Spatial Data in a Big Data World
When we think about spatial data, most of us have this vision in our mind of Google Maps and navigation; however, many people aren’t aware that spatial data plays a key role in other areas of focus, too, like the analysis of phenomenon and the dependencies between them. According to European Inspire legislation, public authorities on the national and community level should collect, harmonize, or organize the dissemination or use of spatial information. Critical geospatial information is also used by companies around the world to improve their decision-making process and development.
The beginning: From a classical world map on a wall
Maps were at one time an important tool because they demonstrated the spatial distribution of objects, but after some time, simply showing this information was no longer enough. People wanted the ability to store spatial data and combine them to get new information by using different map layers. In the beginning, maps were stored in an analog version; when the sixties emerged the concept of Geographic Information System (GIS) had been created and defined. Today, GIS should be understood as a system which allows to gather, analyze, and understand spatial data.
Why is spatial data so important?
We know that we were seeking to gather spatial data, but why? The reason for determining the spatial distribution of objects is usually for navigational purposes, but there are other uses for spatial data. For example, the Inspire portal; in times of crisis, like a volcano eruption, earthquake, failure in a nuclear power plant, or flood, we need to understand the range of the phenomenon and other details like the direction and speed of movement of harmful dust.
In the city level, a situation can arise where emergency medical services need to intervene and help a person in distress. Thanks to maps, which can show traffic jams and related information, we are able to calculate the shortest route to this person. Spatial data and spatial analysis are also widely used in crime investigations and predictions.
As we can see, spatial data is really popular and important in the public sector, but it’s not exclusive to this sector Nearly every trade and industry are using spatial data to accomplish their business goals. Take banking, for example. Banks are using spatial data for planning the optimal distribution of branch locations and ATMs. Some banking institutions are using spatial data to perform business analyses as well.
One of Collibra’s customers, which is a top five financial institution in the US, is using Esri technologies to gather all information regarding transactions via the internet, mobile, and in bank branch for spatial data analysis. This information helps in the decision-making process when it comes to determining things like which bank branch needs to be rebuilt, the locations for new branches, etc.
Spatial data governance
What is the difference between spatial data governance and any other type of data governance? Is spatial data governance less important for companies than it is for more traditional business information?
The answer is simple: there is no difference, spatial data can be just as valuable as any other source of data. Imagine if companies could store metadata for spatial data in Collibra; what if clients could ingest spatial data in Catalog and see information about it? Currently, Tableau is supporting spatial data and we can see the results of some analysis also in Catalog.
In the case of Tableau, we are only storing basic information like view image, description or tags. If we could ingest data via Catalog, we could store much more information including. references, maximum/minimum elevation, resource relationships, as well as basic information like the owner of the data, whether it’s been updated, and its connection to business terms. The usefulness of such information varies depending on the company type. What we can say for sure is that spatial data becomes more and more powerful in the everyday life of many companies.
Dominika Olejniczak is a QA Engineer at Collibra where she is responsible for quality in Catalog. Previously, Dominika worked as a GIS specialist. She’s also a big fan of machine learning, big data, IoT, and GIS.