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Merchandising, pricing, inventory, forecasting, and store systems continuously feed one another. DataSmart traces a questionable number back to its source and shows which downstream plans and decisions are affected before bad data travels further.
High-frequency data moves quickly from transactions into forecasting, inventory, and replenishment. DataSmart compares pipeline runs and identifies where a number changed, helping teams catch errors before stores and supply chains act on them.
Sales, retailer, syndicated, distribution, and supply-chain data arrive from different sources on different schedules. DataSmart maps how those sources combine, identifies gaps and conflicting definitions, and works with your existing data catalog to maintain one traceable path.
In our clients’ own words
“This partnership allows us to eliminate repetitive, low-value tasks so our merchandisers can focus on understanding what is truly happening in our brand and make quicker, higher-quality decisions to get the right products in the right places for our customers.”
“We landed on Impact Analytics for 3 core reasons: culture, technology, and an intuitive interface that was easy to adopt.”
“Our planners immediately recognized that Impact Analytics understood the realities of retail planning at scale. The opportunity to move from spreadsheet-driven, line-by-line planning to an exceptions-management model is a significant step forward.”
Data lineage shows where data came from, how it changed, where it moved, and which systems depend on it. DataSmart builds and maintains that trail automatically across your data environment.
DataSmart traces the number backward through its downstream tables, transformations, pipelines, and source data to identify where it changed. Iris can then explain the issue in plain language and show which downstream systems depend on the affected data.
Yes. DataSmart continuously maps pipeline dependencies and transformations as your data environment changes, keeping lineage current without teams manually documenting every update.
AI Agents are only as trustworthy as the data behind their decisions. DataSmart provides a traceable record of where that data came from, how it was transformed, and whether it passed the quality and governance controls around it, giving teams evidence behind the decisions AI Agents make.
No. DataSmart can work alongside your existing data catalog, adding continuously updated lineage, transformation-level visibility, run-level traceability, and root-cause analysis across the pipelines behind it.