Nobody can tell you who owns that table.
That's the actual problem. Sphere puts owners, lineage, access rules and one golden record per customer, product or supplier into the systems you already run, not into a policy document nobody reads.
Book a data governance scoping call- Owner
- Sales operations
- Sources
- Salesforce · NetSuite · Billing
- Match
- 3 records merged
Book a data governance scoping call
Tell us what's driving it: an audit, an AI rollout, a merger or reports nobody trusts. We'll scope the first step.
Your call is with one of oursenior data architectsTechnology partners
Snowflake
Databricks
AWS
Microsoft Azure
Google Cloud
Governance on paper protects nothing.
A policy document says who should own data. Governance in the pipeline makes sure they actually do, every time the data moves.
The catalog knows who owns what. Here's what that looks like.
See a catalog entryEvery critical table, with an answer attached.
Who owns it, where it came from, who can see it, whether it's sensitive and when it was last checked. The questions that used to take a week of Slack messages get answered in one place.
finance.customer_revenueCertified- Owner
- Finance · Revenue operations
- Steward
- Data governance lead
- Lineage
- SalesforceNetSuiteWarehouseBoard dashboard
- Classification
- Contains PIIConfidential
- Access
- FinanceLeadershipMasked for sales
- Quality
- 12 of 12 tests passing
Illustrative catalog entry.
A governance program people actually follow.
Built with your teams, not handed to them, so it survives after we leave.
Data assessment
Where data lives, who uses it, where quality and access break, ranked by risk.
Governance framework
Roles, decision rights and the operating model that fits your size, not a textbook one.
Stewardship & ownership
Named owners and stewards for critical data, with clear responsibilities.
Policies & standards
Definitions, quality thresholds, classification and retention, written to be enforced.
Master data management
Golden records for customers, products and suppliers, with matching rules and steward review.
Governance in the pipeline
Access controls, lineage and quality tests built into the platforms your data already runs on.
From "who owns this?" to governed by default.
Assess first, then put owners, rules and master data into the systems, not a binder.
Book a data governance scoping call- Weeks 1–3
Governance assessment
We map critical data, owners, access and quality gaps, and rank them by risk and regulatory exposure.
You getA ranked gap map and a remediation plan. - Weeks 3–6
Framework & owners
We agree roles, decision rights, definitions and policies with your teams, and name owners and stewards.
You getA framework your people helped write. - Weeks 5–12
Enforce & master
Access rules, lineage and quality tests go into your platforms, and golden records are built for the core entities.
You getGovernance running in production. - Week 12 onward
Stewardship
Dashboards for quality and access, steward workflows for exceptions and training for your team.
You getA program that runs without us.
Governance that shows up in the numbers.
Governance and master data work from the published case studies.
a year recovered from one margin-formula discrepancy (estimated)
After a merger, PavingX found money hiding between two estimating systems.
Unify, a governed data layer over merged ERPs, CRMs and estimating systems, resolves duplicate customers into golden records, normalizes cost codes and enforces source-system access, which surfaced the margin gap.
Read the PavingX storylower employee turnover
A major bank built workforce analytics it could trust.
An AWS-based HR analytics platform integrates HR systems through Informatica, is governed with Collibra and reports through QuickSight and Tableau.
Read the banking storystandards met in one secure Azure data environment
A healthcare organization put patient data under one set of rules.
A secure Azure environment integrates patient, physician and administrative data, with ETL-automated compliance reporting and real-time Power BI dashboards.
Read the healthcare storyless data ingestion time
One client, one ID, across every provider.
Sphere restructured debtor data in the warehouse, standardized client identification across third-party providers and tuned ingestion, indexing, caching and resource allocation on AWS.
Read the commercial finance storyWant results like these?Tell us about your project. A senior architect replies within one business day.
Book a data governance scoping callFrom the people who signed off on it.
Leaders who trusted Sphere with the data their business runs on.
“Sphere approached AI transformation the right way — starting with workflows, data governance, and operational trust instead of simply deploying another AI tool. Their team built a secure and scalable AI infrastructure that allows us to capture institutional knowledge, protect sensitive data within our environment, and continuously expand our AI capabilities with confidence.”
“Sphere consistently demonstrated a high level of accountability, technical expertise, and executive partnership. Their team remained deeply invested in our business outcomes and successfully supported us through complex technology and operational challenges.”
“Sphere was a valuable partner in helping us advance our AI-native product lifecycle initiatives. Their combination of product thinking, engineering expertise, and execution discipline enabled the delivery of impactful solutions that improved our platform and accelerated innovation.”
Architects who build production data systems.
Your first call is with the people who design Sphere's data and AI platforms, and they stay with the project after you sign.
Built products and led high-scale engineering teams at HP Software, Gett and Google before leading Sphere's technology practices.
Architects enterprise AI platforms that turn complex operations into secure, production-ready systems built to hold up at real-world scale.
Led high-scale operational systems and modernization at Gett and bp pulse. Now builds automation-driven platforms and intelligent workflows.
More than a decade designing and scaling software from first MVP to enterprise-level systems across FinTech, transportation and real-time streaming.
Governance on your platforms, not another silo.
Snowflake, Databricks Unity Catalog, your cloud's native controls or Collibra.
Classification, access and audit trails mapped to GDPR, HIPAA and SOC 2.
Stewards trained and workflows in place before we step back.
Snowflake
Databricks
AWS
Microsoft Azure
Google Cloud
Salesforce
SAP
NetSuite
Dynamics 365
HubSpotCloud data platforms and the business systems we connect them to.
21 years of engineering. Our top 20 clients stay 7+ years.
Independent recognition for the AI work Sphere delivers, from Clutch reviewers to global AI award juries.
Top Artificial Intelligence Company · US 2025
Top AI Code Generation Company · US 2025
Top Chatbot Company · US 2025
Top Recommendation Systems Company · US 2025
Top AI Text Generation Company · Florida 2025
Top App Development Company · Manufacturing 2025
Global Generative AI Award · 2026 Winner
Best in AI for Documentation Validation · 2026
Best in AI-Powered Invoice Auditing · 2026
Global Recognition Awards · 2026
Global Tech Award Winner · 2026
Most Innovative Technology Consulting Firm · 2026
Data governance, answered directly.
Don't see yours? Ask it on the call.
Book a data governance scoping callWhat's the difference between data governance and master data management?
Governance sets the rules: who owns data, who can use it, how quality is measured and how lineage is tracked. Master data management applies those rules to your core entities, like customers, products and suppliers, so each has one trusted golden record across systems.
We tried governance before and it became a document. Why is this different?
Because we build it into the pipelines and platforms. Ownership lives in the catalog, access rules are enforced where data is queried, and quality tests run on every load. It works whether or not anyone rereads the policy.
Do we need a governance tool first?
No. We work with what you have, from Collibra to Databricks Unity Catalog to your cloud's native controls, and recommend a tool only if the gaps call for one.
How does this help with GDPR, HIPAA or SOC 2?
We classify sensitive data, apply access rules and keep lineage and audit trails, which is the evidence auditors ask for. We align your data with those frameworks; certification stays with your auditor.
How long does it take?
A readiness assessment maps the gaps in 2–3 weeks. Putting owners, rules and master data into production typically takes 6–12 weeks, depending on scope.
What does it cost?
The 2–3 week readiness assessment is free. For the build, we quote a fixed price once the gaps and priorities are clear.
Give every critical dataset an owner and a rulebook.
Book a scoping call with a Sphere data architect. You'll leave with:
- Where ownership and lineage break today
- Which master data to fix first
- A fixed scope for the first step
Book a data governance scoping call
Tell us what's driving the governance work. We'll reply within one business day.
Your call is with one of oursenior data architects