Artifact sources

Artifact sources let you bring outside data into Admire — from tools like your CRM, support desk, or any system that produces records. Once your data is in, you can query it and surface it in dashboards and role metrics, connecting what people do to the outcomes it produces.

Why bring data here rather than into a BI tool or a spreadsheet? Artifact sources sit right next to your people and skills and are built to be simple: you get an easy way to combine data from several systems without standing up a complex BI stack, and Admire’s permissions are preserved, so each person sees only what they’re allowed to. The payoff is that performance data lands in context — where it drives coaching and lets individuals monitor their own results.

Use cases

• Let individuals self-monitor — ingest the data tied to a role (a rep’s deals, a support agent’s tickets) so each person can see their own performance on their profile and power role metrics that update continuously.
• Combine data without a BI project — pull records from several systems into one place and join or aggregate them with derived sources and queries — no separate warehouse or BI tool to stand up and maintain.
• Permission-aware reporting — because access follows Admire’s existing permissions, you can share dashboards and metrics broadly, knowing each person only ever sees the data they should.

What a source is

A source is a definition of a kind of data you want to track. Each source has columns with types (text, number, date, true/false, and so on). Admire works schema-on-read: it can infer column types as data arrives and widen a column’s type automatically if new data needs it, so you don’t have to get the schema perfect up front.

Arbitrary JSON rows

Under the hood, each record is stored as arbitrary JSON — you can ingest rows of almost any shape. A column simply routes a JSON path to the value it should show. That means a column can pull from a nested field, not just a top-level one.

For example, given a record like:

{ "number": 42, "state": "open", "author": { "login": "alice" } }

you might define columns that route to:

• pr_number → $.number
• state → $.state
• author → $.author.login

The JSON stays as-is; the columns decide which paths become tidy, typed columns for querying and reporting.

Getting data in

See Uploading & ingesting data.

Artifact processing (dynamic columns)

Beyond the columns that come straight from your data, you can add dynamic columns computed with SQL — for example, a derived status, a bucket, or a score. The processing runs over the source and stores the results as extra columns you can query and report on just like any other. See Querying your data (SQL) for how the queries work.

Derived artifact sources

You can also create a derived source — a brand-new source whose rows are produced by a SQL query over one or more existing sources. This is the way to build aggregations, cross-source joins, or computed tables that you then query and chart like any other source.

Purging vs. deleting

There are two ways to clear data, with an important difference:

• Purge removes the records in a source but keeps the source itself — its columns and configuration stay in place, ready for a fresh load. Use this to reset the data without rebuilding the source.
• Delete removes the entire source — its definition and all its records. Use this when you no longer need the source at all.