Turn AI Uncertainty
Into Action

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

Get in Touch

Our Solution:
AI Value Validation &
Data Trust Roadmaps

Before you invest in any AI or
GenAI build, we help you answer:

  1. Is my data trustworthy enough to drive results?
  2. Which AI use cases make sense for us today?
  3. 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

Goals:

  • Understand root causes of distrust
  • Identify priority data domains

Deliverables:

  • Data trust baseline report
  • Prioritized data domains (e.g., Customer, Revenue, Ops)

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

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

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

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

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

Related Services

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AI Implementation

Design and deploy AI systems that align with validated use cases and trusted data, ready for real-world impact.

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MLOps

Operationalize AI with automated pipelines, model versioning, and monitoring to ensure stability at scale.

Read More
Intelligent Automation
Intelligent Automation

Streamline decisions and reporting with automated workflows powered by AI and governed logic.

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Data Modernization
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
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Enable self-service insights with verified dashboards, certified metrics, and a governed analytics layer.

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Ready 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.

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Hear From Our Clients

Sphere Partners
Selah Ben-Haim VP of Engineering at Prominence Advisors

Our experience with Sphere and their team has been and continues to be fantastic. We keep throwing new projects at them, and they keep knocking them out of the park (including the rescue of a project that was previously bungled by another vendor).

Sphere Partners
Ben Crawford Senior Product Manager at Enova Financial

I would expect to be delighted. It’s been a really positive experience, working with Sphere, and I would expect you to have the same.

Sphere Partners
Mark Friedgan CEO at CreditNinja

Sphere consistently prioritizes the needs of their clients, demonstrating both agility and teamwork. They bring innovative and well-considered solutions, consistently surpassing my expectations.

Sphere Partners
René Pfitzner Co-Founder at Experify

Sphere provided excellent full-stack development manpower to augment our team and work with us.

Sphere Partners
Bruce Burdick Chief Information Officer at Integra Credit

We've been working with Sphere and its excellent consultants since our founding. Their combination of offshore talent, pricing, and shift offsetting is hard to beat. They provide crucial augmentation to our in-house team. We simply couldn't achieve our production ambitions without their service.

Sphere Partners
Jemal Swoboda CEO at Dabble

The resources and developers that Sphere Software provides are skilled and have the required technical expertise to complete their tasks successfully, with the team easily scaled in either direction. The deliverables are always high-quality.

Sphere Partners
Arthur Tretyak Founder and CEO at IntegraCredit

With Sphere, we were able to migrate in half the time it would take to train an additional FTE…

Sphere Partners
Lee Ebreo VP of Engineering at Credit Ninja

These things would not have been achievable if we did not build our own in-house system. We augmented our development team capabilities using Sphere’s developer, who works very well with our Dev Lead in Chicago. Sphere’s developer was an expert in the new system, and continues to be an expert as we evolve it.

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Contact our Data Experts

Flexible, fast, and focused — Sphere solves your tech and staffing challenges as you scale.

Mario Schwarts

Managing Director Data & AI Practice

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20

Years of Experience

4.9*

Clutch.co Review Score

97%

Client retention rate

1600+

Completed Projects

Join 350+ Satisfied Clients

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Frequently asked question

A Data Trust can be described as a level of confidence your organization has in its data – in accuracy, consistency, transparency, and governance. When data is trusted, teams can rely on it to make decisions, power analytics, and fuel AI systems without second-guessing numbers or manually verifying reports.

You need a Data Trust Roadmap before implementing AI because even the most advanced models fail when built on bad data. Without clear ownership, quality controls, or lineage, AI outputs become unreliable and hard to explain, leading to wasted investment, low adoption, and increased risk. A roadmap helps you assess your current data state, fix what’s broken, and create a solid foundation so AI can deliver measurable, repeatable results.

Sphere performs a comprehensive audit across your systems, including CRM, ERP, and data warehouses. We assess data lineage, accuracy, governance, and consistency to establish a trust baseline and identify gaps. Our deliverables include trust scorecards, issue dashboards, and a clear roadmap to make your data AI-ready.

Our Data Trust and AI Readiness program is ideal for mid-sized to enterprise organizations that are exploring or piloting AI initiatives. It’s especially useful for firms struggling with siloed data, inconsistent reports, or failed AI/ML experiments due to poor data quality.

You’ll receive a comprehensive set of actionable deliverables, including:

  • Data trust baseline and scorecards
  • Prototypes for validated AI use cases
  • Governance playbooks
  • Real-time dashboards
  • Trusted reporting layers
  • An enterprise-wide data trust operating model

No shelf-ware. Just execution-ready tools.

The full roadmap spans 6–12 months, depending on your organization’s size and complexity. The first results—like trust scorecards and prototype validation are typically delivered within the first 8–12 weeks.

Both. Our service bridges strategy and execution. We help you validate which AI use cases make sense for your current state and deliver working prototypes, alongside a solid data foundation and governance model that ensures long-term AI success.

We’re tool-agnostic but work with leading platforms for data quality (Monte Carlo, Talend), lineage (Atlan, Purview), catalogs (Collibra, Alation), and BI (Power BI, Tableau). We adapt our methods to your existing stack to avoid disruptions.

By improving data quality and aligning AI investments with validated use cases, companies reduce wasted spend, avoid failed pilots, and shorten time-to-value. Our clients typically see a 30%+ boost in data quality scores and up to 50% reduction in rework due to bad data.

Schedule a free consultation with our AI experts. We’ll discuss your current data state, business priorities, and AI goals—and recommend a tailored plan to move from uncertainty to action.

Get Started Today

Get in touch