The Problem
Develop the know-how to aggregate, analyze, and most importantly monetize the growing surge of available data
This value is obtained through
- Variable identification
- Place identification
- Relational inquiry/testing
The KNOW-HOW
Answering specific basic questions to accomplish a specific objective
¿Dónde invierto? (v1, v2 Explorador Planeación)
¿En qué invierto? (v3 BI Operación)
¿Cómo invierto? ( v4 Alineación)
THe TOolBox
Find solutions through algorithmic processes..
DATA COUNT
Filters, Range, Ranks, Quantiles
DATA DESCRIPTION
Stats, Correlations, Distribution Shape, Outlier Measures, Trends (if any), Ensemble Scores
THe TOOLBOSS
DATA INFERENCE
Imputation/Forecast, Time and Spatial Univariates,
Bias Estimation, Parameter Tests...
DATA MODELING
Latent Analysis, Multivariate Analysis,
Space and Time Dynamic,
Prediction/Simulation & Ensemble Modeling
The bestiary
DATA HIERARCHY
Numeric or Categorical
Spatial and Temporal spans
num, cat,
num_tmp, cat_tmp,
num_spc, cat_spc,
num_tmp_spc, cat_tmp_spc ...
The "where" Incognit
The highest ROI: Progress per Cost Unit
Identify Success Conditions
Greater and Better Information
Many Sources Consolidation
Exhaustive Knowledge of Agent/Place/Action Interactions
Fidelity Criteria and ...
BEaUTYFEEL Algorithms!
Vista de Lugar :: (Characterization via Features)
General: OPI Key Indicators
MultiSummary(aggleveldependent), %NA's,
Outlier Measure Dist, Global Decil Rank
Time ( growth rates distribution)
Space ( LISA, spill over... )
(Dis)Similar Places Comparison
Specific : Cluster / Classification + Fuzzy Logic
BEaUTYFEEL Algorithms!
Vista de Variable :: (Characterization over Places)
General
Places Ranking (outlierness), Summary, NA's, Fidelity Score, QualityRank, Correlation
Time (correlated series, forecasting, modeling)
Space ( spatiallyrelated vars )
(Dis)Similarity Variables Comparison
Specific : Feature Selection / Interaction Detection
specifics InTEL
En esta liga se encuentra una lista detallada
*_*
Coming soon.... BI: Arquimedes v3
