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Knowledge Graphs

Knowledge Graphs illustration

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.

Data challenges

Siloed data – Data spread across multiple silos, for example in ERP systems and accounting systems, makes 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 data analytics databases because most are designed for structured data.

Organisations need to find new methods to analyse, locate, extract, and organise data.

Data quality - It's 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 doubtful 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 Graphs.

For enterprise use, Knowledge Graphs use AI and graph technologies to compile structured and unstructured data, which is then served up visually, providing context and highlighting 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

Beyond the scope of traditional data analytics

Knowledge Graphs can be used to make better more informed decisions across a wide variety of use cases. Engine B has developed domain-specific Knowledge Graphs for:

Fraud detection

Use our Knowledge Graphs to uncover and detect fraud quickly. Unlock hidden risks in data to make faster decisions.

Anomaly detection

Use our Knowledge Graphs to discover anomalies in data. Make sense of huge data volumes and surface the most valuable links and relationships to understand the context created.

Legal investigations

Use our Knowledge Graphs for property/real-estate analysis and combine them with other available data sources. This empowers professional services firms and in-house teams to seamlessly interact with non-legal services where necessary.

Tax investigations

Use our Knowledge Graphs to analyse and interrogate suppliers, leases or contracts, and transfer pricing rules. For example, understand which suppliers are based in a certain country due to a change in import duty or analyse what lease contracts don’t have a termination clause.

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