Facilitating Trade:
Improving Customs Risk Management Systems
In the OIC Member States
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journey from existing entity-relationship (ER) model (ex. traders, means of transport, goods,
etc.).
CRM and Law Enforcement Data Warehouse - the separation of operational data from the
analysis data is the most fundamental data-warehousing concept. Not only is the data stored in
a structured manner outside the operational/production system, but analysis services also
allocate considerable resources to build data warehouses at the same time that the operating
applications are deployed.
It is necessary to separate the operational data from analysis data (DW) since the data will be
not significantly changed by the transfer and transmission of the data into the warehousing
system. In the analysis and design phase, the building of a Data Warehouse is performed via ETL
from entity-relationship (ER) model. Advances in technology support very sophisticated online
analysis including multi-dimensional analysis.
The LE Data Warehouse shall be entity-oriented meaning that the data is organized into
dimensions with similar data linked together. The time is an important dimension; data are
recorded and tracked within timelines so that change of modus operandi and patterns in
offenses can be determined over time. Another important dimension is the type of crime/
offenses by linking others elements related to entities, places, commodities, etc.
 Information about the type of event, offense, crime, route, modus operandi, method of
concealment, etc.;
 The time and place of the event;
 The factual consequences and potential consequences;
 The measures proposed – Risk Indicators and collected intelligence;
 The measures that were undertaken and other activities – feedback;
 Request for further measures for coordination and other measures and activities;
 Other relevant information.
Since the data needs to be brought together from more than one source, this integration can be
done at a place independent of the source data layers/applications. The primary reason for
combining data from multiple source applications is the ability to cross-reference data from
these data layers.
Data Layers Integration;
The underpinning for integration is that existing data layers continue to be operational but that
there is an integration layer on the information level that allows multiple users to share data
and processes, and enables a single point for analysis.
 The logical reasons to push for integration is to transform Customs into an integration
point for Integrated Risk Management;
 Offering better alignment of business and IT with high reduction in costs;
 To manage complexity better and aiding overall decision-making;
 To provide for cross-border information exchange from a single secure technical
infrastructure.