The Data OS joins every signal, campaign, creative, audience, conversion, CRM and revenue, into one governed source of truth. Schema imposed at campaign creation, not patched after the fact. No amount of dashboard rearranging replicates what you get when the joining keys exist before the data does.
Each platform measures what makes it look good. That is not conspiracy; it is product design. The problem is your P&L does not care which platform takes credit.
Meta reports 42 purchases. Google reports 38. Actual orders: 51. Both platforms claim credit for the same customer journey. You cannot reconcile this without a neutral source of truth.
Retargeting ROAS looks extraordinary because it intercepts customers who were already going to buy. The conversion was happening. You are measuring coincidence, not causation.
Upper-funnel video plants intent that converts weeks later on brand search. Platform attribution does not wait. The channel that started the journey gets no credit for the sale.
High CTR, low order value, high return rate. You find out three months later, if someone connects the datasets manually. Most teams never do.
Forrester found that 32% of analytics time is consumed managing data quality problems, not extracting insight. That is a person and a half doing work a joined data model eliminates.
Three dashboards, three different ROAS figures for the same period. The CFO dismisses all three. Marketing loses budget authority because the data cannot defend itself.
"Prospecting-Q1-UK" in one account, "UK_Prosp_Cold_Jan" in another, "Brand-Awareness-Top" in a third. Now try comparing last January to this January. You cannot. The data is structurally broken.
Account manager leaves. New agency starts fresh. Three years of creative learnings, audience fatigue curves, seasonal patterns: gone. You pay to rediscover what you already knew.
An agent optimising Meta spend without visibility into Google search demand, CRM pipeline, or offline revenue has no context for its decisions. Fragmented inputs produce confident, wrong answers.
Not scraped after the fact, not reconciled in a spreadsheet. Structured at creation with consistent keys, so every object in the lake is queryable against every other.
Every campaign and ad set across all platforms: spend, impressions, reach, frequency, CPM, CPC, CTR.
100+ data points per creative: format, length, hook type, talent, offer, copy angle, call-to-action. Queryable like conversion data.
Audience type, seed source, lookalike degree, exclusion logic, funnel stage at targeting. Every audience has a lineage.
Every campaign object mapped to See, Think, Do, or Care at creation. Budget allocation by funnel stage is reportable from day one.
Consistent naming conventions enforced at creation across 13+ business dimensions: market, funnel stage, channel, audience type, creative ID, and more. Every object carries a Maaten ID, the universal joining key that links campaign to creative to audience to conversion.
Session quality, time on site, scroll depth, micro-conversions. Joined to the ad object that drove the visit, not just the last click.
Platform-reported and server-side, deduplicated where possible. Margin signals and AOV alongside volume, so revenue quality is measurable.
Pipeline value, deal stage, close rate, time to close. Paid media investment connected to CRM events it generated, not just the leads.
Gross margin, net margin, subscription value, refund rates. Not just revenue volume but revenue quality, traceable to the campaign that drove it.
Every prior period, same schema, same taxonomy. Year-over-year comparisons that are structurally valid, not manually assembled each time.
Actual spend per platform, per campaign, per day. Reconciled against invoices and API-reported figures to catch discrepancies before they compound.
Every action logged with rationale: what changed, why, what the expected outcome was. Fully auditable, queryable, attributable.
Every dashboard tool in the market is trying to make sense of data that was already messy when it arrived. They reconcile exports, rename columns, build lookup tables, and call it a "unified view." It is not. It is expensive rearranging of fragmented inputs.
Maaten imposes naming conventions and a Maaten ID at campaign creation. Before the first pound is spent, every campaign object is tagged across 13+ business dimensions: funnel stage, channel, market, audience type, creative ID, and more. This is the structural fix no amount of post-hoc processing can replicate.
Maaten's ELT pipeline is proprietary. No Fivetran, no Supermetrics, no third-party connector that breaks whenever a platform updates its API. The pipeline was built for this schema and maintained in-house by the same team that built the lake.
Clean data at creation means every downstream query returns a meaningful answer, not an approximation, not a best estimate. A number you can defend in a board meeting.
The platforms will never agree on attribution. The only way to get a number you can trust is to build the joining keys before the data exists, not after you need the report.
Same funnel stage, same market, same audience type. Diagnose whether CAC decay is seasonal or genuine, something a 90-day platform window cannot do.
Same creative, controlled for audience type and funnel stage. Does the TikTok video also win on Meta? Now you can know.
Which channel performs best for which segment, instead of letting each platform's algorithm guess privately and report selectively.
Google's "100 leads" claim measured against Facebook video that drove awareness four weeks earlier. The lake holds both. You decide what counts.
Holdout groups stamped at creation, not approximated later. The math is clean because the taxonomy was clean from the start.
MMM requires clean, consistent, long-running data. The lake provides exactly the structure these models need to return reliable outputs rather than plausible-sounding guesses.
100+ creative metadata points per asset, queried against revenue outcomes. Know which hook type drives your best LTV customers, not your best clicks. This does not exist elsewhere.
A media buyer goes directly into Meta or Google Ads Manager. Maaten's monitoring detects it, flags the schema gap, suggests the fix, and on approval pulls the campaign back into the taxonomy. Off-platform creation no longer creates permanent silos.
The data dysfunction Maaten solves has been quantified by every major research house. The numbers are consistent. The industry has just not had a structural fix.
Of every media dollar wasted from poor data quality. The same study found 32% of analytics team time consumed managing data quality problems, not analysing anything.
Only 38% of marketers evaluate holistic ROI by measuring traditional and digital media together. The other 62% are making channel allocation decisions on partial information.
More likely to report significantly above-average ROI. Companies using data and analytics intensively are 2.6 times more likely to achieve significantly higher returns than competitors who do not.
Cost efficiency improvement for brands with mature data-driven marketing practices. BCG found the same cohort also reports 11% incremental revenue above peers without unified data infrastructure.
Measured's research shows Meta's attribution methodology structurally over-attributes conversions to Meta-owned touchpoints. Not a bug; the architecture. Without a neutral lake, you cannot see it, let alone correct for it.
The IAB recommends centralising cross-channel data in warehouses and lakes with a neutral governance layer applied at ingestion. That is the Data OS. Not a Maaten opinion. An industry-standard recommendation without a viable product, until now.
None of these tools are bad. Each solves a real problem. The issue is that none of them address data quality at the point of creation, which is the only place it actually matters.
Large companies pay a team of ten to twenty specialists to stitch together the following boxes. The boxes were never designed to share schema, so they never do.
Governs taxonomy. Requires separate ETL and BI. Does not create campaigns or ingest performance data.
Moves data from source to destination. Does not clean it, structure it, or make it queryable. Breaking changes in platform APIs arrive with no warning.
Powerful infrastructure. Requires a data engineer to build and maintain the schema. That schema never includes campaign-creation-level taxonomy because it was never part of the workflow.
Visualises what is in the lake. Requires an analyst to build every view. Self-service in practice means: a data analyst builds it and everyone else reads it.
Sits on top of the lake and applies models. Still depends on the quality of what is underneath it. If the lake is fragmented, the model outputs are plausible, not reliable.
Someone's job is to notice when the numbers disagree and investigate. That someone spends 32% of their time on it, according to Forrester. And they still do not fix the underlying problem.
The lake, the schema, the naming conventions, the ELT, the measurement, and the creation surface in one internally connected system. No integration team. No six-month setup project. No boxes that never learned to share keys.
Most tools deliver the same output on day one as they do on day 730. The Data OS is different. The longer it runs, the more it knows, and the harder it becomes to leave behind.
This is not lock-in by contract. It is lock-in by value accumulation. Your data becomes the competitive moat, not our product. We build and maintain the infrastructure that makes it possible.
They build horizontal tools. They do not impose naming discipline at ad set level across ten platforms. They have never built schema control inside the ad platforms. They do not earn the right to ingest customer ad accounts. These are capabilities built through years of narrow, specific integration work, not general-purpose AI capability.
Agent startups build on whatever data their customers already have, which is fragmented. The agent is only as good as the context it operates in. Without the naming conventions, the unique IDs, and the unified schema underneath it, even a very good agent is optimising a fragment and calling it the whole picture.
Showmax, Netflix competitor, acquired by Canal Plus
Faster campaign setup across every platform. 80% data cost reduction. The Data OS and Agent OS running together in production, on a real international account, right now. Not a pilot. Not a proof of concept that lives in a sandbox.
Successful POC. Now in ongoing production use.
The Data OS is the fuel. The Agent OS is the workforce that runs on it.
The Data OS powers the Agent OS. Learn what the agents do with it.
The Data OS is live from day one, schema enforced at campaign creation. Not a future roadmap item; not a premium add-on.
Typically 4 to 6 week onboarding. We bring the infrastructure, you bring the accounts.