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Data Enrichment


The most valuable data is often the product of more than one dataset. We help our clients enrich their business data with additional data points from secondary datasets to allow them to draw richer and more insightful conclusions. Therefore, we bring together various datasets from the public domain (climate, geospatial, demographic) or from proprietary sources to add nuance to data that inform real-world business decisions.



  • Analysis of the current scenario and definition of objectives
  • Identification of data sources and scouting
  • Budget evaluation


  • Planning the integration of various data sources into the existing scenario
  • Integrating the new data sources


  • Support in identifying concrete use cases for the newly integrated data sources
  • Developing new analytical models and generating insights


We leverage a mix of industry standards and proprietary accelerators for automation, risk mitigation and velocity.


We enrich your existing datasets with public or proprietary socio-demographic data to expand their possible use cases by adding new properties (e.g.: marital status of a person in a geographic area, number of children, type of car driven, education level).


We cross-reference any dataset with custom online profile personas to improve customer segmentation (e.g.: online browsing preferences, preferred devices, average time spent online, preferred connection type).


We enrich any dataset using social behavioral data to verify any insights relative to the geographical area of a marketing initiative (e.g.: preferences, habits, buying preferences, actions, eating preferences, basket analysis).


We use mobile footprints to perform exploratory data analysis and to identify behavioral patterns over time, assess social interactions, predict life outcomes and mobile intervention, and verify real behavior of customer segments (e.g.: cellphone presence data, mobile sensor data, light sensor data, mobile status, GPS status).


We use environmental data to compare changes in the environment and provide snapshots to get more insights about situational data (e.g.: weather dataset, food production dataset, wine production dataset, tidal level, wind trend).

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