Spain: CARTO, the leader in location intelligence, unveiled today at the CARTO Locations conference its new data strategy, anchored on derivative data streams, which optimize modern location data sources to help organizations to better harness the power of location intelligence across a variety of business challenges.
Derivative data streams, which pull raw data from actual business transactions and events, as opposed to traditional, static demographic data to create brand new data sets not available anywhere else, will be included in CARTO platform for the first time. These streams are combined from multiple sources such as financial transactions, GPS and telco, social media, vehicle sensors, and more to allow citizens, governments and organizations to make decisions based on the current state of a situation rather than making assumptions on what likely happened in the past.
“Organizations want to benefit from the modern data sources made available by IoT and Big Data, but we often hear from them that they are limited to the information they collect, or to outdated, point-in-time data sets,” said Javier de la Torre, CEO at CARTO. “We are taking the lead to make it easy for these organizations to actually use those newly available data streams to solve business problems.”
Available immediately, the Foot Traffic data layer combines mobile events and GPS data to measure the number of pedestrians in transit to and from distinct locations. Users can leverage insights about foot traffic to make critical business decisions such as site selection, marketing planning, competitive analysis or sales forecasting. This is available through CARTO’s Data Observatory, which already includes a wide variety of data sets from a network of industry-leading partners such as Zillow, the Consumer Data Research Centre, and the National Institute of Statistics and Economic Studies.
Typically, raw data collected is difficult to work with and contains biases which often lead to errors in decision-making. In addition to anonymizing and aggregating the data to ensure responsible use, CARTO’s Data science team cleans and removes biases in the data, making it ready for spatial analysis and visualization. Organizations can immediately combine it with their own data and Location Intelligence solutions for more comprehensive analyses.
To make these new data sources more accessible for analytics, CARTO is also announcing the ability to query data from its Data Observatory throughCARTOframes, meaning data scientists can leverage CARTO data through Python programming language without leaving their Python environment. CARTOFrames creates connectors to the CARTO Data Observatory so data scientists can supplement their own data with third party sources for better models, predictions, and machine learning processes in their preferred environment.