{
  "id": 11400177,
  "title": "Why executives still don’t trust their data – and how to fix it",
  "url": "https://urgent.news/2026/10/02/why-executives-still-dont-trust-their-data-and-how-to-fix-it",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-10-02T07:47:19.000Z",
  "source": {
    "name": "ITWeb",
    "slug": "itweb",
    "url": "https://www.itweb.co.za/article/why-executives-still-dont-trust-their-data-and-how-to-fix-it/LPwQ57lbVVZqNgkj"
  },
  "original_language": "en",
  "account": "South African business leaders are still wary of their organisation's data, despite substantial investments in cloud infrastructure, advanced analytics, and artificial intelligence. Executives often receive conflicting information from various departmental dashboards when inquiring about basic operational data, like the number of active customers or the most profitable product line. The advanced technology stack has not translated to increased trust in the available information.\n\nThe core issue lies in fragmented data sources, which are rarely unified to serve as a single source of truth. Legacy systems such as ERPs, CRMs, operational platforms, and rogue spreadsheets have not been designed to provide a cohesive enterprise-wide data foundation. Consequently, duplicate customer profiles, inconsistent product identifiers, and conflicting operational figures arise. Data quality deteriorates gradually through missing fields, outdated records, and broken business logic. When executives realize the unreliability of information, they revert to manual spreadsheets and reconciliations, inadvertently worsening the situation.\n\nAnother contributing factor is semantic ambiguity. Different business leaders may have divergent definitions for fundamental concepts like customers or gross margin. This lack of shared understanding hinders the integration of systems, even when they appear technically connected.\n\nArtificial intelligence, while promising, can exacerbate bad data rather than remedy it. If customer entities are duplicated or KPI logic is fractured, AI-driven machine learning models will merely produce flawed insights at an accelerated pace.\n\nHigh-performing organisations prioritize building a strong data foundation to overcome the trust gap. They adopt five practical execution steps:\n\n1. Construct an active, intelligent data inventory: Leading companies utilize active, intelligent data catalogues that continuously discover, classify, and map relationships across the entire data estate. Data quality is not a one-time project but an ongoing operational discipline.\n\n2. Implement automated data quality management: Forward-thinking organizations deploy automated observability platforms that generate AI-driven rules and continuously monitor and detect anomalies in data quality. While not every database column requires extensive management, core business entities such as customers, products, suppliers, and materials undoubtedly do. Deploying specialized master data management (MDM) platforms enables organizations to identify duplicates, harmonize records, and synchronize golden records across disparate systems without replacing existing operational systems.\n\n3. Adopt practical and lightweight governance models: Traditional data governance frequently fails due to bureaucratic committees and excessive documentation. Successful organizations embrace practical governance models, clarifying ownership, setting light decision rights, and integrating stewardship into daily business operations instead of imposing burdensome processes.\n\n4. Ensure continuous improvement: Attempting to complete governance before delivering tangible business value is a recipe for failure. Successful data leaders embed governance into practical data management improvements, allowing governance to become a natural outcome of value creation.\n\nIn the evolving landscape of South African commerce, the ultimate competitive advantage lies in possessing a robust information foundation. When executives can trust their data, they stop debating calculations and begin making confident, high-impact strategic decisions.",
  "summary": "A modern data foundation guarantees data is trusted, governed and understood long before it hits a report or feeds an AI model.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}