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 answer that’s needed may lie in publicly available data. Yet cutting through the noise and identifying information of real value depends entirely on effective execution.

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

Regulated entities in particular face fines and even criminal prosecution if readily viewable risks aren’t acted upon. These organisations need to get smarter about how they utilise OSD by adopting an intelligence-led approach to investigations. Using freely available information, organisations can gain a broad understanding of the people and companies they work with, reducing their likelihood of inadvertently facilitating crime. 

Methods such as making use of the Intelligence Cycle, paired with advanced OSINT tools, can transform the value of open source data. 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 this information to focus on what’s important allows investigators to contextualise and progress investigations with an informed direction. Limiting data handling is especially crucial, not only to save time but also to ensure ethical investigation best practice. 

You can ensure that your focus will be aligned with processes that procure intelligent outcomes by incorporating the 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: An intelligence collection plan identifies gaps in understanding and determines where best to gather that information from. 
3. Processing: Sorting and categorising collected data helps investigators to analyse it effectively.
4. Analysis: Analysis relies on tools that draw meaning from, or help to interpret, that intelligence.
5. 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 highlighting of risk factors

Component # 2: Leverage all open source 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 contextual insight that can enhance the outcomes of OSINT investigations. 

Strategies to help

OSINT technology that collates, analyses and organises data from disparate sources can support investigators navigating multiple silos, thanks to capabilities such as:

• Simultaneous collection from all data sources
• Intelligent prioritisation of relevant results
• Easy navigation between sources

The ability to store all information within one platform enables cross-matching and visualisations that can reveal new insights to investigators.

Component # 3: Visualise connections within open source data

Publicly available data can provide insights into networks which cannot be found anywhere else. Visualising these connections is essential, both to draw insights across data sets and to present them in a clear and intelligible way. Let’s explore why: 

• Data doesn’t equate to intelligence: In other words, using vast data sets does not automatically mean your investigations will produce actionable outcomes. Without sophisticated visualisation, the data identified will often be nothing more than indigestible information. 
• Visualisation for analysis: 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 investigation processes can you use open source data truly 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, an effective OSINT platform is vital. 

OSINT software is the key to the open-source data lock; it pulls together the required components for actionable outcomes and keeps processes in one place. Visualisation features can include maps, graphs and more — and 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: Automate what you can

The wholly manual handling of open source data limits the effectiveness of open source investigations. Intelligent automation 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 drastically speeds 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 repetitive manual processes across financial institutions, government, and beyond, by automating repetitive tasks reliably at scale. IA software can also augment investigators by speeding up 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, 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. If subjects are made aware that they are being observed, this can limit the effectiveness of an investigation . Anonymity and secure data handling processes are essential to keep investigators’ actions under the radar. To ensure security, investigators must consider:

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

Strategies to help

OSINT software that incorporates security at every step 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 ensure integrity, so that the subject of the investigation is not tipped off. Integrated security saves teams time and money so that they can focus on effective OSINT. 

Component # 6: Establish ethical usage of open source data

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 consderations during an investigation should include: 

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

Strategies to help

Simplifying access to open source data through a reliable OSINT platform lowers the risk of ethical trangressions. 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 accessed. 

Choosing the right tools for open source data use

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 investigators to do this. 

Our platform, Videris, helps investigation teams to collect and analyse relevant data quickly and accurately – generating valuable insights and enabling informed decision-making. With Videris, investigators can ensure effective open source data usage by leveraging a range of core features:

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

Book a demo today, and take your open source investigations to the next level.