What is fusion analysis?
Fusion analysis is the process of bringing together data from multiple sources to create a more complete view of a person, network, event, or investigation.
In practice, that means combining information that would otherwise sit in separate systems such as case records, financial transactions, communications data, open-source intelligence, watchlists, geospatial data, or internal reports. Once that information is connected, analysts can examine it as a whole rather than as isolated fragments.
Fusion analysis is particularly valuable when the problem is not a lack of information, but the difficulty of seeing how different pieces of information relate to one another. By combining data from multiple sources, organisations can uncover hidden relationships, identify patterns, and make decisions based on a fuller operational picture.
Why fusion analysis matters
Many organisations already have the data they need. The problem is that it is spread across too many systems, making it hard for anyone to quickly see the full picture.
An analyst investigating a person of interest may need to search across case management systems, phone records, financial data, intelligence reports, and open-source material just to understand whether two events are connected. Each source may be useful on its own, but the real value often emerges only when those sources are brought together.
Fusion analysis helps solve that problem by turning fragmented information into connected intelligence. It helps analysts move from isolated records to a clearer understanding of what is happening, who is involved, and where to focus further investigation.
How fusion analysis works
Fusion analysis typically involves four stages.
1. Collecting data from multiple sources
The first step is gathering data from the systems relevant to the problem. These may include structured sources such as case records, customer databases, or transaction data, as well as unstructured sources such as documents, reports, emails, or open-source content.
2. Standardising and preparing the data
Data from different systems rarely matches neatly. Names may be inconsistent, dates may use different formats, and key details may be duplicated or incomplete. Before analysis can begin, the data needs to be cleaned, standardised, and prepared so that records can be compared reliably.
Once prepared, the data is linked into a common model. This often means connecting people, organisations, accounts, devices, locations, and events so analysts can explore how they relate to one another across sources.
4. Analysing the combined picture
With the data connected, analysts can start to ask more useful questions. Which entities appear across multiple investigations? Which accounts, devices, or locations connect otherwise separate cases? Which patterns or anomalies stand out when viewed in context?
This is where fusion analysis becomes valuable. It allows analysts to move beyond searching individual systems and instead investigate the relationships, patterns, and behaviours that emerge across them.
Common applications of fusion analysis
Fusion analysis is used anywhere organisations need to make sense of complex, fragmented information.
Financial crime and fraud
Banks, financial intelligence teams, and investigators use fusion analysis to combine transactions, account data, company records, sanctions information, and external intelligence. This helps identify suspicious behaviour, uncover hidden relationships, and investigate complex fraud or money laundering activity.
Criminal intelligence and investigations
Law enforcement teams use fusion analysis to bring together case data, communications, digital evidence, location data, and intelligence reporting. This can help identify links between incidents, surface suspects or associates, and build a clearer picture of organised criminal activity.
National security and public safety
Security and intelligence teams use fusion analysis to combine information from multiple reporting streams, intelligence sources, and operational systems. This can help identify emerging threats, monitor networks of interest, and support more informed operational decisions.
Cybersecurity
Security teams can use fusion analysis to connect alerts, logs, devices, users, and threat intelligence. This helps analysts understand how an incident developed, whether activity across different systems is related, and where to focus response efforts.
Risk and compliance
Fusion analysis can also support due diligence, sanctions screening, and investigations into hidden ownership structures by connecting companies, directors, shareholders, transactions, and adverse information across multiple datasets.
Fusion analysis in law enforcement and intelligence
Fusion analysis is particularly valuable in intelligence and law enforcement because investigations rarely depend on a single source of information.
A case may involve mobile phone data, financial records, CCTV, witness statements, prior case history, intelligence reports, and open-source material. Each source contains part of the story, but no single source is likely to explain the whole picture.
Fusion analysis helps investigators bring those pieces together. It can support tasks such as:
- Connecting people, devices, vehicles, organisations, and locations across cases
- Identifying overlaps between separate investigations
- Uncovering criminal networks and key intermediaries
- Reconstructing timelines using events from multiple systems
- Prioritising leads based on the strength of connected evidence
- Revealing patterns that would be difficult to identify manually
This is especially useful in high-volume, high-complexity environments where analysts need to assess fragmented information quickly and reliably.
How GraphAware Hume supports fusion analysis
GraphAware Hume supports fusion analysis by helping organisations connect, explore, and analyse information from multiple sources in a single investigative environment.
Using GraphAware Hume, analysts can:
- Bring together data from different systems into a connected graph
- Investigate people, organisations, devices, locations, and events in context
- Use visual analysis to explore relationships across multiple sources
- Combine graph analysis with timeline and geospatial views
- Investigate patterns, anomalies, and shared connections
- Manage investigative workflows without relying on manual cross-referencing across separate systems
By turning fragmented records into connected intelligence, GraphAware Hume helps analysts work across data silos and build a more complete intelligence picture.
FAQs
What is fusion analysis?
Fusion analysis is the process of combining data from multiple sources so it can be analysed together rather than in isolation. It helps organisations uncover relationships, patterns, and insights that would be difficult to identify from a single dataset alone.
What is the purpose of fusion analysis?
The purpose of fusion analysis is to create a more complete and accurate view of a situation, investigation, network, or risk. It helps analysts connect fragmented information, reduce blind spots, and make better-informed decisions.
What is the difference between fusion analysis and network analysis?
Fusion analysis combines data from multiple sources to create a unified picture. Network analysis focuses on analysing the relationships within that connected data, such as identifying central actors, clusters, or hidden links. In practice, network analysis is often one part of a wider fusion analysis workflow.
What is the difference between fusion analysis and predictive analysis?
Fusion analysis involves integrating and analysing information from different sources. Predictive analysis uses statistical or machine learning methods to forecast likely outcomes or future events. Fusion analysis may support predictive work by providing a richer and more complete data foundation.
Where is fusion analysis used?
Fusion analysis is used in law enforcement, intelligence, fraud detection, financial crime investigations, cybersecurity, risk management, and compliance. It is valuable anywhere important information is spread across multiple systems and needs to be understood as a whole.
Why are graphs useful for fusion analysis?
Graphs are useful for fusion analysis because they represent entities and relationships directly. This makes it easier to connect data from different sources, explore how people or events are related, and analyse complex networks that would be difficult to understand in tables or spreadsheets alone.