Data consulting
Data strategy, architecture, and analytics consulting.
Senior-led consulting for the data foundations that everything else depends on. From assessment to architecture to analytics, built by the same team that operates the DataBillity platform every day.
The problem
Why data initiatives stall.
Fragmented systems
Data lives in dozens of tools that don’t talk to each other. Every integration was urgent when it shipped. None were designed as part of a whole.
Unclear ownership
No single team owns data quality, lineage, or access governance. Requests route through tribal knowledge, not architecture.
Consent gaps
Data was collected before privacy regulation caught up. Consent records are missing, inconsistent, or disconnected from the data they govern.
AI on shaky foundations
AI projects get greenlit before anyone asks whether the underlying data is clean, governed, consented, or even accessible to the models that need it.
Our approach
How strategy, architecture, and analytics connect.
Strategy
Define where you’re going. Align data priorities to business outcomes, executive visibility, and regulatory reality.
Architecture
Design how you get there. Cloud-native schemas, ingestion patterns, governance layers, and consent coupling that are built to last.
Analytics
Make it useful. Dashboards, metrics frameworks, self-service layers, and customer intelligence that leadership actually acts on.
How it works
From discovery to delivery.
Every engagement follows a clear progression. You can start at any stage.
Discovery conversation
We learn your current state, priorities, and constraints before proposing anything.
Data estate assessment
A structured audit of your systems, data flows, quality, and governance posture, with a scored findings report.
Architecture & build roadmap
A target architecture, migration plan, and prioritized build sequence aligned to your timeline and budget.
Delivery or ongoing advisory
We build it with you, or we advise while your team builds. Either way, the same senior practitioners stay accountable.
Data strategy & architecture
Services in detail.
Data estate assessment
Comprehensive audit of your current data landscape: systems inventory, data flow mapping, quality scoring, and a prioritized findings report with remediation roadmap.
Data lake & warehouse design
Cloud-native architecture for Snowflake, Databricks, or BigQuery. Schema design, ingestion patterns, partitioning strategy, and cost optimization.
Governance framework
Tooling and process design for data classification, role-based access control, lineage tracking, and regulatory compliance alignment.
Consent infrastructure
Consent capture architecture and coupling patterns built to satisfy GDPR, CCPA/CPRA, PIPEDA, and more. Consent travels with the data, not alongside it.
Migration roadmap
Phased migration plans from legacy systems to modern cloud-native architecture. Dual-run strategies, rollback planning, and validation gates included.
Vendor evaluation
Independent evaluation of data platform vendors and AI/ML infrastructure. Weighted scoring, proof-of-concept design, and contract review support.
Analytics & business intelligence
Turn data into decisions.
Executive dashboards
KPI dashboards in Tableau, Power BI, or Looker that answer the questions leadership actually asks, not the ones that are easiest to build.
Metrics framework
Structured metrics framework connecting board-level KPIs to operational definitions. Every metric has an owner, a source, and a refresh cadence.
Self-service analytics
Semantic layer design that enables business users to explore data without writing SQL. Governed access, consistent definitions, and audit trails built in.
Customer analytics
RFM analysis, cohort analysis, lifetime value modeling, behavioral segmentation, and churn prediction. Actionable intelligence, not vanity metrics.
Data quality monitoring
Automated data quality monitoring with alerting, root cause analysis, and SLA tracking. Catch issues before they reach a dashboard or a model.
Proof
We build the infrastructure we consult on.
The DataBillity platform isn’t a demo. It’s production infrastructure that serves live customers, governed to the same standards we recommend. Our consulting practice draws directly from what we’ve built and what we operate.
- Consented data clean room architecture
- Governed data gateway with real-time consent verification
- Cross-network intelligence at scale
- Production AI models on fully governed data
AI readiness
What data foundations AI agents need before they can be trusted in production.
AI doesn’t fix bad data. It amplifies it. Before any model can be trusted in production, the foundations underneath it need to be sound.
Tech stack
Platform-agnostic. Experience-driven.
We work with the tools that fit your organization, not the ones that pay us referral fees.
Industries
Verticals with direct data delivery experience.
Our team has delivered data infrastructure, analytics, and AI solutions across industries where data complexity is a competitive advantage.
Deliverables
What you walk away with.
FAQ
Common questions.
How do engagements typically begin?
Every engagement begins with a discovery conversation to understand your current state, priorities, and constraints. From there, a data estate assessment sets the foundation for everything after it.
How long does a data estate assessment take?
Typically two to four weeks, depending on the number of systems, the complexity of your data flows, and stakeholder availability. You receive a scored findings report and prioritized roadmap at the end.
Do you work with our existing platforms or recommend new ones?
Both. We evaluate what you have, identify what’s working, and recommend changes only where the business case is clear. We’re platform-agnostic and don’t resell vendor licenses.
How is consent built into our data architecture?
Consent records are coupled to data records at the point of ingestion, not tagged after the fact. If consent is revoked, the data is automatically quarantined. This is the same architecture that powers the DataBillity platform.
What does AI readiness actually require?
Clean source data, consent governance, access controls that distinguish human from model access, lineage tracking, drift monitoring, and a governance framework that covers model risk. Most organizations need foundation work before AI can be trusted in production.
Can you work alongside our internal data team?
Yes. Most engagements are collaborative. We embed with your team, transfer knowledge continuously, and build documentation so your team can maintain everything we deliver.
Your data challenges. Our delivery teams. Let’s build.
Whether you need a data estate assessment, a cloud migration roadmap, or an embedded analytics team, our consulting practice is ready to deploy.