This web platform employs a SaaS model aided by cloud computing to collect, aggregate and visualize data from gas and oil production companies on the global scale. The mined data from mandatory disclosure reports is processed into statistics in the form of diagrams and maps.
152 country profiles
comprise detailed overviews and graphs.
are listed with their payments.
Resource Statistics is a SaaS platform that offers information on countries and companies engaged in mining and extracting natural fuels, primarily oil and gas. The source data is collected using disclosures obtained from relevant companies per governmental regulations. This web platform offers everyone free access to analyzed data that may be sorted and downloaded for the purposes of business intelligence.
This BI project combines data mining and analysis, as well as visualizing the resulted information in a convenient graphic form. Resource Statistics was developed under SaaS model and enhanced with cloud computing possibilities that allow it to store and process increasing volumes of source data and analysis results. To implement these and other beneficial features required to make this enterprise software a convenient source of valuable business information, we employed several proven technologies, including Node.js and Angular.
Resource Statistics presented minimum challenge, since we have previously developed similar solutions. The large amount of source data and the necessity to process it defined the choice of cloud computing for this enterprise software. To facilitate the development and deployment processes, our team used the “software-as-a-service” model that proved its effectiveness in this case.
The result of this project is the successful release of a web platform that meets client’s requirements and adds another entry to our portfolio. Our developers implemented all the required functionalities, such as data mining and processing, in order to provide users with analyzed and structured data. This result was achieved due to carefully selected optimal technologies aided by cloud computing. The software-as-a-service model proved to be advantageous for this enterprise software, as it allowed to reduce development and deployment time.