Fragmented data is causing businesses huge issues - especially when it comes to AI (www.techradar.com)

New HubSpot research warns that fragmented customer data is already costing businesses revenue and blocking AI progress: 34% of companies report revenue loss from scattered data, only 31% say most of their data is accessible to AI, and just 9% trust their data for accurate reporting. The study finds 92% of organizations keep valuable insights outside their CRM—in spreadsheets, chats and other unstructured places—37% lose productivity reconciling records, and 74% still manually transfer data into CRMs at least weekly. As a result, 88% face a major AI-adoption challenge and 71% of UK firms have had AI projects delayed or disrupted by GDPR or the incoming AI Act. For AI/ML teams these findings are a red flag: models and analytics are only as good as the pipelines feeding them. Fragmentation increases data latency, creates labeling and governance gaps, and prevents unified feature stores, effective training datasets and reliable reporting. The practical remedy HubSpot recommends—centralizing customer data into a single, governed source of truth—implies investing in ingestion/ETL, metadata, APIs, schema harmonization, and privacy-aware access controls so data is queryable, auditable and AI-ready. Firms that pair consolidation with proactive regulatory compliance will be better positioned to extract trustworthy insights, scale ML initiatives, and convert AI-driven discovery into competitive advantage.