Services
Data & Analytics
Data platforms and analytics that support real decisions.
We build data pipelines, warehouses, and analytics platforms that turn enterprise, logistics, and operational data into something decision-makers can actually use.
Overview
Most organizations do not have a data shortage; they have a trust shortage — numbers that disagree between systems, dashboards no one believes, and analysts spending their week reconciling instead of analyzing. Our practice builds the unglamorous foundations that make data dependable, then the analytics and data products that make it consequential. Credible data first, clever analysis second.
What We Offer
Services within this practice.
Focused offerings that can stand alone or combine into an end-to-end program.
Data platform engineering
Design and build of modern data platforms on Snowflake, Databricks, and cloud-native services — ingestion, modeling, orchestration, and cost governance. Architected for the workloads you have and the ones you can name.
Analytics and BI delivery
Reporting and self-service analytics in Power BI, Tableau, and Looker, built on governed semantic models so two dashboards cannot disagree about the same number.
SAP data and analytics
Extraction, modeling, and reporting on SAP data — a specialty in its own right — connecting ERP and Vistex data to modern platforms without losing the business meaning encoded in the source.
Master data management
Governance and tooling for the customer, vendor, product, and pricing data that every downstream system depends on. Designed with clear ownership, because master data without an owner decays on contact with the business.
Streaming and event data
Real-time data architectures using Kafka and cloud event services for operational use cases — shipment tracking, inventory positions, transaction monitoring — where yesterday's batch is too late.
Data quality and observability
Automated testing, lineage, and monitoring across pipelines so data breaks are caught by engineers before executives. Trust in data is built through visible reliability, and we engineer for it.
Outcomes
What good looks like.
- A single set of numbers that finance, operations, and leadership all accept as the record
- Analysts spending their time on questions rather than reconciliation
- Data infrastructure with known costs, known owners, and known freshness for every critical dataset
- A foundation clean and governed enough that AI initiatives can build on it instead of stalling against it
Our Approach
How we deliver this work.
- 1
Consolidate
We bring fragmented data sources into a coherent, governed platform.
- 2
Model
Data is structured around the decisions it needs to support, not just the systems it came from.
- 3
Deliver
Analytics and reporting are built for the people who use them, not just the people who build them.
- 4
Govern
Data quality and governance are maintained as an ongoing discipline, not a one-time project.
FAQ
Common questions.
It depends on your workload mix, team skills, and existing cloud commitments, and we make the call with you rather than for the vendor. Warehouse-centric analytics, heavy data engineering and ML, and streaming-first operations each pull toward different centers of gravity. We work across the major platforms and design so the decision is revisable, because your needs in three years will not match today's.
Let's talk about data & analytics.
Tell us about the challenge you're solving for — we'll follow up to understand the fit before proposing anything.