DataRoad is a groundbreaking Data Logistics infrastructure that empowers universal connectivity and dynamic data transformation.

By Carl Mattocks

It serves as a comprehensive real-time distributed knowledge graph for digital information, facilitating seamless sharing and compliance enforcement across jurisdictions. With self-describing data, DataRoad enables on-demand transformation, matching, and linking to suit the specific requirements of any application.

Data AI Logistics (DAIL)

DAIL (Data AI Logistics) support an assembly of unifying actions that empower managers to build trustworthy and value-based positioned data economy products. Acknowledging that the data economy is a complex and ever-evolving ecosystem, assemblage outcomes include:

  • Raw Data is the data created through everyday activities like online browsing, social media interactions and businesses transactions.
  • Secured Insight is the raw data transformed securely and efficiently into meaningful insights through semantic metadata layering, artificial intelligence augmentation, channel protections.
  • Monetized Property is enforcement of intellectual property licensing agreements that expand business models and revenue streams.
  • Boundary Guardrail controls assure responsible data collection, use, and protection.
  • Digital Infrastructure scalable components connect devices and relay products from one point to another.

Trusted Marketplace

  • To operate a reliable marketplace DAIL enables buyer and seller to be trusted participants. Specifically, to promote accountability, transparency and agreement governance, DAIL explainability (factors influencing outcome) and observability (anomaly detection) capabilities support:

  • Unveiling Biases within the data by tracing lineage and algorithmic reasoning.

  • Measuring Performance using key metrics, like accuracy, precision, recall, to facilitate proactive intervention and recalibration for sustained effectiveness.

  • Optimizing Resources by promoting use of underutilized assets while ensuring timely delivery of quality products.

  • Accelerating Diagnosis that determine root cause, when a product malfunctions, parameters have inconsistencies and environmental factors change.

  • Facilitating Experimentation that allows real-time comparison of value-based configurations.

  • Reducing Risk by enforcing policy that fulfill licensing agreements.

Quality Boundary

For quality provisioning DAIL boundary guardrails help determine the extent data economy products meet predefined standards, satisfy tolerance requirements and exceed expectations of reliability, accuracy, fairness as defined by boundary specific metrics. e.g. completeness percentage, latency benchmark, granularity level. Setting of quality boundaries can include:

  • Integration Verification that different components work together seamlessly.
  • Algorithm Validation which accepts diverse set of inputs and conditions.
  • Bias Assessment for data points reference domain sensitivity specifications.
  • Ethical Considerations in algorithm’s design use a referenceable problem-solving strategy.
  • Regulatory Compliance involves domain specific measures e.g. do no harm.
  • Representativeness Relevance reflects the characteristics of the entire product.
  • Data Leakage protections prevent synthetic data inadvertently influencing expectations.

Scenario Simulation

DAIL scenario simulation provides a consistent, safe and realistic way to observe how use case definitions determine the outcome of a proposed data economy product. Simulations are likely to be accurate, reliable, and actionable when use case scenarios reference:

  • Product Domain understanding that spans context, constraints, goals and potential impact.
  • Collection Preparation that affirms device connectivity can securely relay all raw data patterns.
  • Pre-process Transform templates with slots for missing values, outliers, inconsistencies, and issues.
  • Feature Interpretability reasoning that is weighted by importance of characteristics and relationships.
  • Feedback Iteration that can contrast and compare similar scenarios.