Datasets

A dataset is one analytics table that Solya maintains about your business — your products, stock, sales, and the metrics derived from them. The datasets page lists every dataset you can access, grouped into six universes, and the datasets explorer lets you open each one in a read-only table view.

The principle

All datasets are produced by the data platform’s ingestion and computation pipelines, then organized into two analytics layers:

Silver — cleaned, normalized source data: your reference and master entities (products, brands, shops…) and conformed taxonomies. This is the trustworthy version of what your POS systems sent.
Gold — business-ready analytics built on top of silver: facts (transactions), snapshots, per-entity summaries, trends, forecasts, and AI decision data. This is what powers dashboards, KPIs, search, alerts, and recommendations.

Two things are true for every dataset:

Read-only — datasets reflect ingested data; you don’t edit them here. To change what they contain, fix the source data or the ingestion spec.
Scoped to your organization — you only ever see your own data.

Using the explorer

Open any dataset from the datasets page to:

Browse the rows in a table view.
Filter by the dataset’s columns.
Paginate through rows and inspect column values.
Export the current view for offline analysis.

The six universes

The datasets page groups every dataset into six universes. Below is what each one contains and what it’s used for.

References

Your master and reference entities — the “who and what” of your catalog and network. Built in the silver layer from your POS data and enriched (taxonomy classification, image scraping) by the platform.

Dataset	What it holds	Powers
Products	Master product records (name, brand, description, images).	Product cards, search, every product-level analysis.
Product variants	Variant-level detail (size, color, collection).	Variant cards, size curves, plan line items.
Inventory items	The product × shop inventory identity.	Stock attribution, per-shop inventory views.
Brands	Brand master data.	Brand dashboards, brand-level filters.
Shops	Store / location master data (address, region, country).	Shop selectors, geographic analysis.
Suppliers	Supplier master data.	Procurement, order and supplier-return plans.
Countries	Country reference data.	Localization, geographic grouping.
Collections	Seasonal / product collection definitions.	Collection-based filtering and planning.

Taxonomies

Classification hierarchies that organize your catalog (family, gender, color, size). Derived during ingestion, partly via AI classification.

Dataset	What it holds	Powers
Taxonomy	The global classification hierarchy.	Consistent categorization across the app.
Brand taxonomy	Brand-level classification.	Brand grouping and navigation.
Product taxonomy	Product-level classification (family, gender).	Category filters and reporting.
Variant taxonomy	Variant-level classification (color, size).	Size curves, color/size breakdowns.

Transactions (facts)

The event-level record of what happened — denormalized star-schema fact tables in the gold layer, built from your sales, orders, and movements.

Dataset	What it holds	Powers
Sales lines	Individual sale line items (quantity, price, cost, margin, discounts).	Sales, margin and discount analysis; KPIs; forecasts.
Order lines	Purchase / order line items (quantity, cost, supplier).	Procurement analysis, supplier performance.
Movement lines	Stock movements (receipts, transfers, adjustments).	Stock-flow analysis, transfer tracking.

Stock

The state of your inventory, both historically and right now.

Dataset	What it holds	Powers
Stock ledger	Historical stock movements with running balances and cost.	Stock aging, historical reconstruction, cost of stock.
Stock snapshot	Current stock by shop × variant, with days-of-supply.	Stockout-risk alerts, reorder points, stock valuation.

Summaries

Pre-aggregated per-entity metrics so common questions answer instantly without scanning the facts.

Dataset	What it holds	Powers
Product summary	Per-product aggregates (sales, stock, performance).	Product cards and dashboards.
Brand summary	Per-brand aggregates.	Brand portfolio analysis.
Shop summary	Per-shop aggregates.	Store performance and comparisons.
Variant summary	Per-variant aggregates.	Variant cards and recommendations.

Analytics

Pre-computed trends and entity × shop metrics that feed visualizations and risk detection.

Dataset	What it holds	Powers
Sales trends	Daily sales aggregates with rolling windows (7d/30d/90d).	Trend charts, demand sensing.
Stock trends	Historical stock levels by time window.	Stock trend charts, stock-out risk.
Product-shop analytics	Pre-aggregated product × shop metrics.	Store allocation, product-by-shop performance.
Brand-shop analytics	Pre-aggregated brand × shop metrics.	Brand performance per store.

For the full technical reference — every table and column, including the gold-only AI tables (forecasts, decision vector) — see the data catalog. For the operational, app-managed data you create (plans, rules, alerts, tags…), see the data model.

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Apps

Data layer

Data platform

Intelligence layer

Inventory plans

Signals & alerts

Inventory risks

Tags & automation

Analytics

Administration

The principle

Using the explorer

The six universes

References

Taxonomies

Transactions (facts)

Stock

Summaries

Analytics

​The principle

​Using the explorer

​The six universes

​References

​Taxonomies

​Transactions (facts)

​Stock

​Summaries

​Analytics

The principle

Using the explorer

The six universes

References

Taxonomies

Transactions (facts)

Stock

Summaries

Analytics