of go-to-market professionals consider their organizations highly aligned across shared goals, data, and systems, and 53% point to disconnected tools and workflows as the primary source of friction. Separately, the CMO Council cites 31% of marketing leaders saying organizational silos across marketing, IT, sales, and product continue to actively hinder collaboration.
Marketers used to define data needs and hand them off to product or BI counterparts for fulfillment, often creating a lengthy back-and-forth cycle. Campaigns lagged. Innovation stalled.
Now? That dynamic is changing fast. Successful brands are bringing these functions into strategic partnership with shared tools, shared goals, and – critically – shared data access. Marketers are becoming more technically fluent and learning what their data warehouse can actually do. Data teams are helping shape campaign strategy. Both are evaluating martech together to make sure new platforms align with broader data architecture goals.
That kind of CTO + CMO alignment is what separates the strongest teams – not just the collaboration itself, but a shared understanding of the tech stack and a unified strategy for using it.
4. Building agentic AI on a solid predictive foundation
To no one’s surprise, AI continues to dominate martech conversations everywhere.
Gartner claims “agentification” split the CDP category in two this year. The Marketing AI Institute cites that among all trends on the rise, AI agents and autonomous workflows have pulled ahead as the #1 technology marketers expect to matter most, with 27% naming it their top pick – well ahead of generative content at 17% and predictive analytics at 7%.
But here’s what sometimes gets lost in the agentic hype: predictive AI is what actually feeds the “autonomous” part of autonomous workflows.
Predictive models inform segmentation logic, tailored content recommendations, and optimized send times by identifying things like churn risk, ideal channel selection, likelihood to purchase, and next-best action. An agent deciding what to do next is only as good as the signals its decisioning engine sits on top of.
Think of predictive AI as the traffic and route data being read in real time by your GPS, and agentic AI is the voice telling you “turn left in 500 feet.” The turn-by-turn instruction only works because of the traffic/map data underneath it. If you’ve got bad data, you get confidently, calmly guided into a traffic jam.
Marketers are using these signals to prioritize high-impact audiences and design campaigns that are relevant from the very first touch without needing prompt engineering or creative direction for every decision – just good data, solid modeling, and infrastructure that connects insight directly to execution.
The brands getting the most out of agentic AI right now aren’t the ones who skipped straight to agents. They’re the ones who already had centralized data natively feeding these machine learning models and scoring their customer base. That data infrastructure (not which agentic vendor or LLM to pick) is still being underrated.
The takeaway: Enterprise marketing belongs in the data layer
All four of these trends point in the same direction: modern marketing is becoming data-native, privacy-centric, AI-driven, and cross-functional. The organizations pulling ahead are the ones investing in infrastructure, not just tools.
Success in this environment depends on tech stacks built for this reality – not just for marketers, but for how the whole organization works with customer data. The winners won’t be the teams chasing the latest point solution. They’ll be the ones who invested in the speed, flexibility, and scale to make everything else actually work.
That’s where warehouse-native marketing platforms like MessageGears shine.
By giving marketers secure, direct access to live customer data, MessageGears eliminates the need for replicated customer profiles, reduces friction, and puts privacy and personalization on equal footing. It empowers marketers and data teams to work from the same source of truth and activate insight in real time.
The future of martech is composable, governed, intelligent – and it lives in the warehouse.