Final month, an information staff and a advertising and marketing staff sat in the identical room and talked previous one another for 45 minutes.
The engineers spoke in Delta tables and medallion structure. The entrepreneurs spoke in journeys, segments, and ship time optimization. Identical buyer. Identical information. Two utterly totally different languages. No one was unsuitable. They only could not hear one another.
That lifeless area — between the place information lives and the place it prompts — is the place thousands and thousands of {dollars} in buyer worth go unrecovered each week, at corporations with world-class information infrastructure and world-class advertising and marketing ambitions. They’ve the rocket ship. They forgot the launchpad.
Scott Brinker’s analysis report, The New Martech “Stack” for the AI Age, revealed in partnership with Databricks, places the correct title on what wants to interchange the structure that created this drawback. He calls it the composable canvas. The prognosis is precisely proper, and the route it factors is precisely the place this partnership is constructed to go.
What the composable canvas truly means for entrepreneurs
If you happen to’ve ever exported a CSV out of your information warehouse, emailed it to the marketing campaign staff, and waited three days for it to get uploaded into your advertising and marketing platform — that is the outdated stack mannequin failing you.
For twenty years, martech was inbuilt inflexible vertical layers: information on the backside, techniques of engagement within the center, campaigns on the prime. Every layer was its personal field. Getting these containers to speak to one another required pipelines, connectors, sync jobs, and groups of individuals whose whole job was shifting information from one system to a different. As Brinker’s report notes, integration remained a top-three problem for almost all of selling organizations surveyed — not in 2015, however in late 2025. After thirty years of distributors promising to unravel it.
The composable canvas is the architectural different: a unified information basis the place each software, out of your buyer engagement platforms, to your AI brokers, to your analytics, operates on the identical shared substrate, with out information ever having to maneuver. No middleware. No lag. No Sunday evening spreadsheet ritual.
Bryce Peake, former VP of Advertising and marketing Choice Sciences at Domino’s, places it plainly:
We have been making an attempt to switch the identical martech stack we have had because the web began interneting. Of us, we will need to construct a brand new one.
Trendy information platforms like Databricks present an open, shared basis that entrepreneurs, brokers, and apps can all function on collectively. And the enterprise case is concrete: “Pace: From months to minutes. Immediately, including a brand new advertising and marketing functionality means integration initiatives, information pipelines, and IT tickets. Within the composable canvas, new instruments and AI brokers plug right into a shared information basis immediately.”
The structure: 5 rings, one heart of gravity
Brinker’s framework organizes the composable canvas into 5 concentric rings, every with a definite position.
- Information Core: the unified basis of buyer, firm, content material, code, and management information; the gravitational heart of the whole lot
- Semantic Layer: shared definitions that make information constant and significant throughout each system that touches it
- CaaS (Context-as-a-Service): platforms like CDPs that package deal related information and context for the apps and brokers that want it
- Decisioning: the place AI engines optimize next-best actions and resolve rivalry when a number of brokers need to attain the identical buyer concurrently
- Apps & Brokers: the outermost ring, the place buyer experiences truly get delivered
The payoff of this construction is a dramatic discount in integration complexity. Within the outdated point-to-point mannequin, ten techniques might require as much as 45 integrations. Within the composable mannequin, every new functionality joins a coherent shared ecosystem fairly than an online of brittle pipelines. As Rick Schultz, CMO of Databricks, places it:
All CMOs try to unravel the identical three issues: effectiveness, effectivity, and self-service.
The composable canvas is how all three grow to be achievable concurrently.
Elizabeth Dobbs, AVP of Advertising and marketing Expertise at Databricks, describes the affect from firsthand expertise:
As soon as all of our advertising and marketing information was centralized within the lakehouse, we revisited fashions that had by no means totally leveraged that richness… The rebuilt mannequin transformed at 4X the earlier charge. From there, we shortly replicated the identical strategy to launch a extremely predictive account scoring mannequin in simply 4 weeks.
The final mile: the hole between your first-party information platform and a reside marketing campaign
Brinker is specific that this report is a North Star, not a step-by-step implementation information, and that is precisely what makes it useful. What it deliberately opens up is the practitioner query: how does a unified information basis truly grow to be a triggered marketing campaign? How does a decisioning layer get constructed for a advertising and marketing staff that does not converse information engineering? How do the 5 rings transfer from an structure diagram to a buyer expertise that fires on the proper second?
These questions are the place the true work of implementation lives — what Sew calls the final mile.
This is what the hole between the start line and the end line of the final mile truly seems like in apply:
- The autonomous brokers with nowhere to go. A advertising and marketing chief at a significant model not too long ago described a maddening state of affairs: her engineering staff had simply deployed an autonomous agent system on Databricks that might motive throughout their whole buyer information set in actual time. But it surely had no concept tips on how to set off a marketing campaign. In the meantime, her advertising and marketing staff’s lifecycle journeys had been nonetheless being fed by a batch file up to date as soon as a day. Two gifted groups constructing the longer term, with no visibility into one another’s work.
- The propensity rating no one acted on. A gaming model’s information science staff constructed a classy churn mannequin. The propensity rating fired accurately, flagging clients in danger. It went right into a dashboard no one was checking. The shopper felt nothing from the model in the meanwhile they wanted to really feel one thing… and left. As a result of the final mile by no means obtained constructed.
- The Sunday evening spreadsheet. A nationwide grocery retailer had a Databricks setting filled with provide eligibility information and a buyer engagement platform constructed for precisely this sort of personalization. The hole between them? 4 hours, each Sunday evening, and a CSV. Marketing campaign prep time: hours. The info was there. The activation functionality was there. No one had constructed the bridge. As soon as related, with provide eligibility flows robotically into buyer profiles and campaigns, triggering in actual time. These 4 hours grew to become minutes, each week, completely.
As Brinker notes,
The martech stack would not sit on prime of information. It’s information.
Architecturally, that is true. Operationally, for many manufacturers at the moment, the information and the marketing campaign nonetheless reside in utterly totally different worlds.
What closing the final mile requires
The manufacturers making the composable canvas actual aren’t doing it by shopping for higher instruments. They’re doing it by constructing the bridge — and that requires deep fluency in each the information platform and the advertising and marketing execution layer concurrently.
Advertising and marketing information structure constructed for activation. There’s a significant distinction between an information basis constructed for analysts and one constructed for entrepreneurs. The previous is optimized for queries, insights, and governance. The latter is optimized to set off a marketing campaign on the proper second, enrich a buyer profile in actual time, and supply an AI agent with the context it must decide. Getting there requires understanding each what Databricks can do and what the engagement layer truly wants from it.
Self-service analytics for advertising and marketing groups. Brinker describes pure language interfaces as a core function of the composable mannequin — and for good motive. That is achievable, however most advertising and marketing groups are nonetheless submitting tickets to get fundamental marketing campaign efficiency information.
The chance: construct analytics environments scoped to actual advertising and marketing use circumstances, so non-technical groups can reply their very own questions with out SQL, with no queue, and with out ready on information engineering. It is one of many highest-leverage implementations out there proper now.
AI brokers that run natively on the information layer. Probably the most highly effective agentic advertising and marketing workflows aren’t brokers bolted on prime of the present stack. They’re brokers that reside inside the information basis and execute via the engagement layer — pulling segments, validating information high quality, triggering campaigns, and measuring outcomes with out the CSV, with out the handbook handoff, with out the lag. Kumar Ram, VP and World Head of Advertising and marketing Information Sciences at HP, frames the strategic precept exactly:
We need to personal the core assemble of the information and infrastructure. If we modify companies, all we have to do is flip the activation layer on the prime. The inspiration stays with us.
Migrations that construct towards composability, not away from it. For manufacturers nonetheless operating on inflexible legacy advertising and marketing platforms, the transfer to a composable structure will not be merely a platform swap; it is an architectural resolution. Carried out proper, it positions Databricks as the information layer beneath an engagement stack that may flex and evolve as AI capabilities change. Elizabeth Dobbs captures the objective:
We have designed our stack like modular Lego blocks so the enterprise can transfer sooner. As new applied sciences and AI brokers emerge, we’ve the pliability to undertake the perfect instruments with minimal raise.
3 ways to begin constructing towards the composable canvas at the moment
Brinker is evident that this can be a 3-5 12 months architectural journey, not a rip-and-replace undertaking. The excellent news: each step delivers worth by itself phrases. Listed here are three beginning factors that persistently unlock probably the most speedy affect.
1. Get your advertising and marketing information into Databricks — truly into it.
The commonest model of “we’ve Databricks” is: the information engineering staff has Databricks, and advertising and marketing has a dashboard that often displays it. That is not a unified information basis. That is a reporting layer.
The primary transfer is consolidation: getting marketing campaign efficiency information, buyer behavioral alerts, loyalty information, and engagement historical past flowing into the identical lakehouse the place your transactional and operational information already lives. That is plumbing work — connectors, pipelines, information high quality checks — however it’s the inspiration the whole lot else relies on.
The speedy payoff is unified reporting: one model of fact that advertising and marketing, gross sales, and finance are all taking a look at. As Brinker’s report places it:
When everyone seems to be wanting on the similar numbers, calculated the identical means, conferences shift from debating information to deciding what to do about it.
That alone is well worth the funding. But it surely additionally ends the information tax that slows each marketing campaign down — the tickets, the exports, the ready.
The place to begin: Map the place your advertising and marketing information at the moment lives versus the place it must be. Determine the 2 or three information sources that might most change what advertising and marketing can do in the event that they had been unified — sometimes transaction historical past, behavioral occasions, and provide or loyalty information — and construct towards these first.
2. Construct one self-service analytics use case to your advertising and marketing staff
Most advertising and marketing groups are sitting one query away from an perception that might change how they run their campaigns. The issue is not the information. It is the friction between the marketer and the reply.
Brinker’s composable canvas places self-service analytics — particularly, pure language interfaces that permit non-technical customers question information immediately — on the heart of the mannequin. Instruments like Databricks AI/BI Genie make this achievable at the moment: a marketer varieties a query in plain English and will get a solution in seconds, with out SQL, with no ticket, with no three-day wait.
The secret’s constructing for a selected use case fairly than making an attempt to present advertising and marketing entry to the whole lot without delay. Begin with the query your staff asks most frequently and takes the longest to reply: marketing campaign conversion by section, provide redemption by retailer, churn alerts by buyer cohort. Construct the Genie area round that call. Let the staff use it. Watch what occurs when friction disappears.
One model’s marketing campaign supervisor went from a four-day turnaround on a fundamental efficiency query to a solution in seconds — after which requested 12 extra questions over the following 20 minutes, surfacing an perception about push notification timing that had been hiding within the information for 1 / 4. The friction was gone, and so was the ceiling on what she might discover.
The place to begin: Determine the only most typical information query your advertising and marketing staff escalates to the information staff. Construct a ruled, use-case-focused analytics setting round that query first. Develop from there.
3. Decide one AI agent use case and ship it
The most important mistake advertising and marketing groups make with AI is treating it as a method fairly than a apply. The composable canvas makes AI brokers genuinely operational — however provided that you begin constructing them on actual information, in actual workflows, in opposition to actual outcomes.
The simplest first agent use circumstances are slender, high-frequency, and related on to a marketing campaign end result. A QA agent that validates each e mail earlier than it sends. A personalization agent that pulls real-time stock or loyalty alerts and generates copy on the fly. A churn agent that displays behavioral alerts in Databricks and triggers a marketing campaign the second a buyer crosses a danger threshold — not three days later when a batch file runs.
What makes these use circumstances work is the structure: brokers that reside within the information layer and execute via the engagement layer: no CSV exports, no handbook handoffs, no lag. The marketing campaign is barely as clever as the information feeding it — which is precisely why the information basis comes first.
As Brinker notes within the report,
Probably the most useful agentic workflows cross system boundaries as a matter after all. Brokers are ravenous for context–relevant information, directions, and instruments for the precise objective they’re fixing for.
The composable canvas is what offers them that context. The agent is simply the factor that acts on it.
The place to begin: Determine one high-frequency, high-cost handbook course of in your marketing campaign operations — QA, segmentation, content material technology, efficiency reporting — and scope a single agent to automate it. Measure the time saved and the change in end result. Use that proof level to construct the case for the following one.
Why the window to construct that is now
The timeline on Brinker’s three-to-five-year imaginative and prescient is compressing sooner than most advertising and marketing leaders anticipated.
Databricks simply launched CustomerLake, an Agentic CDP natively embedded throughout the Lakehouse. Core CDP capabilities together with Buyer 360, identification decision, viewers constructing, marketing campaign automation, activation, and personalization can occur the place buyer information, AI fashions, and governance already reside — no middleware required.
Buyer engagement platforms like Braze are connecting on to Databricks via native Cloud Information Ingestion and OpenSharing. New platform capabilities like Lakebase imply full-stack advertising and marketing functions — entrance finish, transactional logic, and marketing campaign execution — can run on a single platform with a single governance mannequin, eliminating the necessity to sew collectively 4 separate techniques.
The sample Brinker identifies is already enjoying out: the platforms on every finish are getting smarter and extra immediately related. The combination layer within the center is getting thinner each quarter.
The manufacturers doing this work at the moment see the place the market goes. They’re constructing the information structure now so they don’t seem to be scrambling to catch up when everybody else figures it out.
The composable canvas describes the vacation spot. The final mile is the work between right here and there.
Obtain Scott Brinker’s analysis report: The New Martech “Stack” for the AI Age
Databricks and Sew are partnering to assist advertising and marketing organizations shut the hole between trendy information infrastructure and actual buyer outcomes. In case your group is sitting on a Databricks funding that it hasn’t totally activated for advertising and marketing, speak to Sew →