Learn
Explore guides to the data and AI work behind business decisions. Find practical explanations of data foundations, health and research, finance and HR, analytics and AI, with examples and tool guides to help you evaluate the work your team needs.
Analytics and AI
What is agentic analytics, and when is it useful?
Learn how to choose an investigation, test its evidence and put the result into everyday work. The guide explains agentic analytics through a daily order review.
Data foundations
Evaluate cloud data warehouses by workload, delivery needs and the path to useful answers.
Connect field definitions to business terms and maintain meanings that reports and AI can use.
Learn how source connections, historical records and maintained models support daily business use.
Build governance around real decisions, shared definitions, enforced access and daily operation.
Explore governance tools for catalog work, lineage, access and usable business data.
Connect finance, people and operations through shared records, historical relationships and definitions.
Choose a lake, warehouse or lakehouse around the reports and investigations people need.
Explore tools for source movement, shared records, governed questions and ongoing delivery.
Plan reporting dependencies, acceptance checks and a controlled cutover for data migration.
Learn how ETL pipelines handle source changes, dependencies, recovery and incomplete loads.
Connect platform resource use to the reports and workflows people actually need.
Explore testing, monitoring and investigation tools to keep business reports dependable.
Understand the chief data officer role and how delivery and AI responsibilities fit together.
Compare cheaper places to run PySpark and Spark SQL jobs without rewriting them.
Test a business workload and the work needed to keep reports and pipelines running.
Learn how ETL and ELT prepare reporting data and handle changes and retries.
Explore ETL tools by source behavior, transformation needs and ownership of the finished workflow.
Follow bronze, silver and gold layers from source records to an explainable business measure.
Connect sources and shared definitions to reliable reports, adoption and support for smaller teams.
Resolve conflicting reports through shared definitions, source records and consistent reporting cutoffs.
Health and research
Use AI on Epic data for operational and research questions, with shared measures and query review.
Understand when healthcare needs a warehouse and how Epic and other sources fit.
Match an Epic question to its data, reporting needs and the people using it.
How health systems land Clarity and Caboodle in a warehouse and keep it working after upgrades.
Understand when Cosmos fits a research question and when local institutional data is needed.
Build repeatable Epic cohorts with appropriate extracts, reviewed flowsheet mappings and source history.
Explore platforms for population programs and reporting across Epic, finance and workforce systems.
Examine HIE, HL7 and FHIR records, patient matching and source coverage for Epic analytics.
Learn how OMOP mappings preserve clinical meaning and what to check before releasing research data.
Give investigators cohort tools and a governed research copy with access controls an IRB can review.
Finance and HR
Build board reports with consistent measures, clear commentary and a repeatable release process.
Evaluate financial reporting software through source history, shared calculations, review and the recurring close.
Agree four definitions and one as-of rule, then close without hand-reconciled exports.
Build workforce reports with shared definitions, effective dates, appropriate access and review.
Align budget and actuals, explain the variance and identify an owner and next action.
Learn why historical reports show current values and how effective dates and corrections affect them.
Join Epic professional billing to Workday positions by effective date and reconcile one quarter.
Analytics and AI
Choose an investigation, test its evidence and put the result into everyday work.
Explore AI analysis tools by the work people need and how to evaluate business answers.
Explore BI tools by reporting fit, data preparation, migration tests and ongoing ownership.
Understand BI through shared measures, reporting, delivery ownership and the decisions it supports.
Prepare business data for AI through defined tasks, shared meaning, evidence and access controls.
Which AI products a BAA covers, what it leaves out and how hospitals deploy AI on PHI.
Examine private LLM deployment through source access, model endpoints, retained records and answer evaluation.
Connect business definitions to calculations and keep measures consistent across reports and AI tools.
Connect student records to explainable signals, advising support and measures of what follows outreach.
Discuss an opportunity in your work
We can talk about how your team investigates a change and what happens after it finds an explanation. A demo is a chance to discuss where an agent could help and how people would check the work it does.