{
  "schemaVersion": "1.0",
  "language": "en",
  "id": "data-quality-knowledge",
  "name": "Data Quality & Governance",
  "description": "Data quality, freshness, stewardship, and decision use.",
  "alternateNames": [
    "データ品質・ガバナンス",
    "Data Quality",
    "Data quality",
    "Content Operations",
    "Information Freshness",
    "Data Governance",
    "Big Data"
  ],
  "canonicalUrl": "https://catalysphere.co.jp/en/topics/data-quality-knowledge/",
  "machineReadableUrl": "https://catalysphere.co.jp/en/topics/data-quality-knowledge/index.json",
  "updatedAt": "2026-10-01",
  "articles": [
    {
      "slug": "benchmark-contamination-detection",
      "title": "Does that benchmark score measure unseen capabilities?",
      "summary": "We examine the inclusion of the public test set in training data from multiple perspectives, including data duplication, completion behavior, and example order, to isolate suspicious scores from decision-making.",
      "quickAnswer": {
        "what": "High scores on public benchmarks do not constitute evidence of unseen capabilities if the test examples or similar examples were included in the training data.",
        "why": "Rather than making definitive judgments based on a single inspection, we evaluate suspicions by combining data-side duplication with black-box behavior.",
        "action": "Suspicious scores are placed in a separate category and reconfirmed through evaluations created after publication, non-public data, and time-split assessments."
      },
      "updatedAt": "2026-07-24",
      "url": "https://catalysphere.co.jp/en/articles/benchmark-contamination-detection/",
      "machineReadableUrl": "https://catalysphere.co.jp/en/articles/benchmark-contamination-detection/index.json"
    },
    {
      "slug": "synthetic-training-data-model-collapse",
      "title": "Recursive training on synthetic data can lose distribution tails before the average collapses",
      "summary": "Instead of uniformly prohibiting the reuse of synthetic data, we design learning data operations that separate the replacement, accumulation, and selection of real data to preserve rare cases and diversity.",
      "quickAnswer": {
        "what": "Synthetic data does not automatically cause model collapse, but recursive learning that replaces real data can lose the tails of the distribution and diversity.",
        "why": "This is because degradation depends not on the material name, but on how the retention of real data, reuse across generations, sample selection, and errors from finite data overlap.",
        "action": "Preserve every sample’s provenance and generation, test rare groups, tails and diversity separately from average performance, and quarantine deteriorating sources or categories before restoring the verified baseline and retesting."
      },
      "updatedAt": "2026-10-01",
      "url": "https://catalysphere.co.jp/en/articles/synthetic-training-data-model-collapse/",
      "machineReadableUrl": "https://catalysphere.co.jp/en/articles/synthetic-training-data-model-collapse/index.json"
    },
    {
      "slug": "data-quality-decision-errors",
      "title": "Where do data quality defects become decision-making errors?",
      "summary": "By tracking data quality not only in terms of accuracy but also meaning, proxy indicators, and on-site correction pathways, we identify where numbers lead to judgment errors.",
      "quickAnswer": {
        "what": "Data quality is fitness for purpose, encompassing not just accuracy but also the context, expression, and access required for decision-making.",
        "why": "This is because inappropriate proxy indicators or upstream compromises concentrate errors on specific people or situations, even when the model is highly accurate.",
        "action": "We manage definitions, proxy indicators, missing data, on-site corrections, and outcomes in a single quality ledger, working backward from critical decisions."
      },
      "updatedAt": "2026-10-01",
      "url": "https://catalysphere.co.jp/en/articles/data-quality-decision-errors/",
      "machineReadableUrl": "https://catalysphere.co.jp/en/articles/data-quality-decision-errors/index.json"
    },
    {
      "slug": "knowledge-base-content-operations",
      "title": "Internal AI answers fail on knowledge lifecycle before search accuracy",
      "summary": "RAG accuracy is determined not only by the model but also by how quickly document version, expiration, source, permissions, and deletion are reflected in the index; we consider operational design from knowledge benchmarks.",
      "quickAnswer": {
        "what": "Internal knowledge is not data ingested once, but a business product that requires continuous maintenance of source, version, expiration, permissions, and deletion.",
        "why": "This is because even if the search engine is correct, answer quality cannot be maintained if the knowledge source is outdated, conflicting, unable to trace back to evidence, or has different access permissions.",
        "action": "Assign document owners and expiration dates, measure the time from change to index reflection, and continuously test freshness and provenance using evidence-based questions."
      },
      "updatedAt": "2026-10-01",
      "url": "https://catalysphere.co.jp/en/articles/knowledge-base-content-operations/",
      "machineReadableUrl": "https://catalysphere.co.jp/en/articles/knowledge-base-content-operations/index.json"
    },
    {
      "slug": "big-data-decision-quality",
      "title": "Does Big Data Really Improve Decision-Making?",
      "summary": "Data volume and decision quality are not the same. We examine the conditions for a usable data foundation, considering metric meaning, causality versus correlation, update frequency, and on-the-ground context.",
      "quickAnswer": {
        "what": "The value of big data is determined not by storage volume, but by the ability to update critical decisions faster and more accurately.",
        "why": "Because inconsistencies in definitions, selection bias, and confusion between correlation and causality mean that more data can strengthen misconceptions, caution is required.",
        "action": "Design metrics, comparison methods, update responsibilities, and post-action learning loops one by one, working backward from the decision."
      },
      "updatedAt": "2026-07-24",
      "url": "https://catalysphere.co.jp/en/articles/big-data-decision-quality/",
      "machineReadableUrl": "https://catalysphere.co.jp/en/articles/big-data-decision-quality/index.json"
    }
  ]
}