{
  "id": 15601270,
  "title": "Why Data Modeling Is Not Enough",
  "url": "https://urgent.news/2026/10/19/why-data-modeling-is-not-enough",
  "topic": "business",
  "section": "Business",
  "published": "2026-10-19T11:00:59.000Z",
  "source": {
    "name": "MIT Sloan",
    "slug": "mit-sloan",
    "url": "https://sloanreview.mit.edu/article/why-data-modeling-is-not-enough/"
  },
  "original_language": "en",
  "account": "Data modeling is insufficient for successful business outcomes, as illustrated by a global retailer's experience. The company's data science team developed sophisticated models that predict product demand with high accuracy. However, when the CEO asked about optimal inventory levels and distribution, the models failed to provide actionable insights. This scenario is common, with studies indicating that 60% to 85% of data science projects fail due to a disconnect between the models and business requirements. The main issue is that data scientists focus on prediction accuracy, while managers prioritize decision-making for business metrics like cost, market share, and profit. The language barrier between the two groups further complicates the situation. Decision science seeks to bridge this gap by modeling business decisions directly, aligning data models with business goals. It focuses on problem framing, decision modeling, and business domain knowledge, complementing the data-centric approach of data science. Decision science involves problem framing and solution identification, while data science excels at data modeling and prediction. The disconnect between the two fields often leads to irrelevant problems being solved or the wrong metrics being used, causing management to reject solutions. By adopting decision science, organizations can ensure that data models are directly linked to business decisions, increasing the likelihood of successful implementation and ROI.",
  "summary": "Otto Dettmer/Ikon Images Imagine that, as an operations manager at a global retailer, you have invested heavily in data science. Your data science team has built sophisticated models that produce demand predictions for the company’s products with impressive accuracy. Yet, when the CEO, seeking to increase profit, asks you how much inventory should be held […]",
  "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."
}