Turn AI Uncertainty
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Rebuild Data Trust
Before You Build AI
Your team doesn’t need more dashboards. They need to believe the data behind them.
Conflicting reports, duplicate numbers, low model accuracy, shadow reporting. Does this sound familiar?
The truth is, AI can’t succeed on shaky data foundations. Business leaders today are pulling back, not because they doubt AI’s potential, but because they don’t trust the data that fuels it.
That’s where we come in.
Talk to us about your data trust challenges
Our Solution:
AI Value Validation &
Data Trust Roadmaps
Before you invest in any AI or
GenAI build, we help you answer:
- Is my data trustworthy enough to drive results?
- Which AI use cases make sense for us today?
- How do we build momentum without the risk?
Through a 5-phase engagement, we audit your data, validate use cases, build prototypes, and deliver a clear, quantifiable roadmap that connects your AI ambition with execution-ready confidence.
What You Get
Actionable Deliverables
No vague assessments or shelf-ware reports. We deliver prototypes, trust scorecards, and issue dashboards fast.
Strategy-to-Execution Bridge
We reduce uncertainty between leadership goals and technical feasibility so you’re not throwing darts in the dark.
Tailored to Your Stack, People, and Priorities
Whether you’re cleaning up customer data or scaling governance enterprise-wide, we customize our playbook to your context.
Why It Matters
FROM
Conflicting numbers from multiple reports
TO
Single source of truth everyone agrees on
FROM
Hesitant decision-making
TO
Confident, data-driven leadership
FROM
Ad hoc, manual reporting
TO
Governed, reusable insights
FROM
Low AI/ML effectiveness
TO
Trusted, clean training data powering real results
Sphere’s 5-Phase Data Trust Roadmap
Phase 1: Discovery & Assessment (Weeks 1–4)
Goals:
- Understand root causes of distrust
- Identify priority data domains
Actions:
- Conduct stakeholder interviews (IT, business, analytics users)
- Perform data audits on key systems (CRM, ERP, data warehouse)
- Inventory critical reports/dashboards and inconsistencies
- Identify data silos and duplicated sources
Deliverables:
- Data trust baseline report
- Prioritized data domains (e.g., Customer, Revenue, Ops)
Phase 2: Governance Foundation (Weeks 5–8)
Goals:
- Set accountability and guardrails
Actions:
- Appoint Data Stewards and Data Owners for each domain
- Define policies (naming conventions, ownership, data usage)
- Create a data glossary and business term dictionary
- Document critical metrics and calculations
Tools:
- Data Catalog (Alation, Collibra, Microsoft Purview)
Deliverables:
- Operational governance framework
- Business glossary and metric documentation
Phase 3: Data Quality & Lineage (Weeks 9–16)
Goals:
- Increase transparency and accuracy
Actions:
- Set up data quality KPIs: accuracy, freshness, completeness
- Implement automated monitoring and validation rules
- Map end-to-end data lineage for 1–2 priority domains
- Set up alerts for broken pipelines or schema drifts
Tools:
- Data Quality: Monte Carlo, Talend, Informatica
- Lineage: Atlan, Purview, open-source tools (e.g., OpenLineage)
Deliverables:
- Dashboards with real-time data trust signals
- Lineage visualizations and issue tracking
Phase 4: Self-Service & Transparency (Weeks 17–24)
Goals:
- Empower users and eliminate "shadow reporting"
Actions:
- Enable governed self-service in Power BI / Tableau
- Roll out data catalog access with descriptions, owners, ratings
- Launch internal data literacy training
- Publish data trust metrics (e.g., % of trusted data assets)
Deliverables:
- Trusted reporting layer
- Data portal with searchable, verified assets
- Data Trust Scorecard for leadership
Phase 5: Scale & Sustain (Months 7–12)
Goals:
- Extend governance and automation
Actions:
- Add new domains (HR, Ops, Finance)
- Conduct quarterly data trust reviews
- Tie data quality to business SLAs or OKRs
- Collect feedback and iterate on data platform features
Deliverables:
- Enterprise-wide data trust operating model
- Quarterly improvement plans and dashboards
Success Metrics
90%+
Critical data assets with owners
95%
Of reports certified in catalog
+30% vs. baseline
Data quality score improvement
-50% within 6 months
Reduction in data-related rework
8+/10 in internal surveys
User satisfaction w/ data access
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Intelligent Automation
Streamline decisions and reporting with automated workflows powered by AI and governed logic.
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Data Modernization
Upgrade legacy data systems to a modern, scalable architecture built for AI and analytics.
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Data Engineering
Build robust, real-time data pipelines with lineage, monitoring, and storage optimized for trust and performance.
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Data Analytics
Enable self-service insights with verified dashboards, certified metrics, and a governed analytics layer.
Read MoreReady to Trust Your Data Again?
If you want to make AI real, start with data trust. Let’s build a foundation your teams believe in.
Get in Touch
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Client Partner
20
Years of Experience
4.9*
Clutch.co Review Score
97%
Client retention rate
1600+
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