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Measure · Tool guide 02 of 40

Collect Data

Data collection is the controlled recording of observations needed to answer the project’s measurement questions. It includes capturing context, following operational definitions and protecting the original record. Collection can use existing records or new observations.

Expected output: A traceable dataset collected under agreed rules, with deviations and missingness recorded.

01 · Purpose

Why and when to use it

Use it after the definitions and plan are ready. Consistent collection creates a defensible baseline and preserves the context needed for later analysis.

Who takes part?

The measurement owner works with people who understand the process and the recorded data. Statistical or measurement-system decisions should involve someone able to check the assumptions and study design.

02 · Application

How to apply it

Start with an agreed measurement purpose, explicit units and trustworthy observations. Identify missing cases and assumptions before calculating.

  1. Check the purpose, eligible cases and collection instructions.
  2. Trial the method on a few cases and resolve unclear classifications.
  3. Collect according to the planned sampling and time coverage.
  4. Record identifiers, timestamps and relevant context consistently.
  5. Log missing observations and deviations; retain raw data and a cleaning record.

03 · Example

A worked measurement example

Original fictional teaching example. Small datasets illustrate the method; they are not proposed as sufficient study sizes. Supplied parameters are assumptions, not estimates from a real process.

OrderPromised dateHandover dateRecorded result
A0110 June10 JuneOn time
A0210 June11 JuneLate
A0310 JuneMissingUnknown; investigate, do not classify as on time

04 · Interpretation

What the result tells you

Unknown cases remain visible. Excluding them without reporting their number could make the process appear better than it is.

Completion check: A traceable dataset collected under agreed rules, with deviations and missingness recorded.

05 · Judgement

Mistakes and limitations

Do not invent missing observations, retrospectively change rules without a record, or select only convenient cases. Staff may alter behaviour when observed; record collection conditions.

07 · Reading

Sources and further reading

The tool list follows the accepted LeanTrainingLab overview and its reference to Six Sigma+Lean Toolset, second edition (Lunau, editor; Springer, 2013). The book chapters were not available for direct review during this build. Explanations, examples and worksheets are original teaching material. Further reading supplies professional background; local conventions are stated explicitly.