Why normalized data matters
We are monitoring import cargo for 68 terminals in the US and Canada today, and while 68 isn't a massive number of sites, it does represent a significant undertaking in cleaning up data that is incredibly inconsistent.
Before we launched our platform, if you were trying to find out the status of your container, not only would you have to search across a variety of sources (even more when you include shipping lines), but you also would have to decode the data once you found it. The difference between 5/04/2017, 2017-05-04, and 05.04.2017 isn't huge, but it does mean that you have to pay attention to be sure you're dealing with May 4 and not April 5. Even when the data is similar, a person has to translate it when there are differences.
The bigger problem, however, can be in dealing with the way that information is presented, and the different terms and values used. Some sites provide data in tables, which are a little easier to read than blocks of text, but not much. Either way, you would still have to sort through a bunch of information that wasn't relevant to you (like notes or yard shift details) in order to find the information that is important: whether your container is available for pickup, and if it's not, why.
This was the task we undertook: not only to provide a single access point for data about all import containers coming into the US and Canada, but also to provide a consistent and normalized view of shipment information. By sorting out all the extraneous data that doesn't matter to you, presenting information in a consistent and easy-to-read format, and highlighting the information that matters most, we make it easier to track and plan for your container arrivals.