Internodal has been built as a data analytics tool to support the enterprise decision maker


Internodal has been built as a data analytics tool to support the enterprise decision maker through connecting and visualising the relatedness of the existing datasets. Internodal makes it easy to collate and connect your datasets irrespective of the database that is used, giving you a completely new view of the relationships between current and legacy datasets. It helps you to understand and obtain insights from data that is generated and used by the business. Typical sectors that use Internodal consist of: debt collection, security, customer relationship management and distributed asset management.

The problem

Most enterprises have their data spread across many relational databases with large systems built on top of those databases, making it difficult to see how data from those separate databases is connected. Enterprises are collecting more and more data, but the connections between data that would enable better decision making, remains hidden.


This current approach to data management means that data is not being used to it full potential because it is housed in multiple disparate systems, so centralising these data sources would be beneficial. By doing this we will start to see a bigger picture enabling us to make more informed decisions with more relevant and compelling digital content.

The solution

Internodal offers a bigger and more comprehensive view of each data source and their connections to each other. Surfacing this connectedness adds more value to the raw data as it enables an enterprise to leverage their data and capitalize on opportunities presented by the now evident connections or relations in data.

With Internodal you can unlock value from existing data by translating the normal forms of your relational database into a graph model without having to rewrite your transactional system but instead mapping your data to a graph in batch time (once per day, every hours, or even every minute). In doing so, you can retain all the details of your data while enhancing it with connectedness visualisations.

The patterns that we have identified in working with relational data and trying to connect it to other relational data (i.e. who’s connected to who/what’s connected to what) are interesting in their own right, and the cutting edge process we have discovered for facilitating this will be useful for others.


Rather than relying on complex relational databases (as conventional business databases are called) to store and analyse data, graph databases allow users to store, search and query complex interconnected content from assessing product stock levels to calculating the quickest routes possible. A graph database does this by applying graph algorithms to the data and leveraging relationships and connections between physical assets (people, objects), their locations and subjects using complex patterns to match and make sense of the information. This means you can ask the database complex questions and it will give you the answers in real-time. As a result, the tool drastically improves the management tracking of deliveries.

Some of the practical applications of Internodal could be on:

  • improving logistics management in the health sector to prevent problems like the current drug shortage.
  • facilitating better environmental control processes to manage and monitor issues like alien invasion species.
  • improving organisation’s partner networks, enhancing customer relationship management (CRM) and enabling social marketing by using customer information, business connections and social networks intelligently.
  • graphs databases are great for social networks, routing, path finding, fraud detection, recommendations, GIS, master data management, complex permission management and infrastructure modelling, amongst other things.