Point of view
Data & Analytics
Data & Analytics
Build trusted data foundations and turn information into decisions — data engineering, architecture, business intelligence, data science and governance.
Overview
AI and analytics are only as good as the data beneath them. We design and build modern data platforms that are reliable, well-governed and ready for both reporting and AI workloads.
From modernizing a legacy warehouse to standing up self-service analytics or advanced data science, our teams connect business questions to the right data, architecture and tools — and make sure people actually use the results.
Capabilities
What we deliver
Select a capability to see deliverables, technologies and FAQs.
01
Data strategy & architecture
Target-state architectures, platform selection and roadmaps for warehouses, lakehouses and data mesh approaches.
Explore 02Data engineering
Robust batch and streaming pipelines, ingestion frameworks and transformation layers with testing and lineage.
Explore 03Modern data platforms
Implementation and migration on leading cloud data platforms, optimized for performance, security and cost.
Explore 04Business intelligence
Dashboards, semantic layers and self-service reporting that leaders trust and teams adopt.
Explore 05Data science & advanced analytics
Statistical modeling, forecasting, segmentation and experimentation that answer high-value business questions.
Explore 06Data governance & quality
Ownership, cataloging, quality rules and access controls that make data trustworthy and compliant.
ExploreOutcomes
What clients can expect
We measure success by business results, and agree how we will measure them before we start. Open an outcome to see how we deliver it and the measures we track.
A single, trusted view of the metrics that run your business
We define key metrics once in a governed semantic layer and build reporting on top of it. Leaders stop debating whose numbers are right and start discussing what to do about them.
How we measure it
- Conflicting metric definitions retired
- Executive reporting cycle time
- Share of reports built on certified datasets
Data platforms ready for AI and advanced analytics
We design platforms that serve reporting, data science and generative AI from the same governed foundation. New use cases start from reusable data products rather than one-off extracts.
How we measure it
- Time to provision data for a new use case
- Reusable data products in active use
- Share of AI use cases served from governed data
Lower platform and maintenance costs
We retire unused assets, consolidate overlapping tools and build cost management into the platform. Automated testing and monitoring reduce the effort spent firefighting broken pipelines.
How we measure it
- Platform cost per workload or per user
- Pipeline failure and incident rates
- Engineering time spent on maintenance versus new delivery
Faster, self-service access to insight for business teams
Certified datasets, intuitive dashboards and training let business teams answer more of their own questions. Analysts spend less time on ad hoc extracts and more on analysis.
How we measure it
- Active users of self-service reporting
- Volume of ad hoc data requests
- Time from business question to answer
Clear accountability for data quality and access
Named owners, published quality scorecards and defined access policies make data issues visible and fixable. Audit and compliance requests become routine rather than disruptive.
How we measure it
- Data quality scores for critical data elements
- Time to resolve data issues
- Access review completion and audit findings
Approach
How we deliver
Select a step to see the activities, deliverables and typical timing.
Step 1 · 3–5 weeks
Assess
Map current data assets, flows, pain points and the decisions that matter most.
We interview business and technical stakeholders, review existing platforms and pipelines, and trace the data behind priority reports and use cases. The aim is a clear, shared picture of what works, what does not and where improvement will pay off.
Activities
- Stakeholder interviews and decision mapping
- Inventory of data sources, pipelines and reports
- Data quality and architecture review
- Cost and usage analysis of current platforms
Deliverables
- Current-state assessment
- Prioritized pain points and opportunities
- Initial business case
Step 2 · 3–6 weeks
Design
Define a pragmatic target architecture and a delivery roadmap sequenced by business value.
We design the target architecture, standards and operating model, and validate key choices with proofs of concept where needed. The roadmap is sequenced so that early increments deliver visible value.
Activities
- Target architecture and standards definition
- Platform evaluation or proof of concept
- Governance and operating model design
- Roadmap and investment planning
Deliverables
- Target-state architecture
- Value-sequenced roadmap
- Data operating model
Step 3 · Typically 8–24 weeks, in two-week iterations
Build
Deliver in increments: pipelines, models and dashboards that ship early and improve continuously.
We build in short iterations, each delivering working pipelines, data products or dashboards to real users. Automated testing, CI/CD and monitoring are in place from the first increment.
Activities
- Platform foundation and security setup
- Pipeline and data model development
- Dashboard and data product delivery
- Testing, reconciliation and performance tuning
Deliverables
- Production platform increments
- Tested pipelines and data products
- Dashboards and semantic models
Step 4 · 2–6 weeks, alongside the final build increments
Enable
Embed governance, documentation and training so your teams own and extend the platform.
We transfer ownership deliberately through pairing, documentation and training tailored to engineers, analysts and business users. Governance processes are handed to named owners along with the tools to run them.
Activities
- Pairing and code walkthroughs with your engineers
- Training for analysts and business users
- Governance handover to data owners
- Adoption tracking and support
Deliverables
- Platform documentation and runbooks
- Trained internal teams
- Governance processes in operation
FAQs
Questions we often hear
Can’t find what you’re looking for? Browse all FAQs or ask our team.
Should we modernize our data platform before investing in AI?
Not necessarily all of it. We identify the minimum data foundation each priority use case needs and build that first, so AI value is not held hostage by a multi-year program. Each increment is designed to be reused by the next use case.
Which data platforms do you work with?
We commonly work with Snowflake, Databricks, Microsoft Fabric, Google BigQuery and Amazon Redshift, along with open-source tools such as dbt, Airflow, Spark and Kafka. We recommend options based on your workloads, skills and existing investments rather than a preferred vendor.
How long does a data platform project take?
An assessment and target architecture typically takes six to ten weeks. A first production increment, such as a priority data domain with pipelines and dashboards, can often follow within about three months, with the wider platform built out in value-driven waves.
How do you price data and analytics work?
Assessments and strategy work are usually fixed fee. Build phases are priced as fixed-scope increments, time and materials or a dedicated delivery team on a monthly basis, depending on how well defined the scope is and how much flexibility you need.
How do you handle sensitive and regulated data?
We work within your environment and security policies, use least-privilege access and avoid copying production data where it is not needed. Platforms are designed with classification, masking, row-level security and audit logging to support requirements such as GDPR and HIPAA.
Can you work with our existing data team?
Yes. We often work as a blended team with your engineers and analysts, agree shared standards early and pair on delivery so knowledge stays with you. We can also take on a defined workstream while your team focuses elsewhere.
What if our data quality is poor?
That is common, and it is not a reason to wait. We profile the data behind priority use cases, fix the issues that matter most at the source where possible and put monitoring in place so quality improves over time instead of slipping back.
How do we get started?
Most clients begin with a short assessment focused on one or two priority business questions or use cases. That gives you a clear view of the gaps, a target architecture and a costed plan before committing to a larger program.
Insights
Related thinking
Explore our other services
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