Singapore: +65 6384 5411
Sydney: +61 2 9025 3976
Perth: +61 4 0662 5401

Enhancing the Effectiveness of Security Agencies through Big Data

Organisations today are beginning to realise that Big Data not only holds huge potential, it also is very versatile in terms of application. With astute interpretation, it can even help law enforcement in fighting crime. Specifically, by analysing the right kind of data sets, security agencies are able to function more effectively and proactively to correctly direct resources and empower their agents to be a step ahead of the criminals.

What Kind of Data is Being Used?

In the field of crime and security, much of the data may be in reference to past crime statistics in relation to certain areas. This data can then be combined with data on specific known people and information on their whereabouts. Using this kind of information, security agencies can identify hot spots and strategically deploy security presence in those areas. As the data changes so do the hot spots. Taking the data even further, security agencies can look at possible areas of risk. This type of data modelling has led to real results in dealing with crime.

Proactive Law Enforcement through Data

The Australian Crime Commission (ACC) is now using data analytics to engage in proactive law enforcement. Any single data point by itself won’t reveal any insight—the trick is proper aggregation, analysis, and interpretation. The ACC scans the data it gathers for trends and patterns in order to identify emerging criminal threats. Predicting crime is, at its core, about the likelihood of risk and the impact.

Just as Big Data has been used for years by insurance companies to determine risk, data interpreted by security agencies can provide foresight on possible crimes. There’s still, however, the need to connect different kinds of data and provide useful analysis. At that point, law enforcement and security agencies may be able to actually prevent crime more effectively. We are now all well aware of the examples of agencies having various data points but fail to connect them in order to prevent crime.

Optimising Resources

It’s the desire to be more proactive that really has the interest of security agencies. West Midlands Police in the UK looked at seven years of police records across 14 different forces in addition to 1,200 different variables. By analysing this data they found that, 40-percent of the time, police officers weren’t where they were most needed. This realisation allowed them to work more effectively with the resources they had.

There are a total of 43 police forces in the UK, all with different dynamics and needs. As such, it was important to provide the proper context when available for law enforcement. The agency was able to add and analyse other data points such as cash points to more accurately identify where crime might strike next. They’ve also used data analytics to identify where and when women are at a higher risk of domestic abuse by looking at seemingly unrelated information such as football results or temperature changes. Because they had a “playbook” that provided them insights into seeing trends in the proper context, they were able to be more proactive in dealing with crime.

Utilising Big Data and the Internet of Things

One novel approach security agencies can also use is to gather and analyse the data available from the Internet of Things (IoT). When IoT data is used together with other known data sets, it creates a “pre-crime” opportunity and allows for predictive policing.

Police detectives in Chicago are combining historical data such as crime records with real-time IoT data produced by devices such as sensor-based cameras. The BBC recently profiled this approach, which is allowing the Chicago Police Department, in partnership with the University of Chicago, to focus on specific areas where crime may be probable. This process allows for predictive risk and produces data-based alerts which can then be communicated to officers on duty so they can be in the places they are most needed. They also have what could be considered “inside” information that allows them to be better prepared in a city that has no shortage of crime.

Recently, the Data to Decisions (D2D) Cooperative Research Centre (CRC) opened its doors to bring together researchers to better understand Big Data challenges and opportunities for Australia’s national security agencies. The D2D-CRC looked at all elements related to Big Data, including its analysis and how it can be used to strengthen their national security strategies and protocols. Their Integrated Law Enforcement program works toward data integration from law enforcement agencies across the country and aims to identify and react to unusual patterns as well as develop threat models related to specific events, locations, or people.

One of the main goals of this data-driven initiative is the improvement of security agencies in gathering, integrating, and analysing relevant data from various sources to enhance their efforts and effectiveness when it comes to predicting crime, efficient deployment of security forces, and identifying risks related to criminal activity. A recent example is Austrac’s use of big data to assist the Department of Human Services to fight welfare fraud.

Through proper data interpretation, businesses and organisations in different fields—from marketing to law enforcement—can gather the necessary insights to better their services and develop a competitive advantage. Intelligent data management platforms such as Latize Ulysses help companies do just that, allowing them to harness Big Data and turn it into a true resource for continuous improvement towards the future, without the need for data scientists.

Leave a reply