FDE Foundations · Module 2: Customer Discovery

Mapping the Current Process

A process map turns operator stories into a diagram you can point at: systems, handoffs, failure points, and the exact spots where data quality dies.

10 min reading

Objectives

  • Draw a process map with systems, handoffs, and failure points
  • Build a system inventory with owners and access paths
  • Identify where data quality dies in the current flow

The map

Draw the flow as it is, not as documented: trigger, steps, systems, human handoffs, outputs. Mark every handoff with a question: what does the receiver need that the sender does not provide? Handoffs are where errors hide.

The inventory

Alongside the flow, keep a table of systems: name, owner, access path (API, file export, database read replica), data sensitivity, and change process. This table becomes your integration plan and your security review input. An engagement stalls when nobody knows who owns the third system in the chain; find that out in week one.

Where quality dies

Mark the points where data degrades: free-text fields, manual re-entry, spreadsheet round-trips, timezone-naive timestamps. These marks predict your validation workload. A pipeline is only as clean as its dirtiest source, and you want to know the dirtiest source before you design around it.

Share it back

Walk the map with the operator. Corrections to the map are free in discovery and expensive in week four.

The map is also a political tool: it shows four teams where the gaps are, without blaming anyone.

Quick check

An optional 2-3 question self-check. Answers never leave your device, are not stored, and never count toward any assessment.

Exercise

Map a real process you know end to end. Include at least three systems, two handoffs, and two failure points, plus a system inventory table with owners and access paths. Use a fictional company name if needed.

Pass criteria

Diagram or structured text includes systems, handoffs with failure marks, inventory with owners and access paths, and at least one data-quality risk called out.

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