Bringing the context together for intelligent decision making
Corporate data can be a huge strategic asset. However, the sheer volumes of data, different types of data, including structured and unstructured, and the fact that in many cases data exists in silos can make analysing and using the data to make better operational decisions difficult.
Knowledge Graphs resolve these challenges by combining and uncovering connections across silos of information so data can be analysed in a meaningful and more intelligent way.
Siloed data – Data spread across multiple silos, for example in ERP systems and accounting systems, making the efficient collection, aggregation, and analysis of data expensive and time-consuming.
Organisations need to connect their disparate data so it can be analysed in a meaningful and more intelligent way.
Data types - Some data is structured, however, most data is unstructured. Unstructured data cannot be analysed with current databases because most data analytics databases are designed for structured data. O
Organisations need to find new methods to analyse, locate, extract, organise data.
Data quality - It is unlikely that data in an organisation is going to be 100 per cent accurate. Databases can contain the wrong information, house duplicates and contain contradictions. It’s unlikely that data of inferior quality can bring any useful insights or unearth opportunities to precision-demanding business analysis.
Organisations need to ensure that any analysis is based on robust, good quality data.
Knowledge Graphs for enterprise use
For the past decade, Knowledge Graphs have been part of our daily lives. For example, Alexa, Siri or Google Assistant are all types of Knowledge Graph.
For enterprise use, Knowledge Graphs use AI and graph technologies to compile structured and unstructured data, which is then served up visually, showing context and complex relationships between data entities.
Using Knowledge Graphs with Common Data Models - Enabling industry-wide decision-intelligence
Our Audit Knowledge Graph sits on top of our Audit Common Data Model giving auditors everywhere one, universal source of client data (structured and unstructured) that can be interrogated and analysed. The Audit Common Data Model creates not only a base layer of quality data but universal data standards, thus enabling industry-wide decision-intelligence.
Harnessing the power of Knowledge Graphs to solve different business problems
Knowledge Graphs can be used to make better more informed decisions across a variety of use cases. Engine B has created Knowledge Graphs to drive intelligent decision-making in professional services and many other industries.
Our Knowledge Graphs use a combination of Machine Learning and Graph Technology to give organisations a competitive edge, improve efficiencies and to guide operational decisions.