Geospatial Big Data ANALysis

At Gisaïa we envision a future where everybody with a question is able to work with Big Data, especially if the data relates to a specific location. 

Unfortunately, with the exponential growth of raw data organizations face various challenges that may threaten their competitiveness and efficiency:

• The ingestion, storage and retrieval of huge amounts of (un)structured data, with different formats and sources takes time to rapidly process into useful insights.
• A lack of analytical capabilities, as not many are trained data engineers and scientists, hinders broad knowledge dissemination and effective decision making.
• The many traditional systems in use for business and geospatial information (GIS) tend to be rather complex and cumbersome to use for non-geospatial experts.

The latest advancements in distributed storage and computing in the cloud deliver organizations a host of benefits. From avoiding the capital expenses of not having to invest in costly infrastructure to avoiding operational expenditure of managing their own infrastructure. Additional benefits are flexibility and scalability to adapt quickly to changing business needs. Especially valuable when we take into todays increasing big data storage, processing and management requirements into account.

Machine learning (ML) enables data analysis by automation of analytical model building. With ML systems can learn from your data, identify trends and patterns and make decisions with minimal human intervention and assistance. No longer do organizations have to employ an army of data engineers and scientists. No longer is intensive manual manipulation needed of complex and big data sets. The ML algorithms automatically learn and improve from experience without being explicitly programmed, to assist non-experts in their business intelligence and decision making.

Understanding is all about context. Luckily, a significant percentage of all data has a location marker that determines the ‘where’ of an asset, person or activity. This location provides contextual information on the relationship to features of other people, items and activities in geographical proximity. For instance, the speed and direction of a vehicle/person in space and time (temporal-spatial data). This information can be visualized through interactive easy-to-use map applications for experts and non-experts alike.

Combined  Cloud technology, Machine Learning algorithms and location awareness of your data have already allowed organizations to streamline processes, offer better services, gain market share, etc. As can be seen in a wide variety of sectors, such as; transport, utilities, government, finance and insurance, retail and manufacturing.