We build Data Lake/Lakehouse architecture and automate data pipelines; Power BI and Tableau open self-service reporting at enterprise scale.
The critical topics this service addresses and the outcome we deliver in each.
We aim to shorten report preparation time meaningfully, tracked against a baseline and target.
Within the contracted scope we deliver an installed and configured data platform, pipelines and a dashboard library.
We run pipelines as a traceable evidence record with lineage tracking and error alerts.
The team's ability to run the platform independently after knowledge transfer and an operations guide depends on owner validation.
How we phase the service across delivery, governance, and connected service pillars.
We design the architecture with a Medallion (Bronze/Silver/Gold) and optionally Data Mesh approach, and clarify platform selection during discovery based on data volume, growth rate, budget and regulatory requirements.
We orchestrate pipelines with Azure Data Factory, Airflow and dbt, and secure batch and near real-time flows with row-level security, column masking and audit trails.
We build dashboard design with a UX focus and institutionalize data literacy through training at admin, developer and business-user levels.
Illustrative surfaces where this service is commonly activated.
We stand up the new platform while existing reports keep running, clarifying migration scope and acceptance criteria during discovery.
We build live dashboards through Kafka or Event Hub streaming pipelines via Power BI DirectQuery.
We pull data from systems such as SAP, Oracle, Dynamics 365 and Salesforce into a single platform with ready connectors.
This service's depth on sector-specific technical and compliance topics.
With a Bronze raw layer, a Silver cleansed layer and a Gold business-focused layer we progressively turn data into a value-producing structure.
We combine the flexibility of a Data Lake with the structure of a warehouse, gathering schema-on-read and schema-on-write advantages in a single architecture.
We align with regulatory requirements through a hybrid architecture that runs sensitive data on-premise and analytics workloads in the cloud.
Legacy data warehouses built on monolithic architectures can no longer keep pace with the volume, velocity, and variety of modern enterprise data — resulting in stale dashboards, brittle ETL pipelines, and analyst bottlenecks. Our Modern Data Platforms & BI practice designs and deploys cloud-native data warehouses, lakehouse architectures, and governed BI layers that deliver trusted, real-time insights at enterprise scale. We eliminate data silos and enable self-service analytics across business units without compromising performance or security.
Turn the outcome into a measurable target with baseline, owner, and evidence review cadence
Make cost and resource optimization measurable against the agreed baseline and review cadence
Improve quality indicators through baselines, acceptance criteria, and reviewed evidence
Our engagement spans the complete data platform lifecycle from architecture design and source system integration through BI layer build, user training, and production handover. We apply a medallion architecture pattern (Bronze/Silver/Gold layers) to ensure data quality and reusability across all consumer use cases. Managed operations and SLA-backed platform monitoring are available post-delivery.
Turn the outcome into a measurable target with baseline, owner, and evidence review cadence
Shorten operational cycle time against agreed measurement targets and acceptance criteria
Deliverables include a production-ready data platform with full documentation, runbooks, and training materials handed over to your team. We provide architecture decision records (ADRs) for every key technology choice, enabling your team to maintain and evolve the platform with confidence. A 90-day hypercare period ensures stability and knowledge transfer after go-live.
Turn the outcome into a measurable target with baseline, owner, and review cadence
Track growth and reputation outcomes with governed attribution and agreed measurement targets
Deliver measurable ROI documentation with before/after performance and cost benchmarks
A traditional data warehouse stores structured, processed data optimized for SQL analytics. A lakehouse combines the flexibility of a data lake (storing raw, unstructured, and semi-structured data) with ACID transaction controls and performance optimizations of a warehouse — enabling both BI reporting and machine learning on the same platform.
Yes. We use a phased migration approach: historical data is migrated in parallel with live production, using validation checksums and reconciliation reports at each stage. Business users continue accessing the legacy system until the new platform passes user acceptance testing and a cutover plan is agreed.
We implement a streaming layer alongside the batch processing pipeline using Apache Kafka or Azure Event Hubs. Operational dashboard freshness, batch economics, and acceptance targets are measured against the agreed architecture scope.
Yes. We architect multi-tenant data platforms with workspace-level isolation in Power BI Premium or Databricks, combined with attribute-based access control (ABAC) policies ensuring each business unit sees only its authorized data while shared infrastructure reduces total cost of ownership.
We offer tiered managed service options: Standard (business-hours monitoring, 4-hour response SLA), Advanced (contract-scoped continuous monitoring, response SLA, monthly optimization reviews), and Enterprise (dedicated platform engineer, proactive capacity planning, quarterly architecture reviews).
We instrument FinOps dashboards from day one, using cloud-native cost allocation tags, budget alerts, and automated scale-down policies for non-production environments. Monthly cost optimization reports identify unused resources and right-sizing opportunities, surfacing measured cloud-spend reduction opportunities during the first year.
This short form routes your request to the right support team. We clarify context first, then define the safe sharing method.
Privacy-aware first contact; safe sharing flow when needed; no sales pressure.
