Understanding the Data Life Cycle#

🌱 Foundations 🔄 The Analysis Process & Data Life Cycle Lesson 009

Important

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The data’s own biography#

The previous lesson separated the analyst’s process from the data life cycle — the journey the data itself travels. This lesson walks that journey stage by stage, because an analyst who knows where data comes from and where it is going works faster and trusts the right things.

The six stages, in depth#

Plan. The stage most people never see, and the one that decides everything after. Before collection, someone chooses what data is needed, how it will be managed, who is responsible, and under what privacy and retention rules. A well-planned dataset arrives with definitions and owners; a badly planned one arrives as a mystery.
Capture. The data comes into existence or into the organisation: recorded by transactions and sensors, typed into forms, imported from external providers or public sources. Capture choices — what fields, what granularity, what validation at entry — set the ceiling on later quality.
Manage. The custodial stage: storing the data, securing it, organising it so it can be found, backing it up, and controlling who may access it. Most of the ecosystem lesson’s storage layer lives here.
Analyze. The data is put to work answering questions — the stage where this whole course happens.
Archive. Data no longer in active use moves to long-term storage: out of the way, cheaper to keep, but retrievable when an audit or a historical comparison needs it.
Destroy. The deliberate end. When retention schedules or privacy obligations require it, data is securely deleted — a governed act, not housekeeping neglect.

Variation is normal#

Companies and industries carve the cycle differently — a hospital inserts compliance reviews, a bank extends retention for regulators, a startup may barely formalise it at all. The stage names matter less than the underlying questions each stage answers: is this data planned, captured, kept, used, parked, or gone?

Why the analyst should walk the cycle#

Each stage upstream of Analyze is a place your data could have been shaped or damaged: a Plan that never defined “active customer”, a Capture form that made the field optional, a Manage migration that truncated text. When a number looks wrong, the life cycle is your checklist of where to look. And downstream, Archive and Destroy explain the gaps: the missing 2019 data may not be lost — it may have been destroyed on schedule, which is an answer, not a dead end.

Hint