Every marketing org eventually hits the same moment: someone needs "customer lifetime value" for a campaign brief, and three different people point to three different tables, two of which are stale and one of which is right but nobody remembers why. An information map is a practical, maintained inventory that answers, for any piece of customer or campaign data: where does this live, what does it mean, how fresh is it, and who owns it. It's distinct from a business glossary (which defines terms) — the map tells you the physical location and lineage of the data behind each term.

What belongs in the map

A usable information map records, per data asset: the system or table it lives in, its grain (one row per what), its refresh cadence, its upstream source, its owning team, and known caveats (e.g. "excludes guest checkouts," "backfilled only from 2022 onward"). The goal isn't exhaustive technical documentation — it's answering the five questions a marketer or analyst actually asks before trusting a dataset enough to build a campaign on it.

information_map_entry_template.txt

text

Data asset:       Customer Lifetime Value (CLV)
Lives in:          marketing_mart.dim_customer.clv_12mo
Grain:             One row per customer
Refresh cadence:   Daily, 3am UTC batch job
Upstream source:   fact_orders (ecommerce) + fact_subscriptions (billing system)
Owning team:       Marketing Analytics
Known caveats:     Excludes orders refunded within 30 days.
                   Does not include offline/in-store purchases (no POS feed yet).
Last reviewed:     2026-06-01

Mapping at the source-system level first

Before mapping individual fields, map at the system level: which platforms hold customer or campaign data at all (CRM, ESP, ecommerce platform, ad platforms, CDP, support ticketing), which of those feed the warehouse, and which stay siloed. This top-level map surfaces the uncomfortable gaps early — a support ticketing system holding churn-signal data that never makes it into the warehouse is exactly the kind of blind spot an information map is meant to catch before it becomes a campaign targeting mistake.

Information map vs. business glossary vs. data dictionary

Artifact	Answers	Primary audience
Business glossary	What does this term mean?	Everyone, business-side
Information map	Where does this live, how fresh, who owns it?	Analysts, campaign ops, data team
Data dictionary	What is this column's type, format, and valid values?	Engineers, analysts writing queries

Build the map by interviewing the people who already know

The fastest way to build an initial information map isn't a top-down audit of every schema — it's a series of short interviews with whoever currently fields the "where do I find X" questions informally. Every org has a handful of people who already hold this knowledge in their heads; the map's first draft is just externalizing what they already know before they leave or forget.

Keeping it current: the part most maps fail at

An information map built once during a project and never revisited decays within a quarter — new tables get added, old ones get deprecated, ownership changes when people change roles. The map needs a lightweight review cadence (quarterly is common) and, ideally, a "last reviewed" date on every entry so users can judge staleness themselves. Pairing the map with a data catalog tool (Atlan, Alation, or even a well-maintained internal wiki with enforced structure) makes the review cadence sustainable instead of relying on someone remembering to update a spreadsheet.

An unmaintained map is worse than no map

A stale information map actively misleads — someone trusts an entry that hasn't been true for a year and builds a campaign on outdated assumptions, with more false confidence than if no map existed at all and they'd asked a person directly. If no one owns keeping it current, it's better to clearly mark it as deprecated than let it quietly rot while still looking authoritative.

Using the map to onboard new team members and tools

Beyond day-to-day campaign work, the information map pays off hardest during onboarding — a new marketing analyst or a newly-integrated BI tool needs to know where things live, and the map turns that from a multi-week process of tribal-knowledge osmosis into a reference document a new hire can work through in their first week.

What's next

Once you know where the data lives and what it means, the next step is putting it to work — turning a well-mapped dataset into live campaign targeting logic.

Next: Using Information for Campaign Targeting →

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