Why your data hurts — by role
Bad data infrastructure rarely looks like missing data. It looks like data that nobody trusts.
Marketing
Product copy is wrong, outdated or different in every channel.
Product
PIM, shop and CMS disagree on basic attributes.
C-level
KPIs depend on which spreadsheet you open.
Operations
Hours per week spent reconciling the same fields across systems.
Strategy & innovation
Every new market requires building data plumbing from scratch.
What 'data problem' actually means
- 01
Inconsistent product data: same SKU, three different names across systems.
- 02
Missing data ownership: when an attribute is wrong, no one is responsible for fixing it.
- 03
Broken integrations that silently drop fields, with no monitoring.
- 04
Multi-market setups where each country runs its own data fork.
- 05
Reports built on exports of exports — disconnected from the actual source.
Data is not missing. It is unusable.
Almost every company already has the data they think they need. The problem isn't volume — it's structure, ownership and trust. Until those three exist, more data only adds confusion.
How unusable data shows up
Unusable data isn't a technical problem first — it's an organisational one. The system reflects the lack of agreements about what data means, who owns it and where it lives.
- Teams build their own shadow datasets because the official one is wrong.
- Reports take days to produce because every number has to be re-validated by hand.
- AI and automation projects stall in week two when they hit the data layer.
- International expansion exposes structural problems that domestic teams had worked around.
How we design data infrastructure
We design data systems for use, not for storage. The core question is always: who needs this data, in what shape, to do what — and how do we keep it true at scale.
- 01
Audit the current data landscape: sources, owners, sync paths, known divergences.
- 02
Define the canonical model: what attributes exist, what they mean, where they're authoritative.
- 03
Establish ownership and data contracts between systems and teams.
- 04
Implement the integration layer: PIM, DAM, CMS, shop, ERP — wired with explicit rules.
- 05
Set up data quality monitoring so divergence is caught before it becomes a story.
Data becomes an asset you can plan against — not a daily firefight.
Where this lands hardest
PIM integration
Source systems → canonical PIM → channels
One product truth, propagated automatically — no more spreadsheet reconciliation.
Product data normalization
Legacy attributes → canonical schema
Inconsistent SKUs cleaned, deduplicated and locked to a contract.
Multi-market setups
Central model + market overlays
Local teams customize without forking. New markets launch in weeks, not quarters.
Data infrastructure
The business impact
1×
Single source of truth across product, marketing and ops
−80%
Time spent reconciling data across systems
Days
Instead of months to launch in a new market
AI-ready
Clean data is the prerequisite for everything else
Talk to us
Let's audit your data infrastructure.
Book a strategy call. We'll review where your data lives, where it diverges, and what it would take to make it trustworthy and usable.
