By some accepted projections, data creation worldwide is set to grow by 4300% by 2020 (CSC, 2012). Nowhere is this trend more critical than in the trade and financial sector, where NU Borders has tremendous data management experience. We have seen the same trend in the global trade community with customs administrations and the private-trade sector with a rapid transition from manual paperwork to mandatory e-formats.
NU Borders’ data management expertise has evolved from within compliance agencies with regulatory authorities over U.S. financial and trade sectors. Our personnel have first-hand experience in designing data management systems with those trade sector requirements in mind.
Companies who automate best practices for e-compliance – customer screening and vetting, agency data submission, duty and tariff rate requirements, licensing procedures – within their internal data systems will have competitive advantages by significantly lowering transaction costs and enhancing operational efficiencies. NU Borders can assist an organization in setting up efficient data stores – both transactional and analytical. Developing this capability is now critical as globalization of economies intensifies and cross-border compliance demands require automated data sets and e-submissions.
Additionally NU Borders can assist an organization with enterprise data management (EDM), which allows organizations to maintain accurate data models and data structures. By mapping an organization’s business process, NU Borders will develop an accurate business architecture, which can then feed data reference models, enterprise data models and other critical architectural components an organization’s data supply chain.
Efficient management of an organization’s data systems and stores is critical in developing cost-effect business processes. By applying data governance and master data management techniques learned at the Department of Homeland Security, NU Borders can provide an organization with advanced tools and procedures to manage their data sets. This will allow for significant cost savings for an organization by reducing duplicative data sets, as well as allowing an organization to be architecturally prepared to integrate new and improved analytical tools into their IT ecosystem in an efficient manner