Business Intelligence
Turns modeled data into the reports dashboards and self-service analytics that people actually use to make decisions. The outcome is the business seeing what is happening why and what to do about it without waiting on a data team ticket for every question.
Everything included under this practice line.
BI platform selection and rollout aligned to how different user groups actually consume information
Semantic layer and metric store definition so the same KPI reads identically in every dashboard and export
Executive scorecards operational dashboards and self-service workspaces designed for their distinct audiences
Embedded analytics inside internal tools and customer-facing products where analysis needs to live in the workflow
Data storytelling and dashboard design applying visualization principles rather than defaulting to every widget the tool ships with
User training adoption support and center of excellence models to sustain self-service without chaos
Governance model covering certified content sandbox exploration and retirement of stale reports
Alerting and anomaly detection surfaced through the BI layer so users see change without hunting for it
The stack we reach for.
What the business gets, measured.
- Faster and better decisions because leaders trust the numbers in front of them
- Fewer one-off analyst requests as self-service handles common questions
- Consistent KPI reporting across departments and board packs
- Higher tool adoption and lower per-user license waste
- Earlier detection of operational or commercial issues through proactive alerting
The specialists behind this practice line.
BI consultants and analytics engineers pair with the business owners of each report set translating actual decisions into the visualizations that support them. UX practitioners are brought in for executive and customer-facing surfaces where clarity carries real weight.
Compose several capabilities into one engagement.
Data Engineering
We build the pipelines schemas and orchestration that move data from source systems into something a query engine can actually use. Airflow or Dagster dbt for transforms and tests that fail loudly.
Data Warehousing
Snowflake BigQuery or Redshift stood up with sensible cost controls and a modeled semantic layer. We separate storage from compute size warehouses per workload and keep query costs off the CFO's radar.
ETL/ELT Pipelines
Ingestion pipelines that pull from APIs databases and files then land clean data in the warehouse. We prefer ELT with dbt where the warehouse can handle it and stream CDC where batch windows hurt.
Data Lake Solutions
Lakehouse setups on S3 ADLS or GCS using Iceberg Delta or Hudi. Partition layouts and compaction jobs tuned so Trino Spark and Athena all read the same tables without stepping on each other.
Power BI & Tableau Dashboards
Dashboards built in Power BI or Tableau that load fast and don't fall over when someone adds a filter. DAX and LOD expressions written by people who've debugged them at 2am.
Real-Time Analytics
Streaming pipelines on Kafka or Kinesis feeding ClickHouse Pinot or Druid for sub-second queries. Useful when a nightly batch is too slow and ops needs to see what happened five seconds ago.
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