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DATA QUALITY & GOVERNANCE

Data quality that holds up under scrutiny

Data quality is the structured, verifiable foundation for reliable analytics, sound decisions, and AI you can trust. The audit is the first step. From there, we put controls, clear ownership, and monitoring in place that keep the standard in the long run.

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Data Quality Scorecard (Example) Score 68 / 100
Completeness82%
Validity74%
Uniqueness61%
Timeliness55%
Consistency66%
Accuracy70%
A deep dive across 6 dimensions, prioritized risks, a clear path to a reliable, AI-ready data foundation. (Graphic AI-generated)
43%

of COOs name data quality as their top data priority.

5 Mio. €

average annual loss from poor data quality according to IBM, over $25M for 7% of organizations.

85%

of AI models fail according to Gartner due to poor data quality or a lack of relevant data.

20+
Years of in-depth experience with data, with a particular focus on Marketing Analytics.
  • Lavera Naturkosmetik
  • Allianz
  • Fresenius
  • Douglas
  • Aachener Grundvermögen
  • Fielmann
  • logo redbull 51968bc2
  • Telefonica
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  • Roche
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    • HMA_Team_Tobias-Lanzl

    Tobias Lanzl
    Marketing Data Stack Manager
    & Data Quality Expert

    +49 89 219 099 021

    Book your free 30-minute initial consultation now.

    Contact
    Our Stance

    Data quality isn’t a backend topic. It’s your ground truth.

    In a marketing ecosystem built on Google, Meta, Adobe, and others, the quality of your data decides whether a campaign pivot pays off or turns into an expensive mistake. That’s why we treat it like a standard you maintain and prove, not a cleanup you do once.

    Most organizations work reactively: a wrong number only surfaces once a user complains, or months later. By then, budgets have been misallocated, reports have been questioned, and trust has been spent.

    Our guiding principle is confidence over compliance: controls don’t exist to produce paperwork, they exist to stop a wrong number from ever reaching a board deck. And they’re risk-based: the greatest effort goes into the data whose errors would cost the most.

    Without a reliable foundation, AI only speeds up the rate at which wrong decisions get made.

    Reactive, today
    Errors get noticed once they hurt
    Scattered definitions
    “Active customer” means something different everywhere
    Firefighting
    Analysts spend their time chasing down errors
    “Is this number right?”
    No proof of why you can trust it
    Proactive, with a standard
    Data is validated before it reaches the user
    One definition
    Every metric defined exactly once, valid everywhere, as laid out in our Marketing Measurement Strategy.
    Automated testing
    Tests run with every pipeline build
    Provability
    Data lineage, logic, and tests, traceable at any time
    Your Foundation in the System

    One of five layers: the one everything else stands on.

    AI-driven marketing analytics isn’t a tool question, it’s an architecture question made up of five layers. Data Quality & Governance is the prerequisite the four layers above it can’t stand without. Only verified data makes a Semantic Layer unambiguous, a Context Layer trustworthy, and Agentic Intelligence safe.

    Only verified data makes the layers above it trustworthy. A Semantic Layer only becomes unambiguous, a Context Layer only becomes trustworthy, and Agentic Intelligence only becomes safe once the foundation holds.

    Details about the Hopmann AI Foundation
    The Core of the Audit

    The standard we check against

    Before we put controls in place, we define what “good data” actually means in your context. Every Hopmann audit measures your data against these six dimensions: made concrete and testable for analytics data, not left as abstract governance terms, each with a clear failure pattern behind it.
    Completeness

    Are all expected records and fields present?

    Failure pattern:
    Row count below the expected volume; required fields with null values.

    Validity

    Does the data match the expected format, type, business rules, and source?

    Failure pattern:
    A discount field shows 150% due to an upstream conversion error.

    Uniqueness

    Are there any unintended duplicates in the system?

    Failure pattern:
    The same customer under two IDs inflates the customer count.

    Timeliness

    Is the data as current as the business needs it to be?

    Failure pattern:
    The executive dashboard still shows yesterday’s numbers at 10am.

    Consistency

    Is the same fact represented the same way everywhere?

    Failure pattern:
    “Active customer” means something different on the sales dashboard than on the marketing one.

    Accuracy

    Does the data correctly reflect the real world?

    Failure pattern:
    Warehouse revenue doesn’t match Finance’s number for the same period.

    Our Method

    Four control gates instead of one final check

    Quality problems are cheapest to catch at the point where they originate. That’s why our controls sit at four defined gates along the pipeline, not just in reporting, where an error is most expensive and most visible.

    Gate 1

    Ingestion — Source → Raw
    Checks whether the source delivers completely, on time, and in the expected format.

    Catches: API silent failures, schema drift, partial loads.

    Gate 2

    Transformation — Raw → Marts

    Validates business logic, joins, referential integrity, and deduplication via dbt tests

    Catches: Fan-out joins, duplicate primary keys, orphaned records.

    Gate 3

    Consumption Assurance

    Runs automated sanity checks on final figures as they are served to downstream tools and executive dashboards.

    Catches: stale dashboards, metric drift, broken filters.

    Gate 4

    Security & Exposure Control
    Prevents credentials and metadata from leaking into code, logs, or across environments.

    Catches: hardcoded API keys, cross-client contamination.

    Not everything needs the same rigor: risk-based classification

    Critical

    User-facing reporting, revenue and performance KPIs, shared dimension tables.

    Full control coverage, actively monitored.

    Standard

    Raw extracts and models with limited downstream dependency.

    Core controls, reviewed periodically.

    Low

    QA, dev, and sandbox datasets, one-off extracts.

    No active monitoring.

    Every dataset and every KPI is assigned to one of three tiers. The tier determines which controls are mandatory, recommended, or optional, so effort flows to where errors would cost the most.

    What Day-to-Day Operations Look Like

    Data quality as a visible status, not a promise

    Instead of claiming your data is right, we make it readable: a weekly scorecard for your critical domains, built with the tools your team already uses. If a value drifts outside its target range, that doesn’t just sit as an entry on the scorecard: an active alert goes automatically to the responsible owner, in the channel your team already relies on.

    Data Quality Scorecard Week 24 · Critical Domains
    Domain Tier Tests Freshness Open Issues
    Revenue Reporting Critical 38/40 (95%) On time 1 (Medium)
    Marketing Performance Critical 22/22 (100%) On time 0
    Customer Master Critical 15/16 (94%) +2h delayed 1 (High)
    Web Analytics Standard — — —

    Illustrative example. No new monitoring product required: Tests run in the existing deployment,
    and alerts are automatically sent to the responsible owner via your usual channel. (Image generated by AI)

    Why Hopmann

    Four reasons we’re different from tool vendors

    We don’t just flag errors. We build a verified foundation of accuracy that your team and your AI systems can actually trust.

    End to end, not a single tool

    We combine data quality expertise with marketing, BI, and AI enablement: from ingestion to dashboard across your entire Marketing Data Stack from a single source.

    The Difference: A single point of contact for the entire pipeline instead of yet another software subscription you have to manage yourself.

    20 years of practice & experience

    Drawn from years of hands-on experience in global enterprise environments, our proven frameworks, tests, and checklists come from real-world projects — not theory.

    The Difference: Fast rollout using battle-tested assets, driving down costs with every subsequent deployment.

    Anchored directly in the Context Layer

    Results don’t disappear into report appendices. They are embedded right where your AI systems check whether they can trust the data before acting on it.

    The Difference: Every metric is defined exactly once, seamlessly integrating into the Context Layer of your architecture.

    Proactive & risk-based

    Confidence over Compliance: Effort is targeted specifically at the data and processes where errors cost the most. These are validated before they reach the customer.

    The Difference: Automated testing instead of manual firefighting as a secure foundation for your AI.

    The Business Value

    What a data quality standard actually changes

    For a CMO, data quality isn’t an IT topic. It’s the basis for every budget decision, every report to leadership, and every AI initiative in marketing. It pays into four outcomes that sit directly in your area of responsibility.

    Reporting
    Reliability

    Comprehensive testing gives every client dashboard an internal seal of quality. Figures are no longer questioned, but accepted as a solid foundation.

    Target: 95%+ passed tests on critical data

    Protected
    Budgets

    Flawed attribution or duplicate conversions lead to misallocated budgets. Reconciliation against the source stops this before spend is misdirected.

    Less misallocation, less rework

    Reliable
    Numbers

    When a metric is questioned, data lineage, transformation logic, and tests provide proof—even across countries and in compliance audits. Versioned logic additionally shows which rule applied at any given time.

    Every metric verifiable & traceable

    Scalability &
    AI Readiness

    A reusable toolkit of tests and standards reduces the cost of every additional rollout and creates the foundation required for AI to access data safely.

    Established once, reused endlessly

    Proven Methods from 20 Years of Practical Experience

    Our Offer

    Depending on your starting point and goals, we offer structured entry formats as well as individually scalable audit options and trainings. For the concrete setup, we recommend a short alignment call.

    For one clearly scoped use case

    Use Case Audit
    from 4.900 €

    Certain types of reporting must be reliable: the dashboard for management, your ROAS analysis, and campaign steering. We review the underlying data across six dimensions and show you what you can rely on and where action is needed.

    Discuss your use case

    Existing Setups

    Optimization & Expansion

    Price on request

    A 6-dimensional deep dive into your entire critical data infrastructure. We examine both the top-level figures and the operational levels below them, where day-to-day decisions are made. You’ll receive a prioritized list of risks with their business impact and a concrete action plan.

    Request your audit

    End-to-End Solution with AI Integration

    Premium Package

    Price on request

    Continuous monitoring, executive scorecard, and real-time anomaly detection. Full synchronization with the Context Layer so that your AI agents are aware of the quality status of your data when generating their responses.

    Send a request

    Request your free 30-minute initial consultation.

    Tobias Lanzl - Hopmann

    Tobias Lanzl
    Marketing Data Stack Manager &
    Expert for Data Quality

    What our customers often want to know.

    FAQ on Data Quality

    What exactly is a data quality audit?

    A structured review of your data flow to identify risks, gaps, and weaknesses that affect reporting, targeting, and decisions.

    Why is data quality the prerequisite for AI?

    AI needs accurate, well-structured data with clear context. If that data is incomplete, inconsistent, or poorly defined, AI scales the errors, resulting in misleading insights and costly hallucinations.

    Which core dimensions do you measure?

    Six: Completeness, Validity, Uniqueness, Timeliness, Consistency, and Accuracy, made concrete and testable for analytics data.

    Is this just a one-off audit?

    The audit is the first step. From there, we put controls, clear ownership, and monitoring in place, and keep the standard up through ongoing oversight. Data quality is a culture, not a one-time project.

    Do we need a new tool for this?

    No. The standard runs on the tools and channels your team already has: tests run in your existing deployment, alerts go to your usual channel. Automation before manual review, built in rather than added as a separate audit step.

    How is Hopmann different from tool vendors?

    We don’t sell a software subscription. We combine data quality- & governance expertise with marketing, BI, and AI enablement, and anchor the results directly in your Context Layer.