Industries
Analysis, forecasting and search over company data.
How to choose · AI for data and analytics
Look past demonstrations to three questions: can it reach the right data with the right permissions, can you verify how an answer was produced, and does it understand your business definitions. Evaluate connectors, semantic layer support, query transparency, access control inheritance, data movement and retention, and accuracy on your own hard questions.
Comparison · AI for data and analytics
Established statistical methods are transparent, cheap and often strong when history is stable and data is limited. Machine learning helps when there are many related series, rich drivers and enough clean history. General-purpose chat tools are not forecasting engines. Compare methods on your own backtests, forecast interpretability, maintenance effort and how decisions will use the results.
Problems & fixes · AI for data and analytics
Wrong answers usually trace to ambiguous definitions, messy or undocumented tables, wrong joins and filters, stale data, permission gaps, or questions the data cannot answer. Prevent them with a governed semantic layer, documented data, visible queries, curated question sets for testing, and a culture that checks surprising numbers before they spread.
Requirements · AI for data and analytics
Readiness means data that is findable, documented, access-controlled, reasonably clean and owned. Before adopting AI tools, inventory sources, classify sensitivity, fix permissions, define key metrics, document tables, set quality checks and agree ownership. Skipping these steps turns every AI feature into a faster way to surface existing mess.