{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:JYL5QWLDMPLYBRXPGUPA3J5GOQ","short_pith_number":"pith:JYL5QWLD","schema_version":"1.0","canonical_sha256":"4e17d8596363d780c6ef351e0da7a674093e0387eacc1f0e02d6ea975a242176","source":{"kind":"arxiv","id":"1606.03510","version":1},"attestation_state":"computed","paper":{"title":"Defining and Conceptualizing Actionable Insight: A Conceptual Framework for Decision-centric Analytics","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Shiang-Yen Tan, Taizan Chan","submitted_at":"2016-06-11T00:25:19Z","abstract_excerpt":"Despite actionable insight is widely recognized as the outcome of data analytics, there is a lack of a systematic and commonly-shared definition for the term. More importantly, existing definitions are generally too abstract for informing the design of data analytics systems. This study proposes a definition for actionable insight in data analytics. For this purpose, this study conceptualizes actionable insight as a multi-component concept. The components, namely analytics insight, synergic insight, and prognostic insights are grounded in theories from multiple disciplines. Collectively, the c"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1606.03510","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CY","submitted_at":"2016-06-11T00:25:19Z","cross_cats_sorted":[],"title_canon_sha256":"ee1243549bf3bd472f41d545889c5f595d15a8c9ea89bd9a53d2f6a2510b5c2f","abstract_canon_sha256":"5ed803275a16247764128bb1b6c41a7302f59924ad96981c5f2e7010c15c7467"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:12:33.875621Z","signature_b64":"wGgkdUZdlpXSsIMQywouyDh4kaT8pEO5HfCW1crwArDkIUqMXPtnsxzdh6qXwcSxmTn7kPrcUDe3pXL6/4W7AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e17d8596363d780c6ef351e0da7a674093e0387eacc1f0e02d6ea975a242176","last_reissued_at":"2026-05-18T01:12:33.875132Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:12:33.875132Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Defining and Conceptualizing Actionable Insight: A Conceptual Framework for Decision-centric Analytics","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Shiang-Yen Tan, Taizan Chan","submitted_at":"2016-06-11T00:25:19Z","abstract_excerpt":"Despite actionable insight is widely recognized as the outcome of data analytics, there is a lack of a systematic and commonly-shared definition for the term. More importantly, existing definitions are generally too abstract for informing the design of data analytics systems. This study proposes a definition for actionable insight in data analytics. For this purpose, this study conceptualizes actionable insight as a multi-component concept. The components, namely analytics insight, synergic insight, and prognostic insights are grounded in theories from multiple disciplines. Collectively, the c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.03510","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1606.03510","created_at":"2026-05-18T01:12:33.875201+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.03510v1","created_at":"2026-05-18T01:12:33.875201+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.03510","created_at":"2026-05-18T01:12:33.875201+00:00"},{"alias_kind":"pith_short_12","alias_value":"JYL5QWLDMPLY","created_at":"2026-05-18T12:30:25.849896+00:00"},{"alias_kind":"pith_short_16","alias_value":"JYL5QWLDMPLYBRXP","created_at":"2026-05-18T12:30:25.849896+00:00"},{"alias_kind":"pith_short_8","alias_value":"JYL5QWLD","created_at":"2026-05-18T12:30:25.849896+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2601.20086","citing_title":"Evaluating Actionability in Explainable AI","ref_index":61,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JYL5QWLDMPLYBRXPGUPA3J5GOQ","json":"https://pith.science/pith/JYL5QWLDMPLYBRXPGUPA3J5GOQ.json","graph_json":"https://pith.science/api/pith-number/JYL5QWLDMPLYBRXPGUPA3J5GOQ/graph.json","events_json":"https://pith.science/api/pith-number/JYL5QWLDMPLYBRXPGUPA3J5GOQ/events.json","paper":"https://pith.science/paper/JYL5QWLD"},"agent_actions":{"view_html":"https://pith.science/pith/JYL5QWLDMPLYBRXPGUPA3J5GOQ","download_json":"https://pith.science/pith/JYL5QWLDMPLYBRXPGUPA3J5GOQ.json","view_paper":"https://pith.science/paper/JYL5QWLD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.03510&json=true","fetch_graph":"https://pith.science/api/pith-number/JYL5QWLDMPLYBRXPGUPA3J5GOQ/graph.json","fetch_events":"https://pith.science/api/pith-number/JYL5QWLDMPLYBRXPGUPA3J5GOQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JYL5QWLDMPLYBRXPGUPA3J5GOQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JYL5QWLDMPLYBRXPGUPA3J5GOQ/action/storage_attestation","attest_author":"https://pith.science/pith/JYL5QWLDMPLYBRXPGUPA3J5GOQ/action/author_attestation","sign_citation":"https://pith.science/pith/JYL5QWLDMPLYBRXPGUPA3J5GOQ/action/citation_signature","submit_replication":"https://pith.science/pith/JYL5QWLDMPLYBRXPGUPA3J5GOQ/action/replication_record"}},"created_at":"2026-05-18T01:12:33.875201+00:00","updated_at":"2026-05-18T01:12:33.875201+00:00"}