01
Discover
Map every source, profile data quality, trace lineage, and interview the teams who don't trust the numbers.
Output: a documented view of what data exists, where it lives, and where it contradicts itself.
Data Intelligence
The AI Problem Is Usually a Data Problem
We connect fragmented systems, modernize the architecture underneath, and govern what comes out, so decisions stop waiting on data.
What Is Data Intelligence?
Data intelligence is the work of making an organization's data reliable enough to act on. It connects systems that don't talk to each other, modernizes the platforms underneath, and establishes the governance that keeps quality from degrading.
It covers data engineering, platform development, integration, analytics, and governance.
The distinction from business intelligence is scope. BI reports on data. Data intelligence makes the data worth reporting on.
The State of Enterprise Data
67%
Data leaders who say they do not completely trust the data used for decision-making.
Precisely, Data Integrity Trends
1 in 4
Organizations losing more than $5 million a year to poor data quality.
IBM Institute for Business Value, 2025
93%
Organizations reporting that interest in AI has increased their focus on data.
Wavestone, 2026 AI & Data Leadership Executive Benchmark Survey
Signs Your Data Foundation Needs Work
- Conflicting Figures Across Business Units
- Reporting Cycles That Take Weeks to Complete
- New Questions That Require Engineering Effort
- Systems Operating in Isolation
- Infrastructure That Cannot Scale with Demand
- Data That Cannot Support AI Initiatives
What Data Intelligence Covers
Our data engineering services offer a systems that collect, transform, and move data between platforms. Automated pipelines with monitoring, so failures surface immediately rather than being discovered in a report three days later.
The environment your data lives in. Warehouse or lakehouse architecture, storage design, and access patterns built around how your teams actually query, not around a reference architecture.
Connecting systems that were never designed to work together. CRM, ERP, support platforms, finance systems and operational tools joined through APIs, event streams or batch pipelines, depending on how fresh the data needs to be.
Moving off environments that can no longer keep up. Migration from legacy warehouses, re-architecture for scale, and decommissioning the systems nobody wants to touch.
Reporting, dashboards and self-service analytics built on a modeled semantic layer, so that when two people ask the same question, they get the same answer.
Ownership, quality rules, lineage, access control and compliance. The layer that stops a clean data environment degrading six months after it is built.
Structuring and preparing data for AI systems, retrieval indexes, vector stores, feature pipelines and the quality thresholds AI depends on to produce reliable output.
How We Approach Data Intelligence
The Foundation for Better Decisions
You finish with a data environment designed to support today’s operations and tomorrow’s intelligence needs.
Connected data ecosystem- Modernized data architecture
- Reliable data pipelines
- Documented data quality
- A semantic layer teams agree on
- AI-ready data environment
Our Technology Landscape
Data Platforms
- Snowflake
- Databricks
- BigQuery
- Microsoft Fabric
Cloud
- AWS
- Microsoft Azure
- Google Cloud
Analytics
- Power BI
- Tableau
- Looker
Engineering
- Python
- SQL
- Spark
- dbt
Common Questions
Business Intelligence focuses on analyzing and presenting information through reports and dashboards. Data Intelligence creates the broader foundation by connecting, managing, improving, and governing data, so organizations can use it effectively across analytics, operations, and AI initiatives.
Yes. We can work with existing data environments and helps organizations integrate, modernize, optimize, or extend their current platforms based on business goals.
We evaluate data quality, accessibility, structure, governance, and infrastructure maturity to identify whether your current environment can support AI initiatives.
Not always. The right approach depends on your current architecture, business requirements, and technical limitations. Modernization can often happen through targeted improvements rather than complete replacement.
The timeline depends on the complexity of your data environment, the number of systems involved, and the outcomes you are targeting. Initial assessments can identify priorities quickly before larger implementation phases begin.
Not Sure Whether Your Data is the Problem?
Tell us what you cannot answer quickly and we will tell you what is causing it.
Talk To a Data Expert