To date, much of this information has been underutilized. To be usable for customs investigations and risk assessment, the data needs to be collected and fused together in a single platform, with advanced analytics and machine learning models needed to parse through it and extract insights. With dozens of siloed data sources, red flags are easy to miss. When viewed holistically, those same data points can indicate a pattern of suspicious activity. Unfortunately, lacking the right systems, few custom officials can see the holistic view of an individual package, passenger, broker, vessel, or other entity of interest. As a result, they may miss the red flags that a shipment should be inspected or that an import/export broker or shipper should be investigated. To improve their operations, 44% of customs authorities report they are currently using big data analytics, AI, or machine learning, and another 33% intend to start in the near future 4 . While many customs organizations are already investing in AI and analytical tools, as is seen in the survey, these solutions often are far too limited and fall short. Many existing solutions struggle to handle the volume and velocity of data available, are unable to process both structured and unstructured data or to utilize data sources that are siloed in different source systems. Furthermore, there often remains a ‘decision gap’ - a gap between the insights derived from the data and the resulting actions that should be taken. Analytics tools and systems often focus on building models and making projections, but the insights they generate aren't actionable. Stage of adoption of big data analytics No plans Plans to use big data analytics Using big data analytics 33% 23% 44% Source: 2021 WCO Annual Consolidated Survey 7 Data Challenges 3