Quansic maintains the largest artist graph database with over 2.5 million ISNIs and 187 million asset identifiers.

We have developed highly efficient and reliable Artist data matching techniques without fuzzy matching.

We curate and enhance the largest data set of Performers and songwriters.

Automatic metadata enrichment

You can benefit from Quansic extensive ISNI-X dataset and partnerships to enrich your repertoire and monitor its exploitation.

We enrich and update your repertoire with:

  • International Identifiers: UPC, ISNI, ISRC, ISWC, BOWI, IPN, IPI, MBID, wikidata-id, Genius-id, Discogs-id
  • Main Platforms Identifiers: Apple-id, Spotify-id, YouTube Channel, Amazon-id, Deezer-id
  • Extended metadata: alias, name variants, date of birth, name in different languages, relationships contribution to sound recordings and musical works

Check out Data Explorer to see the full data elements that we maintain.

ISNI Matching Services

As an ISNI Registration Agency, Quansic core service is to assign an ISNI to all of your artists. In the process, we disambiguate and enrich your data set.

We use a combination of cutting edge technologies such as graph database and (patent pending) matching algorithms.

We have definitely banned the old and suspicious fuzzy matching techniques and operate in a 100% identifier environment.

Bridging all identifiers

Extending from our data, we also provide links from ISNI to major Music Industry identifiers (IPI, IPN, MusicbrainZ ID, discogs, wikidata,..) and to all major streaming platforms artist IDs (Spotify, YouTube, Apple, Amazon and more).

Extended data set

On top of the original ISNI data set, we have built ISNI eXtended – the largest Artist graph data set with over 14 million artists, 50 million data points and 160 million relationships.

As a Neo4j and Google cloud partner, we operate in highly scalable, secure and efficient cloud framework.

Extending knowledge beyond metadata

The graph topology provides us with additional inferred information derived from the inherent relational nature of the Music Industry.

By running finely tuned algorithms on our data set, we can derive business critical information such as identifying Musical Works that are in the Public domain or inferring beyond doubt ISRC to ISWC links.

For more information please contact us