Ugly data requires:
Beautiful technology

monsterDB is a distributed collection database with machine learning, fuzzy matching, relationship management and geosptial capabilties. 

Machine Learning

Fuzzy matching is at the core of what we do, that is why we built it into the core of the database. But we also built into monster the Machine Learning libraries from WEKA that enables you to build classification trees and regression inside the database without the need to use tools like R


Streaming enables you to start with a collection or subset of a collection of documents and process the documents through in an efficient way performing various types of data enhancement along the process, these enhancements can vary, from filtering using standard selection criteria, geographical selection or fuzzy searching.

Clustering and Coercing

Clustering finds similarities within any source of data using fuzzy rules regardless of whether the information is stored in a collection or is simply being processed by a stream. You can then coerce the clusters of data into conceptual objects that represent a golden collection of the underlying records.

Geospatial Knowledge

We support the ability to index and search your data using standard geospatial techniques, this means that you can geocode your objects in a collection and then search them using polygons (shapes) or points on a geospatial index. To find any object in the collection within an area takes very little effort.

Reduced Complexity

Data has become a science, but we think that this is not an excuse for over-complicating it. We created the worlds best database (well, we think so) and it just knows how to do things that you would expect from any database, like "how do I match people" or "look for trends in this data"


MonsterDB supports named linkages and relationships on any object in the collection, what this means is that you can add a relationship between any number of pairs of objects in a collection without having to implement it yourself. Navigating those relationships can be done via the command line or through the API

Master Data Applications

Matching Legal Entity Data to Support a Master Data Management Solution for FATCA

Customer Relationship Management, ensuring you have a single golden view of your customer base.

Product Master- creating a globally consistent view of products, brands and suppliers.

Industry Applications

Financial Services

Managing Legal Entity and Customer data to ensure regulatory compliance with tougher regulations around the world in this sector. Managing and assessing data quality to ensure compliance with data governance rules.


Using the fuzzy matching capability of the engine, combined with the geospatial, relationship handling and machine learning aspects of the tool you can use monsterDB to solve questions like: “Bob Someone lives near Northampton and may have a blue car”


Building customer loyalty through a more integrated platform allowing retail companies to better understand their customers, product they buy and the distribution channels they use.

Oil and Gas

Building a relationship with suppliers is key to a smooth operation in up-mid or down stream, having a complete handle on the materials required and their availability and alternative sources is key to success.


General Data Protection regulation has been in action for nearly a year now and although it has dropped off our radars, many organisation need to assess the levels of compliance by understanding where personal data is stored and referenced across the organisation.


Motor, Household and Commercial insurance providers need to be able to assess the levels of risk to them at a moments notice and through the integrating nature of an MDM solution they can start to get a better understanding of their clients, risk points and suppliers.