In today's constantly changing environment, it’s evident that companies need access to key data and metrics to meet customer demands, grow and stand out from their competitors. And perhaps this holds true even more so for businesses directly involved with their supply chain processes—those that are geared to obtain competitive intelligence; maintain contractual compliance; connect buyers with suppliers; generate sales leads; and conduct market research, all to help grow their businesses and maintain efficiencies throughout their entire network.
Take the commercial shipping industry. In this industry, access to business critical data is crucial to ensuring the success of large-scale cargo deployments and complex supply chains that span diverse geographical regions and markets. However, delivering timely access to data in this particular industry is difficult as information spans multiple sectors, arrives in different formats and must be standardized before it can be of any value.
Necessary data for effective BI
The primary source of this divergent data are Bills of Lading (BOL), the formal documents that contain the routing, parties involved and contents of all maritime shipments that enter and exit different nations—some of which, like the U.S., are very transparent in this kind of information. Others, like some European Union member countries, approach these documents differently. Along with the BOL data, there are multiple commodity, statistical and other internationally standardized data sets which combine to generate the massive amounts of data that make actionable business intelligence (BI) necessary to address such factors as knowing which trade lanes are over-booked; or how to ship to the U.S. and avoid possible delays because of labor issues.
With these divergent data sets, a variety of national languages and variations in things as simple as the spelling of a port or name of a supplier, combining this information to create intelligence can be very time consuming and costly. Raw data is like any raw material in that the quality varies over time. This also presents a large scale challenge: ‘How do you clean up the data to a level that is perfect without having to read through every one of millions of data points a day?’ To conquer this challenge requires innovative ways to clean, structure and map data points. Without such technology—and at times manual input—data would be unusable.
As the industry grew into a more complex environment, companies were forced to adapt more robust technology products. UBM Global Trade uses BI technology provided by New York, N.Y.-based Information Builders to help create an IT infrastructure which allows us to process volumes of data effectively. We allow these technology stacks to take on the load of standardized tasks—like audit, monitoring, statistical model and report generation—and many others so our teams can focus on developing proprietary algorithms to do the more complex components in our processes. Our customers range in how they accept and utilize our intelligence in their enterprises; from raw data dumps to sophisticated graphical representations of movements across trade lanes. But regardless of their technology needs, it’s imperative that we provide them with unfettered access to data analysis in whatever manner they require.
A different type of data set
Intelligence helps support five key business needs across the supply chain and normal business operations. Prior to the existence of sophisticated intelligence analysis, many in the industry relied upon networks of connections to help facilitate change or establishment of new supply chain elements. As the volume and complexity of interaction trade has grown, these connections, while still important, need augmentation and analysis. What was trucking to and from Canada and Mexico many years ago is now produced in multiple countries, shipped, partially assembled in other nations and then shipped again, with final assembly completed in the destination nation. The auto industry presents a good example of this diversified process.