Companies need to rethink two core assumptions about how they work with data: ETL (Extract, Transform, Load) and how data is evaluated in context.
Rethinking ETL:
Traditional ETL reflects an older model of data thinking: centralize everything before you can ask questions. In practice, this approach is expensive, slow to maintain, and often stale by the time it’s queried. It also introduces unnecessary complexity when the objective is to answer specific questions rather than build a comprehensive warehouse.
With modern AI systems like Adaly, data can remain in its source systems. Queries are resolved at runtime, ambiguities are handled dynamically, and analysis is performed against the most current data available, directly from upstream systems (without the overhead of constant extraction and transformation).
Evaluating Data in Context:
Historically, data has been analyzed system by system. While this simplifies ownership, it creates siloed views that limit understanding of how the business actually operates. Insights derived from a single system rarely tell the full story.
Adaly evaluates multiple mission-critical data sources together, in context. Sales performance alongside marketing spend. Demand forecasts alongside warehouse inventory. By correlating signals across systems, Adaly produces insights that are more accurate, explainable, and operationally useful.
The Mindset Shift:
Shift from treating data as a static asset to treating it as a runtime capability.
Traditional Model:
Users have to hunt down datasets and depend on analysts or platform teams to extract insights.
Decisions rely on delayed snapshots from disconnected systems.
Meaningful analysis requires specialized technical skills.
The Adaly Model:
A conversational layer embedded in workflows that abstracts underlying system boundaries.
Real-time reasoning delivers context, explanations, and forward-looking signals.
Plain-English queries are converted into live analysis, enabling fast, confident decisions.
Why It Matters:
Reduces operational overhead by limiting ETL, redundant storage, and batch-driven workflows.
Improves correctness through runtime access to authoritative systems of record.
Scales decision-making via governed, repeatable analysis without specialist bottlenecks.
Accelerates execution by embedding data directly into operational workflows.
