Library Guide
This guide is designed to help you navigate the Carbon Arc data asset library—a curated catalog of data products powering the Platform. It outlines how to evaluate data assets using a standardized specification system that surfaces critical attributes such as panel size, geographic coverage, data frequency, and supported use cases.
Whether you're exploring data for investment research, demand forecasting, or campaign optimization, this guide will help you read the Library and determine the data assets best suited to your goals, timeline, and budget. The data asset library itself is built for speed and usability, with a streamlined interface, flexible filters, and information dense dataset pages that provide transparency into schema, metrics, and available delivery methods—ensuring you can move from discovery to decision with confidence.
Video Walkthrough
Dataset Display
Data assets are displayed with structured metadata fields to enable rapid comparison. Each entry contains key specifications:
- Name — Title that reflects the dataset’s type and insight focus
- Dataset ID — Unique identifier for API queries and support references
- Description — Overview of methodology, scope, and workflow positioning
- Price — Per-megabyte cost model for budget planning and cost control
- History — Time span of available data for trend analysis
- Frequency — How often data is updated (e.g., weekly, monthly, historic)
- Lag — Time delay between real-world event and data asset update
- Last Update — Timestamp of the most recent data refresh
Sorting the Library
Data assets can be sorted using arrows beside each column header:
- ↑ Ascending order (e.g., earliest to latest)
- ↓ Descending order (e.g., latest to earliest)
Use sorting to compare timelines, pricing, or refresh intervals. Example: Sort by History to see which data assets offer the longest backfill coverage.
Filtering the Library
Use the left sidebar to filter by key criteria:
- Delivery Method — Table or Graph
- Ecosystem — Thematic grouping of data assets
- Allocation — Intended workflow domain
- Type — E.g., transactional, behavioral, structural
- Subject — Topic of analysis (e.g., employment, spend, brand)
You can also use the search bar to find data assets by keyword.
Understanding Dataset Pages
Clicking on a data asset opens a detailed view with the following sections:
Overview
Provides the context, coverage, and primary questions the data asset answers. This anchors the data asset in your workflow.
Key Metrics
Lists the KPIs or fields you’ll extract. These define what you can measure and analyze.
Other Product Highlights
Explains the level of aggregation, granularity, and refresh cadence. Clarifies precision and use case alignment.
Additional Information
Identifies strong-performing categories, sectors, or brands where the data asset provides exceptional signal.
Example Use Cases
Shows real-world scenarios and decisions enabled by the data asset, helping you validate its value for your organization.
Schema
Detailed structure and field descriptions.
Dataset Specification Reference
These terms appear consistently in both table and detail views:
| Field | Definition |
|---|---|
| Dataset ID | Unique identifier for ordering or API access |
| Pricing | Cost per megabyte to access data asset |
| History | Timeframe covered by the data |
| Frequency | Refresh schedule (e.g., weekly, historic, monthly) |
| Delivery | File format or streaming method |
| Panel Size | Number of entities or observations in data asset sample |
| Coverage | Scope of brands, categories, or geographies included |
| Bias | Known geographic, demographic, or sample collection skew |
| Lag | Time delay between real-world activity and data asset refresh |
| Geographic Availability | Countries, regions, or zip codes covered |
Build with Data
Use the “Build with Data” icon to jump into Carbon Arc Builder with this data asset preloaded. Builder provides structured exploration, insight creation, and export tools tied to the dataset’s ontology.
Summary
Carbon Arc’s Library makes it easier to find the right data asset, compare options, and activate high-quality insights. Whether you’re filtering for coverage or evaluating cost vs. history, the goal is simple: help you choose with clarity.