6 Things To Consider When Using Open Source Data

The rapid growth of open source data (OSD) is both a strength and the largest challenge facing organisations that hope to make use of this freely available information. The data might be there, but cutting through the noise and identifying information of real value depends entirely on effective execution.

Regulated entities across the financial sector and beyond face potential fines and even criminal prosecution if readily viewable risks aren’t acted upon. And so these organisations need to get smarter about how they utilise OSD. Intelligence-led investigation techniques are required in order to contextualise relevant information in a way that can help investigators connect the dots and drive better outcomes. 

Turning open source data into open source intelligence (What is OSINT?) is not a new concept in military and government investigatory practices. However, the value of OSINT is now also becoming apparent in the private sector. 

Methods such as making use of the Intelligence Cycle, paired with advanced OSINT tools, can transform the value of open source data. Adopting an intelligence-led approach is the critical factor driving success or failure. Here, we’re going to look at what exactly effective open source data usage looks like, and how it can improve investigation outcomes. 

Component # 1: Focus on what’s important

The sheer amount of open source information available can complicate compliance processes as investigators sort through mounds of directionless data. Filtering these information influxes to focus on what’s important allows investigators to contextualise and progress investigations with an informed direction. The ability to limit data handling is especially crucial, not only saving time but also ensuring ethical investigation best practice. 

You can ensure that your focus will be aligned with processes that procure intelligent outcomes by incorporating the four stages of the Intelligence Cycle into your investigation strategy — 

1. Direction: Direction defines the problem and considers why intelligence is necessary, who needs it, and how that information should be sourced.
2. Collection, processing, and exploitation: An intelligence collection plan identifies gaps in understanding and determines where best to gather that information from. 
3. Analysis: Analysis relies on tools that draw meaning from, or help to interpret, that intelligence.
4. Dissemination: Dissemination refers to the delivery and presentation of those findings.

Strategies to help

OSINT is a way of directing open source data towards investigation requirements. The vast amounts of available data is a complicating factor, sometimes causing organisations to deprioritise OSINT as a form of intelligence that they consider too broad to be effective. OSINT software is the secret to simplifying data collection and analysis, through the implementation of — 

• Targeted investigations
• Contextualised analysis 
• Categorisation by theme
• Automatic highlights on risk factors

Component # 2: Leverage all data silos

The rich, valuable data that is found in open sources is often siloed, creating challenges for investigators who are forced to access multiple different sources and platforms to access the information they need. Cross-referencing across silos provides invaluable connection-creating oversight that can enhance the outcomes of OSINT investigations. 

Strategies to help

By providing technology to collate, analyse and organise data from disparate sources, advanced OSINT software is the secret to success when navigating multiple silos, thanks to capabilities such as — 

• Intelligent prioritisation
• Automatic categorisation
• Highlighted risk terms
• Easy navigation

The ability to store all information within one easy-access platform also plays its part. Accessing silos in tandem enables cross-matching and visualisations that pull all relevant information together, under one accessible roof.

Component # 3: Visualise connection

In order for publicly available data to be deemed truly useful and intelligent, your investigations must prioritise visualisation and connection. This can be done by adhering to the Intelligence Cycle to draw intelligent insights across data sets and present them in a clear and intelligible way. Let’s explore why: 

• Data doesn’t equate to intelligence: In other words, your investigations will not necessarily produce actionable outcomes from vast data sets. Without sophisticated visualisation, the data you identify will often be nothing more than indigestible information. 
• Visualisation for analysis: Capacity for data visualisation is the starting point for any open source investigation. By visualising data, you can identify which connections are important, stratify information into groupings and networks, and find threats more easily. 
• Visual reporting: The insights you’ve derived from publicly available data, no matter how intelligent, will still be useless without a clear way to present your findings. 

Only when visualisation is embedded into your investigatory processes can you effectively use open-source data. However, it is one thing to recognise this need for visual connections, it is another to use it effectively. 

Strategies to help

Open-source data may be a key ingredient of valuable investigations, but OSINT is the method that drives results. Insights differentiate the two — deploying the Intelligence Cycle in tandem with the right tools can bring those insights within easy reach. In order to truly realise valuable outcomes from open source data then, your choice of platform is paramount. 

OSINT software is the key to the open-source data lock; it pulls together the required components for intelligent outcomes and keeps processes in one place for the visualisation of an entire OSINT investigation cycle. Visualisation features can include maps, graphs, groupings and more — all of which make it possible for both analysts and senior decision makers to connect the dots more easily.

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Collect, analyse and visualise open source data faster and more accurately with Videris.

Book your free demo with us to see how having all your investigation tools in one effective platform can benefit your organisation’s AML investigations.

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Component # 4: Intelligently automate what you can

The wholly manual handling of open source data limits the effectiveness of open source investigations. Intelligent automation (IA) focuses on automating less valuable, manual and time-consuming processes — such as collecting and categorising data from multiple sources. IA makes it possible to process thousands of search results across a range of sources, categorising, filtering and flagging risks in a short amount of time. This stands to drastically speed up investigations, ensuring that no relevant stone remains unturned and that available insights are always recognised, understood, and acted upon.

Strategies to help

IA tools transform the efficiency of organisational functions across finance, government, and beyond, by automating repetitive tasks reliably at scale. IA software can also augment investigator decisions throughout various investigative activities — 

• Corporate and social network mapping
• Identification of connections and links across datasets
• Referencing and sourcing of collected data

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Crucial tip: IA can drive data handling but human input is still required for ethical, informed decisions. IA tools augment rather than replace human decision-making by directing analysts to relevant information and connections, and freeing them up to focus on more valuable tasks.

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Component # 5: Remain secure and anonymous

Poor handling of open source data can both prevent the benefits of OSINT from being realised and open investigations to new risks. This can limit the effectiveness of any given investigation if subjects are made aware that they are being observed. Anonymity and secure data handling processes are essential to keep investigators’ actions under the radar. Key components that make this possible include — 

1. Conducting investigations anonymously
2. Reporting/recording findings in one, centralised, place

Strategies to help

OSINT software that incorporates security into every process is the best way to secure all aspects of an investigation effectively. Tools that centralise open source data are essential for limiting the risks of sensitive data exchanges and potential breaches. The ability to anonymise investigations and searches also keeps integrity in check, ensuring that the subject of the investigation is not tipped off. Integrated security saves teams time and money so that they can focus on effective threat-free open source usage. 

Component # 6: Establish ethical usage

The readily accessible nature of open source data doesn’t mean that you should gather everything you can. Companies that hoard data unnecessarily still face significant ethical questions, especially as government authorities double down on data handling. Indiscriminate data stockpiles pose significant questions with regards to data protection. To avoid these threats, ethical practices should play a key part in every stage of an investigation, including — 

• Targeted and proportionate data collection
• Legitimate use cases 
• Decision-making based on human judgement

Strategies to help

Simplifying the access to and implementation of open source data through a reliable OSINT platform lowers the risk of ethical errors. Sophisticated investigation tools can make it possible to target only the most relevant data, whilst also providing tools that allow auditors and regulators to understand why that specific data was utilised. 

Choosing the right tools

The scope and sensitivity of open source data means that it requires careful handling to produce actionable insights. At Blackdot we’ve pioneered technology that allows you to do just that. 

Our software, Videris, allows dedicated investigation teams to collect and analyse relevant data quickly and accurately – generating valuable insights and enabling informed decision making. Videris works to ensure effective open source data usage by leveraging a range of core features — 

• Multi-source searches
• Keyword targeting
• Intelligent automation 
• Data visualisation
• Cross-matching
• Secure and anonymous browsing

Effective open source investigations don’t get simpler than that. Book a demo today, and take your open source investigations to the next level.