92526 Chap.3 The Data Lifecycle from Origin to Reflection
The Data Lifecycle from Origin to Reflection
The data lifecycle is the Week 1 framework for getting data that can genuinely influence a decision. It follows one data source through nine stages: origin, identification, capture, transfer, storage, processing, analysis, dissemination and reflection.
Each stage comes with its own questions about requirements, risks and constraints such as cost, time and access, and reflection closes the loop by asking what went wrong, what went well and what was missed.
The framework matters twice in this subject.
It is core Week 1 content that later weeks build on, and Assessment 1 asks you to apply it to one sporting data source, with a rubric criterion worth 5 points for addressing all stages with metric-specific detail. The chapter works a full model lifecycle for GPS training-load data and shows which gaps keep a plan out of the top band.
What this chapter covers
- 01
Origin and data identification
- 02
Capture, transfer and storage
- 03
Processing and creating new variables
- 04
Analysis and dissemination
- 05
Reflection as the step that closes the loop
- 06
Requirements, risks and constraints at each stage
- 07
Applying the lifecycle in Assessment 1
Worked example · free
Choose the lifecycle stage for five planning questions
- 1(a) Lifetime and disposal of data are storage questions.
- 1(b) Who has access to findings, and whether release is ethical, belong to dissemination.
- 1(c) Turning raw items into a new variable is processing.
- 1(d) Looking back at what to do differently is reflection, which feeds the next loop.
- 1(e) Having a purpose for collection is the first question of data identification.
Key terms
- Data identification
- The lifecycle stage that settles the purpose of collection and the specific items the analysis needs.
- Data transfer
- Moving data from its source to where it will be analysed, with attention to volume, security, cost and control.
- Dissemination
- Deciding how results are communicated, who may access them and whether release raises ethical issues.
- Reflection stage
- The final lifecycle step, which reviews what went well, what went wrong and what was missed at every stage.
The Data Lifecycle from Origin to Reflection FAQ
What are the stages of the data lifecycle?
The Week 1 interactive lists nine: origin, data identification, capture, transfer, storage, processing, analysis, dissemination and reflection. Each comes with a short set of questions, such as whether data is accessible at origin, whether a transfer channel is secure, or who should have access to the findings.
Why does the lifecycle end with reflection?
Reflection asks what went wrong and what went well at each step, what could have been done differently, and whether any source, capture method, storage option or dissemination approach was missed. Its answers change how the next round of data is collected, which turns the lifecycle into a loop rather than a straight line.
How is the lifecycle used in Assessment 1?
Assessment 1 asks you to choose one sporting data source, such as training load, strength testing, wellness questionnaires, fan or ticket data, and work through the requirements, risks and limits it meets at every stage. The rubric's top band, worth 5 points, needs all stages addressed with metric-specific detail throughout.
Which lifecycle stage is hardest to manage in sport?
There is no single right answer, and the Week 1 activity asks you to argue for one. It depends on the data: video creates storage and transfer burdens, wellness surveys make capture fragile, medical data makes dissemination sensitive, and third-party platforms can make origin hard to access. A strong answer names a concrete failure and a control.
What is the difference between processing and analysis?
Processing prepares raw data for the goal: checking it fits the analysis and creating new variables, while watching for new risks those variables introduce. Analysis then decides how and why the data is analysed, who does it and whether the methods carry risks. Deriving weekly load totals is processing; comparing players against plans is analysis.
Exam move
Learn the nine stages in order with one question from each, then practise on a data source you could use in Assessment 1. Write a line per stage covering the requirement, the main risk and one constraint, and check that no stage is empty, because the rubric's top band asks for all stages with metric-specific detail.
Swap plans with a classmate and try to find the weakest stage in each other's work, paying particular attention to transfer, dissemination and reflection, which are easy to skim. Finally, rehearse classifying short planning questions into stages quickly, so the framework is available without notes.
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