Can Big Data Help Keep Us Safe?
In a post 9/11 world, intelligence, defense and public safety agency leaders find themselves struggling to share and leverage the vital information they need to detect threats that prevent crime and terrorist acts. The explicit goal of intelligence and counterterrorism efforts within these agencies is to uncover security threats and take action prior to the execution of malicious and damaging activity – whether it derives from an external enemy or an insider; and whether it be an overt physical attack on citizenry or infrastructure, or a less visible but no less significant act of cyber-threats or espionage. Threats types are increasing, as is their frequency.
The increasing variety, velocity and volume of data – the attributes commonly used to define “big data” – will require that agencies anticipate and pre-empt emerging trends in threatening activity if they are to maintain, let alone increase acceptable levels of response.
The threat and crime prediction and prevention challenges of today are becoming increasingly complex and require a new approach to the management of information. Bad actors are constantly changing their tactics, which means government agencies must be able to continually learn something new. The increasing volume, variety and velocity of this information render traditional analytic approaches insufficient to cull actionable intelligence. To more effectively predict and prevent threats and crime, government organizations must be capable of high-volume management of numerous data types and use advanced real-time analytics designed to transform data into insights.
Data comes from a wide variety of sources, including video, audio, sensors, clickstreams, cell phones, geospatial, imagery, social media, broadcast news and much more.
Government and law enforcement agencies need technology that enables analysis and cross correlation on diverse structured, semi-structured and un-structured big data sets from this large variety of sources. The goal is to provide timely actionable intelligence. Solutions must be cost-effective, efficient and performant. The solutions must also support integration with existing and legacy information management systems, and support efficient deployment models (i.e. cloud deployment).
Technology such as IBM’s big data platform can help acquire, integrate and cleanse the data and then provide the analytical tools necessary to help:
- “Find the dots” – find information that was previously unknown
- “Connect the dots” – find associations between different people or activities
- “Tell me what I don’t know” – uncover heretofore unknown patterns and facts
- “Keep it up to date” – maintain currency of the information
A key feature of the IBM big data platform is that identities and relationships can be identified and resolved from the data, providing critical context for analyses. Large volumes of real-time data – from social networks, to video, imagery and sensors – can be streamed and filtered to automatically provide the analyst with information about detected events, persons, and items of interest in real-time. Also, historical video clips or images stored and classified can be compared to real-time imagery to detect changes or actionable events.
- Analyze vast stores or under-exploited structured and unstructured data from numerous sources
- Incorporate real-time social media, sensor, video, audio and geospatial data into threat analysis
- Improve activity-based intelligence to detect and exploit patterns of life
The common goal of these big data analytics is actionable intelligence, ranging from real-time situational awareness to event detection, anomaly detection, and activity tracking. The bottom line is improved ability to predict and prevent terrorism and crime.
To learn how the IBM big data platform can help address these issues, download "Threat Prediction and Prevention Solution Framework"
U.S. Navy photo by Photographer's Mate 2nd Class Jim Watson