THE STRATEGIC SHIFT
Unlocking Value with Data-Centricity
For the first time, it is economically and technically feasible to move from a compromised, application-centric model to a true data-centric architecture. Storing data at its highest resolution and allowing all applications to query it on the fly is not an incremental improvement—it is a paradigm shift.
The Butterfly Effect in Your Data
The term "The Butterfly Effect" originated from the challenge of weather prediction, where tiny, seemingly insignificant variations in initial data led to wildly different long-term forecasts. The same is true for your business. Data reduction and rollups are the small variations that corrupt your initial data, leading to flawed models, inaccurate insights, and missed opportunities.
When you pre-process data for a single application—for example, calculating an average metric for a KPI dashboard—you destroy the outliers, the maximums, and the subtle patterns. That "lost" data might be exactly what a security analyst needs to find a threat, what a QA team needs to spot a product defect, or what an AI needs to learn a complex relationship. A data-centric model preserves this crucial, high-fidelity data, eliminating the butterfly effect of data reduction.
Opportunities Enabled by True Data-Centricity
Transformative AI Training & Inference
Train both generative and non-generative AI models on complete, unbiased, raw datasets instead of compromised samples. For inference, provide your models with real-time, high-fidelity context from the entire dataset, dramatically improving accuracy and reducing hallucinations.
Accelerated Data Science & Research
Empower your data science teams to explore the full, raw dataset without waiting for complex ETL jobs or data engineering support. They can test hypotheses and iterate on models in hours, not weeks, accelerating the pace of discovery and innovation.
Robust Baselines and Anomaly Detection
With access to the complete historical dataset, machine learning applications can build far more accurate and resilient baselines of "normal" behavior. This makes anomaly detection for cybersecurity, fraud, and operational monitoring significantly more effective, reducing false positives and catching sophisticated threats that would be lost in summarized data.