Environmental, Energy, Emissions, and Compliance Management in the Cloud presented by Locus’ CEO, Neno Duplan.
RailTec, University of Illinois at Urban-Champaign
Abstract of Original 2012 Presentation Follows:
As they go about the lengthy, tedious, expensive and very often dirty job of decontaminating polluted industrial sites, environmental consultants bill their clients by the hour, capturing…and then completely controlling…the superabundance of project-related environmental data that underlies remediation strategies. As a result of this process, a “consultant-centric model” has dominated the field of corporate environmental data management. This is primarily because environmental data is not integral to the daily functioning of a company, and because the quantities and complexities of the data produced are enormous. So company managers are generally quite comfortable with letting their consultants do all the querying, analysis, reporting…and then storing the data.
And since the consultants derive increased billing hours from controlling their clients’ data, the ultimate incentive for them is a renewed or extended contract, an outcome which, though certainly not guaranteed, is optimized by their control of the data.
But change is coming. The environmental data management practices of corporations and their consultants are undergoing a profound transformation as new Web-based software provides a low-cost means of making available the critical information that organizational decision makers need not only to better understand and manage their overall environmental liabilities but also to improve their operations by analyzing the valuable data. While environmental data is collected primarily for compliance reporting, when mined with the right tools it can also be used to point to weaknesses in data gathering and processing operations and provide valuable information on how to eliminate or reduce these.
A new “company-centric” environmental data management model now offers a remote data repository situated in the Internet “Cloud” and equally accessible in real time to all, including both the client and its consultants.
Cloud computing is a software outsourcing model that offers great promise for managing environmental, energy, emissions, and compliance information of any type. It is slowly making its way into companies that have to manage large quantities of data and meet routine compliance requirements. The model fits the way environmental information needs to be managed through mashups (applications that integrate data or functionality from multiple sources or technologies), and has the potential to completely upend the way railroad industry organize, manage, and report their environmental and energy data and information. Companies that have large portfolios of sites and facilities can use Cloud computing as a very low-cost means to take control of their mission-critical environmental data and information, gain new functionality and capabilities, and at the same time circumvent the involvement of their IT department if they so desire.
Cloud-based data management can completely replace existing stand-alone data systems and reporting tools to provide a comprehensive integrated solution to the railroad industry’s one of the most vexing problems—the centralization and management of complex data pertaining to contaminated water, groundwater, soil, and air.
At many contaminated transportation sites or at facilities and other sites contaminated with hydrocarbons, Cloud-based information management systems already provide market-tested solutions that were rapidly deployed and provide a high level of functionality and data security, an extensive set of QA/QC standards, and scalability.
The Cloud provides a platform for the complete electronic processing of analytical data, emissions data, compliance activities, and sustainability data beginning with the upload of electronic data deliverables from labs, and terminating in state-mandated or federal regulatory exports and reporting. When companies use such Software as a Service (SaaS) models, they eliminate most of the difficulties associated with the management of complex data sets while offering the opportunity for more rapid customization of data reporting to meet the changing needs of the industry.