Skip to main content
WealthTech100 2026

Explore the data

Pivot, filter, and chart the companies, products, and citations.

A point-and-click explorer over the coded dataset. Three tables: Companies (one row each), Products (one row per named product), and Evidence (one row per citation). Drag fields to pivot, filter, sort, and chart. Start from a saved view or build your own.

The explorer is built for desktop. Pivot tables don't work well on a phone — rotate to landscape, or come back on a laptop.

Click Configure to open the side panel. From there, drag any field into Group By, Split By, Order By, or Where to pivot and filter — and use the plugin picker at the top of the panel to swap between Datagrid and chart types (bar, line, scatter, heatmap, treemap).

Reading the confidence columns

The Companies table carries evidence-quality columns next to the values, because in a cited dataset those are part of the data rather than metadata to be stripped at presentation time:

Column What it tells you
enriched Whether the pipeline has researched this company yet. false rows are roster seeds.
category_confidence How confident the coder was. low triggers a second verification pass.
pricing_model_confidence Same, for the field most at risk of fabrication.
unique_ip_confidence Same, for the field most likely to attract marketing paraphrase.
gap_count / gap_fields How many fields could not be established, and which ones.
evidence_count How many verbatim citations back this company's record.
pricing_published true only where an actual figure is published. Distinct from a pricing model being known.
has_unique_ip true only where an IP claim survived the evidence requirement.

Filtering on enriched == true before charting is usually what you want — otherwise unresearched roster rows dilute every distribution.