The hidden tax of scattered data
Every quarter, enterprise teams burn hours hunting for “the right” revenue table or the latest customer metric, only to discover five near-identical versions in different lakes. A McKinsey analysis estimates that fragmentation erodes up to 20% of potential analytics ROI in large organisations. The hard cost shows up as licensing bloat and duplicated pipelines; the soft cost is even worse—missed market moves because insight arrived a week late.
The fix isn’t more storage or another catalog. It’s treating information the way product teams treat software code: give it an owner, wrap it in a contract, and release it through a paved interface that anyone can trust.
Why data products beat another one-size-fits-all lake
| Benefit | What it means for the business |
|---|---|
| Speed | Analysts call a ready-made API instead of spelunking through ad-hoc tables. |
| Quality by default | Tests, lineage, and policies ship with the asset, so audits take hours, not weeks. |
| Cross-domain creativity | Marketing can pull finance’s clean ledger feed into churn models without pinging a DBA. |
| Lower total cost | Reusable building blocks reduce redundant warehouses, a finding echoed in modern cost-allocation studies. |
Well-governed, easy-to-consume products are the real fuel for AI assistants, real-time optimisation, and self-service dashboards. No wonder 71 % of chief data officers surveyed in late 2024 ranked “productising data” as their top capability gap for the next fiscal year.
Success stories that moved the needle
- Netflix. By reorganising around domain squads (content, engagement, platform) and making each squad accountable for its own products, Netflix slashed data onboarding time for new features from weeks to hours.
- Shopify & Intuit. Both companies report double-digit percentage gains in model velocity after adopting a mesh-style operating model where teams publish versioned, queryable products with SLAs.
These cases share three traits: clear ownership, a self-service platform, and incentives that reward quality and reuse.
A six-pillar roadmap that won’t blow up your org chart
1. Anchor ownership in business domains
- Map capabilities (orders, inventory, customer, risk) to data product owners embedded in the same domain squad.
- Grant each owner backlog authority and a small budget for improvements—mirroring software product ownership.
- Publish the owner’s email and Slack channel in the catalog so consumers know where to file issues.
Why it works: A named steward drives accountability; questions no longer disappear into a shared mailbox. Recent research highlights the link between explicit ownership and accelerated value delivery.
2. Package raw assets into minimum-lovable products (MLPs)
Start small—one clean Orders feed, one near-real-time Customer 360. An MLP typically includes:
- Curated dataset or stream.
- Business glossary and sample notebook.
- Semantic contract (JSON Schema, Iceberg spec, or protobuf) living in the repo.
- Unit and integration tests that gates keepers run on each pull request.
MLPs prove the concept, surface edge cases early, and build consumer trust without six-month waterfall plans.
3. Pave golden-path APIs and SQL views
Golden paths are the paved roads that keep engineers out of the ditch. Provide a template CLI (or Git repo) that autogenerates:
- Read-only REST or GraphQL endpoints.
- Versioned tables or views for BI tools.
- CI pipelines for schema drift and row-level quality checks.
- Observability hooks (latency, freshness, error rate).
Teams fill in business logic; the scaffold enforces best practice. Engineers move fast, and security teams sleep at night.
4. Bake governance into the development flow
Governance fails when bolted on at release. Instead:
- Policy as code. Tag sensitive columns, retention periods, and GDPR scopes directly in YAML.
- Automated lineage. Stream metadata events from dbt, Airflow, and Snowflake into the catalog for real-time impact analysis.
- Tiered access. Reuse a single RBAC map from warehouse to API gateway so permissions never drift.
Auditors pull lineage graphs and policy docs with one click, shrinking compliance cycles.
5. Fund like a product, not a pet project
Traditional CapEx budgets reward one-off builds. Switch to a cost-recovery model that reflects actual usage.
| Model | Mechanics | Best for |
|---|---|---|
| Chargeback | Bill consuming teams per API call, row scanned, or stream subscription. | Mature FinOps culture, clear consumption metrics. |
| Showback | Expose costs on dashboards; no direct billing. | Early adoption—build awareness, avoid sticker shock. |
| Central pool + KPI guardrails | Platform funds products that meet adoption and freshness targets. | Fast-moving orgs where incentives trump invoices. |
By tying investment to usage and SLA adherence, you nudge squads to prune unused feeds and polish high-value ones.
6. Offer tooling, not tickets
A self-service portal or CLI should let any squad:
- Scaffold a new product repo.
- Register the asset in the catalog.
- Provision IAM roles and secrets.
- Spin up a staging environment.
Once these rails exist, the central data team shifts from ticket-janitor to coach—writing guardrails and evangelising best practices.
Launch plan: 90-day playbook
| Week | Milestone | What success looks like |
|---|---|---|
| 1-2 | Executive alignment | Budget ring-fenced; owners nominated. |
| 3-4 | Platform scaffolding live | Teams can spin a repo and CI pipeline in <15 min. |
| 5-6 | First MLP in production | Orders API v0.1 with schema contract, tests, catalog entry. |
| 7-9 | Early consumers onboard | At least two downstream apps using the Orders feed. |
| 10-12 | Retrospective & roadmap | KPIs baseline, backlog groomed for next two quarters. |
Keep scope ruthlessly thin at first—a single domain often uncovers enough cross-cutting issues (naming, PII tagging, latency) to refine standards before broad rollout.
Metrics that prove (or disprove) progress
- Mean time to first query (MTTFQ). Minutes from “I need X” to first successful call. Target < 10 min.
- Adoption rate. Unique analysts, services, or notebooks consuming each product per month.
- Contract breaches. Schema or SLA violations; aim for zero after 60 days in GA.
- Redundant asset retirements. Tables or dashboards deleted after a product replaces them.
- Team satisfaction. Quarterly NPS from both producers and consumers.
Dashboards that surface these metrics weekly keep the spotlight on value, not vanity.
Pitfalls to dodge
| Trap | How to avoid it |
|---|---|
| Giant upfront platform build | Ship a thin platform slice, then harden based on real usage. |
| APIs as an afterthought | The contract is the interface—block any back-door queries. |
| Misaligned incentives | Fund consumption, not pipeline count. |
| Tool monoculture | Allow language or framework choice inside clear guardrails. |
| Opaque cost models | Explain showback dashboards in plain language; tie them to decisions users care about. |
Stakeholder FAQs
“Will this slow my sprint cadence?”
A scaffolded repo and CI template cut boilerplate, so teams usually ship faster after the learning curve.
“Do we need new head-count?”
Often no—existing product managers or senior analysts can step into the data product owner role with targeted training.
“What about vendor lock-in?”
Contracts capture semantics at the edge. Implementation can live in Snowflake today, Iceberg tomorrow, without changing the consumer’s call.
What good looks like one year in
- Catalog browsing feels like an app store. Producers publish release notes; consumers leave ratings and open issues.
- Cost per insight drops. Financial dashboards show a downward trend in duplicated storage and queries per model run.
- Data literacy climbs. Quarterly surveys record rising confidence scores among business users.
- Audit queries become routine. Compliance reviews pull lineage graphs on demand rather than staging fire drills.
- Innovation flywheel spins. New features piggy-back on existing products, shrinking time-to-market for customer-facing capabilities.
When these signals surface, you’ll know the cultural shift has stuck and the marketplace mentality is alive.
Final thoughts
Raw data alone rarely wins deals or fuels break-out products; the value shows up when information is packaged, governed, and shared at the speed of business. By giving every critical dataset an owner, a contract, and a cost signal, you create a marketplace where teams exchange reliable building blocks instead of throwing spreadsheets over the wall. Start with a single domain, automate the boring parts, and let adoption metrics guide investment. One year from now, silos will feel like a relic—and your organisation will have the agility edge competitors can’t copy overnight.
Ben is a full-time data leadership professional and a part-time blogger.
When he’s not writing articles for Data Driven Daily, Ben is a Head of Data Strategy at a large financial institution.
He has over 14 years’ experience in Banking and Financial Services, during which he has led large data engineering and business intelligence teams, managed cloud migration programs, and spearheaded regulatory change initiatives.