Regardless of where you stand on the immigration issue, one thing remains the same: Our government agencies need to be able to accurately vet anyone that comes into the country no matter what rules our lawmakers put in place.
While the immigration issue may boil down to some as the ability to differentiate the good people from the bad, in reality, it is about data. With accurate data, immigration and border control agents can quickly, effectively and accurately make determinations about a person’s identity. Without that information, chances for errors increase, potentially resulting in misidentification an individual who has the proper paperwork to enter the country or admitting someone who does not meet the government’s standards.
Big Data vs. Relevant Data
The government, along with the rest of society, is currently going through a data revolution. There is more data available on every subject than ever before, and that includes immigration. The challenge is becoming not how to collect information, but how to sort through all the information that exists.
The volume, variety and velocity of data will keep growing, increasing the gap between big data and relevant data. For government organizations without analytical capabilities, this will create an information overload that threatens to increase time to detection and action, and making it easier for “bad guys” to exploit the system. Border protection agencies need all the information they can get, but also need tools and systems to get to the relevant data. They need the best information to guide their decision making in a short period of time. So how do agencies accomplish this task?
The Power of Advanced Analytics
Luckily for government agencies, improving data management will not require a database overhaul or a massive central data warehouse. Instead, agencies need a data integration layer that can connect all of these disparate databases residing across the agency, department or other federal, state and local government entities. A single layer can link data that is stored across platforms—from legacy systems to Hadoop—providing access and analytical functionality from a centralized location. Envision what this could mean for a border agent: a comprehensive set of data that is being pulled from multiple data sources into a single dashboard in real time.
The next issue for agencies is data quality and the need to ensure that the data being analyzed is cleansed—automatically correcting nonstandard or duplicate records and unknown data types. This process includes entity resolution, which more accurately identifies individuals across data sources. Fraudsters often provide inaccurate, incomplete or inconsistent information that prevent records from matching across systems. But with entity resolution, an agency would know that the James Williams in one database is the same person as the J.P. Williams in another.
Finally, to successfully analyze vast amounts of granular data, this data infrastructure must be able to:
- Process large volumes of data quickly.
- Handle the huge variety of data involved, including tables, documents, email, web streams, videos and more.
- Manage the velocity of data, which is increasing rapidly.
Analytics is crucial to keep up with the growing sophistication and complexity of fraudsters and terrorists looking to enter the country. The right data management, integration and quality infrastructure must be supported by a strong business analytics foundation.
The worst case scenario for border agents is letting a person into the country who should not be allowed in and then that person doing some form of harm. Traditional systems are no longer capable of keeping up with the amount of data that is available. People that want to get into the country can forge records, manipulate data and introduce confusion into the process.
Analytics can take all available information to look for inconsistencies. Border agents can use this information to look for red flags and make quick determinations if a person should be allowed to enter the country or if their documentation needs further review. Analytics helps make this process more accurate and efficient for everybody involved.
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