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Overview

The Data area is where operational data enters ProDex, gets understood, and becomes durable knowledge your team revisits. The Data page has four tabs — My Data (the raw inputs), Reports (downloadable deliverables), Insights (dashboards), and Pipelines (the auditable record of how numbers were derived) — and one workflow that ties them together: Exploratory Analysis, run in chat with Dexter, which builds trust in a source and populates the dashboards and pipelines. This page maps those surfaces in the order you touch them, and untangles a few terms that sound like Data concepts but live elsewhere in the product.

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The Surfaces at a Glance

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What Each Surface Is

Data source. A file you uploaded (CSV, Excel, PDF, CAD, and more) or a connected data integration that pulls data on a recurring basis. Sources are the raw material; ProDex does not modify them. See Data Sources. Exploratory Analysis. A structured workflow, run as a conversation with Dexter, that profiles a source, surfaces gaps and ambiguities, resolves them with you, and produces a first dashboard plus the pipeline that documents how it was built. See Exploratory Analysis. Pipeline. A DAG-shaped record of a derivation: source nodes (bound to an upload, an integration, or any upstream artifact — a model, a BOM, another dashboard, a lookup table), transformation nodes (each carrying the SQL or Python that ran), and output nodes (each bound to the artifact it produced: a dashboard, a simulation model parameter, a BOM quantity, a report, and more). See Pipelines.
A pipeline is provenance, not verification. It shows how a value was derived, not whether it matches reality. Trust in the numbers is built during Exploratory Analysis, where Dexter resolves gaps and ambiguities with you before extracting anything.
Data Insight (dashboard). A factory-scoped container for related charts, the durable visibility surface for numbers the team revisits between projects. Distinct from run-scoped or experiment-scoped charts, which live with their run or experiment. See Insights. Insight (chart). A single chart inside a dashboard. The builder covers the usual primitives — bar, line, area, pie, table, scatter, box plot, heatmap, histogram, and gantt — plus higher-level composites like distribution, breakdown, breakdown over time, trend, pareto, and schedule that encode common data-prep patterns. Charts are always children of a dashboard; a chart cannot exist without one.

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Terms That Live Elsewhere

Three terms often assumed to be Data concepts belong to other parts of the product:

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How They Relate

  • A pipeline reads one or more data sources and produces one or more outputs. Each output is bound to a specific downstream artifact: a dashboard chart, a simulation model parameter, a BOM value, a report, a constant, or a lookup table.
  • A Data Insight dashboard contains insights (charts) as children. The dashboard exists first; charts are added to it.
  • The EDA workflow doesn’t contain anything. It’s the guided process that produces a dashboard and its backing pipeline.
  • A pipeline is keyed by slug and overwritten in place when the same derivation is re-done against a fresh upload or a corrected interpretation. A genuinely different derivation gets a new slug.

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A Typical End-to-End Journey

  1. Upload. You drop a file on the Data page, say a quarterly MES cycle-time export. It appears under My Data.
  2. Start EDA. From the source, you ask Dexter to explore it. Dexter profiles the file, asks targeted questions about codes, gaps, and units, and confirms understanding before extracting anything.
  3. First dashboard. Dexter creates a Data Insight dashboard (e.g. “Cycle times”) and populates it with charts: station-level medians, distributions, outliers.
  4. Pipeline lands. Once the numbers are settled, Dexter documents the cleaning, joins, and aggregations as a pipeline whose outputs bind to the dashboard. From any chart, you can now click into the pipeline and see the file, the filters, and the code behind the number.
  5. Reuse downstream. The same pipeline can add outputs bound to a simulation model (fitted service-time distributions), a BOM (yield rates), or a report. Each output traces back to the same source.
  6. Revisit. Next quarter, a fresh upload replaces the old one; Dexter re-runs the derivation and saves the pipeline against the same slug. The dashboard updates and the audit trail stays intact.
Start with Exploratory Analysis for any source you don’t fully understand. It front-loads the questions that would otherwise surface as wrong numbers later, and every answer gets captured in the pipeline for the next person.

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Where to Go Next

  • Data Sources: uploads, integrations, and how sources are documented
  • Exploratory Analysis: the workflow that turns a raw file into trusted numbers
  • Pipelines: reading and maintaining the derivation record
  • Insights: creating and refreshing dashboards
  • Reports: downloadable deliverables built from your data