Business intelligence (BI) is a field of information technology that helps analyse and visualise data from both historical collections and current or real-time collected information. Through the use of BI, data professionals can provide policy makers, other scientific experts as well as the public with an easy-to-understand entry point to discover and understand complex data relationships and big data collections in general. Dashboards like Tableau, which make up a large part of business intelligence today, provide such visual platforms for exploring and sharing data.
A dashboard is a visual display, a type of graphical user interface of all your data. While it can be used in all kinds of different ways, its primary intention is to provide information at-a-glance, such as key performance indicators (KPIs) relevant to a particular objective or business process.
In general a dashboard is often accessible by a web browser and receives information from a linked database. In many cases it’s configurable, allowing you the ability to choose which data you want to see and whether you want to include charts or graphs to visualize the numbers. Dashboards allow all kinds of professionals the ability to monitor performance, create reports and set estimates and targets for future work.
space4environment has in-depth knowledge of and experiences with the development and publication of dashboards, mostly using the Business Intelligence software Tableau. Thereby, data are extracted from linked databases. The links below lead to some recent examples of dashboards that we produced for the EEA in the context of our work in the ETCs DI (former ETC ULS) and BD.
For the implementation of the LULUCF accounting approach space4environment created a point grid database. A 50m grid spacing with a total of a bit more than 1 million points was chosen as it still allows the treatment of the data in standard office software (MS Excel) and still provides statistical significant information at national level.
The database was populated with national land cover / use data from existing national inventories for the year 1989, 1999, 2007, 2012, 2015 and 2018. Due the different nomenclatures used in the inventories, a translation into LULUCF categories was developed for each source nomenclature. Spatial inconsistencies between the different inventories were removed by a temporal harmonisation algorithm which allowed to differentiate real land use changes from technical differences.
See also here.