Question 1

What can Polytomic sync from Standard Metrics?

Accepted Answer

Companies, metrics, metric options, documents, budgets, notes, and users. Company custom columns are discovered from your own configuration and added to the company schema, so firm specific fields come through rather than being dropped. Worth setting expectations on three things people often ask for: there are no dedicated funds, investments, or financials objects. Fund linkage comes through as fund IDs on the company record, and financial data appears as metric rows with a financial category type rather than as a separate financials table.

Question 2

How are metric values and time periods represented?

Accepted Answer

Long form: one row per company, per metric category, per observation date. Each row carries the value, a converted value, the original and preferred currency, the FX rate used, and a cadence such as monthly that tells you the period granularity. There are no separate period start and end columns, so the date acts as the period anchor and the cadence tells you what it spans. Period semantics like whether a category is point in time come from the metric options object. This shape is more work to pivot but far more flexible than a fixed wide layout.

Question 3

Are metric values numeric in my data warehouse?

Accepted Answer

No, and this is worth planning for in your models. Values, converted values, and FX rates are exposed as strings, preserving exactly what the API returned rather than coercing them. Several boolean looking flags on metrics, including the budget metric, archived, and custom metric indicators, are also strings. So plan on casting in your transformation layer. The upside is that no precision is lost and no value is silently mangled on the way in, which for financial reporting is the safer default.

Question 4

Which objects sync incrementally?

Accepted Answer

Metrics and documents do. Metrics keep a checkpoint per company and request only records updated since it, and documents use a single global checkpoint. Notes are a partial case: the API has no filter, so Polytomic sorts by creation date and stops reading once records are older than that company's checkpoint, which means edits to older notes are not reliably captured. Companies, metric options, budgets, and users are read in full on every ETL execution. Because metrics and notes checkpoint per company, their stored position is composite rather than one timestamp.

Question 5

Why move Standard Metrics data into a data warehouse?

Accepted Answer

Because portfolio analysis rarely stops at what a reporting tool displays. Once metrics are in your data warehouse in long form you can build cohort comparisons, benchmark by sector and stage on your own definitions, join to your CRM to see which partner sources what, and drive LP reporting from a single modeled source instead of reconciling exports. Reverse ETL then runs the other way on the same platform, so a computed metric or coverage flag can be pushed onto a Salesforce or Attio record the investment team already works in.

Standard Metrics

Perfect pairings

Long form metric data

Custom columns included

Incremental metrics

Example workflows

What you can sync

From Standard Metrics

How it works

How to set up

Frequently asked questions

Why teams choose Polytomic

No engineering required

Flexible data modeling

Handles scale automatically

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