70% of AI projects fail and the #1 reason is poor data quality. You can hire the best data scientists and license cutting-edge AI platforms, but garbage in still means garbage out. PiLog DQG Suite builds the AI-ready data foundation that turns stalled pilots into production ROI.
AI projects fail due to poor data quality (Gartner 2025)
Data scientists time 'wasted' on data preparation vs. actual modeling
Data scientist productivity improvement (80% → 20% prep)
Supply chain cost reduction via AI with quality data (McKinsey)
60-80% of data science effort is wasted on data preparation and wrangling rather than modeling, creating a permanent pilot-to-production gap.
Inconsistent identifiers, missing attributes, and lineage gaps lead to unreliable predictions and model degradation over time.
Low-quality data inputs undermine confidence in supply chain planning and predictive maintenance AI outputs, killing adoption.
63% of organizations lack AI-ready data practices, resulting in millions in wasted AI technology, talent, and consulting fees.
PiLog DQG Suite creates the governed, enriched, and standardized master data that AI/ML pipelines need to work reliably, reducing data prep time from 80% to 20% and boosting AI project success rates to 90%+.
"High-quality, well-governed data is the critical success factor for scaling AI initiatives beyond pilots. Embedding MDM and DQ controls directly into AI/ML pipelines improves model reliability and auditability."
AI projects fail due to poor data quality (Gartner 2025)
Data scientists time 'wasted' on data preparation vs. actual modeling
Data scientist productivity improvement (80% → 20% prep)
Supply chain cost reduction via AI with quality data (McKinsey)
Let us assess your data readiness for AI and show the acceleration opportunity, before your next pilot.
