Predictive analytics platform for decision management and optimisation. Supports various data sources including warehouses, databases and Hadoop. Delivers instant decisions to users and systems at impact points. Enables analysis of vast amounts of data with minimal movement
Data collection is gathering and analyzing information from various sources. Essential for research, analysis, and decision-making in various fields. Helps find answers to research problems and evaluate outcomes
Data mining discovers actionable information from large datasets using mathematical analysis. Mining models can be applied for forecasting, risk assessment, recommendations, and grouping. Process includes six basic steps: problem definition, data preparation, exploration, modeling, validation, and deployment
High-quality datasets must be accurate, diverse, and complex. Accuracy can be measured using Python interpreters for mathematical problems. Diversity is assessed through topic clustering. Complexity can be evaluated using other LLMs as judges
GDP data shows PPP terms with informal economy estimates and updated base year data. GDP growth rates calculated using World Bank PPP and IMF data. GDP per capita data sourced from UN Population Prospects database. Data quality rated on 0-100 scale, with A being best
GDP data shows PPP terms with informal economy estimates and updated base year data. Compound Annual Growth Rates (CAGR) derived from GDP data using World Bank and IMF data. GDP per capita calculated using UN Population Prospects database. Data Quality Index rated 0-100, with A (best) to E (worst) quality