Dossier

Data analysis for effective border management – the Canadian experience

By Charles Slowey, Director General, Global Border Management and Data Analytics, Canada Border Services Agency

Effective border management requires the identification of people and goods, and the collection and analysis of relevant information at the earliest possible point in the travel and trade continuum. The “business” of modern border management organizations has evolved, and is now driven by the active use of advanced data.

Customs organizations collect and hold vast amounts of data on travellers and goods. As a result, we must embrace an organizational culture that is equally driven by sound principles of information management so that we may truly take advantage of the large amounts of data in our care. By adopting progressive approaches, such as data analytics, to collect and successfully exploit data to drive decision-making, we can strengthen our capacity to protect our citizenry, improve border services, and generate revenue for our governments.

Turning our “raw data” into information enables evidence-based decision-making, and allows border management organizations to invest resources in a way that supports high priority services. In the case of Customs organizations, this can include advanced risk assessment techniques, better resource utilization, and more complete reporting on overall performance to the public.

In order to take advantage of the information under its stewardship, the Canada Border Services Agency (CBSA) has developed a strategy for data analytics, including establishing a centralized governance structure, to drive investments in three key areas: data governance; business intelligence; and advanced analytics. The framework is designed to allow the Agency to derive increased value from its data. At the core of this strategy is the recognition that the CBSA respects the privacy and security of the data in its care, and that the data enables effective border management.

What is data analytics?

The term “data analytics” refers to the use of information technology to harness statistics, algorithms, and other tools of mathematics to improve decision-making. It includes traditional analytics that is often referred to as business intelligence (e.g. “what happened?”) and predictive analytics (e.g. “what will or could happen?”). The CBSA recognized that it could not fulfil its core mandate without data to drive our priority‐setting, decision-making, performance measurement, budget planning, and operations. Key to this was a shift in culture.

Our traditional methods saw us accepting information through “stove pipes” that were in turn analysed in siloes aligned to our various business lines. However, as today’s world is increasingly interconnected, it became clear that we needed to link our diverse data sets and take an enterprise approach to information management. By connecting our data holdings, we could position ourselves to better contribute to global security, and facilitate the free flow of persons and goods. An internal culture shift is underway at the CBSA to see data as a corporate resource that can be used across all of the Agency’s business lines.

Toward a data-driven decision-making process

By recognizing that we are a “data-rich” organization, with significant data systems, the CBSA’s Data Analytics Strategy became the guiding framework to increase the value of these data holdings. Our strategy focuses on three pillars.

The first pillar, Data Governance, includes establishing a Data Governance Centre, launching data stewardship, and implementing an enterprise business data model with data sets consistent with the WCO Data Model standards. To date, the CBSA has improved data policy coherence and strategic direction through improved communication among all branches of the CBSA. We have focused our dialogue to ensure that we are discussing data integrity, data management, and how best to automate manual data entry tools. In short, we have brought the right people together to ensure that we are having conversations on how to use data the “right way.”

The second pillar, Business Intelligence, involves building an integrated data warehouse that draws on key data from multiple sources, including operational systems, as well as financial and human resources data. Early on, the Agency was able to complete high-level business requirements for an integrated overview of the CBSA’s operational, financial and human resources data. We also developed a visualization pilot to improve situational awareness of what data was available. Going forward, we hope to use data to effectively inform decision-making on where to deploy officers and technology in response to real-time shifts in operational needs.

The third pillar, Advanced Analytics, includes expanding operational analytics capacity, exploring the potential of predictive analytics, visualization, and other advanced tools. Under this pillar, the CBSA launched an eManifest project, in cooperation with the WCO, to set in place a standard data model for use across jurisdictions. This enabled us to apply advanced risk assessment to pre-arrival commercial information. We have also used predictive analytics and models to determine patterns and anomalies in existing datasets. This included applying data analysis technologies to examine patterns of trade fraud, targeting models, and to improve resource allocation.

Building blocks of data analytics

Canada’s experience in placing data-driven analysis at the forefront of decision-making required a comprehensive shift in thinking towards using data as a valuable corporate asset to further effective border management.

While there may not be a standard “one-size-fits-all” blueprint for all countries to adopt when looking to take advantage of the information under their management, we believe there are key building blocks to the successful development and implementation of a data analytics strategy.

Using these building blocks, any Customs administration can develop a tailored plan for data analytics, which will allow it to derive value from the vast amounts of data that we as border management organizations collect. From the CBSA’s experience, these include:

  • appointing an executive champion who understands the value of data in decision-making, and who has the authority to make it accessible;
  • establishing formal data governance to ensure relevant, accurate, and timely data is available, thereby increasing confidence in data;
  • developing teams that understand data and technology;
  • always respecting privacy and sensitive information.

The concept of “privacy by design” is essential to the development of policies that hope to benefit from the use of data analytics. In Canada, as we build capabilities from the ground up, we always consider privacy implications and legislative requirements.

Expected outcomes

By integrating data analytics into our regular operations, we expect to see marked effectiveness and efficiency gains in a number of areas. We expect to be more capable of predicting traffic volumes in real time to help inform the deployment of human and technology resources. In addition, we are improving the identification of non-compliance, and, therefore, hope to generate increased revenue from trade verification. We are also refining risk scenarios to improve targeting operations.

Finally, by providing more comprehensive and accurate performance reporting of the Agency’s programmes, we are positioning ourselves to deliver better results for Canadians. We cannot expect to modernize our business overnight, but we can certainly take the right steps today, so that we can be effective partners in the global economy of tomorrow. The CBSA views this culture shift, towards increased use of analytics grounded in a sound information management policy, as foundational to our overall modernization efforts, including a more open government.

More information
CBSA Data Analytics inbox
CBSA.Data_Analytics-Analytique_Donnees.ASFC@cbsa-asfc.gc.ca