Posted by Katie Duval on September 20, 2017

With daily headlines about terrorism attacks, government security and intelligence agencies are under constant pressure to analyze data quickly to uncover threats before an attack occurs. Yet in a world overrun with information, sorting through, preparing and analyzing intelligence to detect these threats can be nearly impossible without the proper data preparation and analysis tools.

The problem is that the rising amount of data — especially that generated by real-time social media and messaging — is collected from a variety of sources and in multiple formats. And while massive amounts of intelligence can help analysis, it can also hinder it when data isn’t cleaned and organized into a single workspace.

Intelligence analysts consistently face three data challenges that impede this crucial analysis and, by extension, communities’ safety:

1. Data overload. As computer-generated information continues to grow, the amount of data collected by organizations also increases. Public-sector data analysts report that they spend up to half of their day gathering and preparing data, leaving little time to glean actionable insights from it. As a result, there is a significant chance analysts may miss an opportunity to connect the dots on key pieces of homeland security intelligence.

2. Dark data. Dark data, or data that is too difficult to use for daily threat analysis, also poses a challenge. According to The Forrester Wave, most organizations include only 12 percent of all available data in their analytics, leaving 88 percent untouched. Failing to use all available data from investigative materials, including financial records, travel records, social media pages and tips from the community, can result in intelligence agents overlooking credible threats.

3. Disparate data. Data in disparate formats also hinders the IC’s ability to perceive and tackle threats. Data from sensor systems, financial reports and ad hoc tips from the public can be delivered as text or log files, spreadsheets, text, PDF or HTML files.

Combining all these datasets requires massive amounts of manual effort, especially if the data must be rekeyed. Many analysts can’t afford to invest this much time and may simply ignore difficult data. This is dangerous, and can lead to intelligence failures.

Removing data barriers with collaboration and preparation

To improve their data analysis and uncover credible threats before the next event is splashed on a breaking news banner, government intelligence analysts need better data access and quicker preparation.

Self-service analytics tools — including data preparation platforms — are among the simple solutions that can address the IC’s crucial data challenges. A self-service data preparation solution allows analysts to easily collect, access, parse, blend and join data from a wide variety of formats. By creating structured workspaces and unlocking the data hidden away in PDFs, spreadsheets, web pages and other formats, analysts can be sure they are using all available data for insight and analysis. Additionally, these tools eliminate the need to manually rekey information, ultimately decreasing human-error and data-quality issues.

With the creation of these new, cleaner data sets, analysts can more easily share information and collaborate with colleagues and law enforcement. Instead of having intelligence analysts waste precious time preparing their data for analysis, self-service analytic platforms speed up threat detection and break down data silos between intelligence departments.

Intelligence analysts who embrace self-service data preparation can address challenges associated with information overload, the use of dark data and disparate data formats. By removing data preparation roadblocks, intelligence analysts will spend less time fighting data and more time analyzing information, sharing intelligence, detecting threats and disrupting plots. Adding these platforms onto existing analytics software is a simple yet critical step to detecting terrorism and protecting communities.

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