In a human brain, each neuron is connected to other neurons by up to 40,000 individual connections (called synapses). Multiplying 100 billion neurons times 40,000 synapses between two neurons is equivalent to the brain having more connections in it than there are stars in the universe. Scientists have argued that it is these connections that dictate intelligence and provide incredible recall abilities to the brain. Human intelligence enhances as we add more relationships i.e. more synapses between neurons, and it is these relationships that differentiate the super intelligent from the rest.
Relating data is a very powerful thing, the human brain’s incredible recall abilities are directly related to its ability to “relate” data. Similarly, as enterprises accumulate exponentially increasing amounts of data, both internal and external to the organisation, how well they can exploit the relationships in that data is what will drive the next wave of competitive advantage. Enterprise digital transformation in a connected world will largely be driven by how well companies can harness exploding data and its inherent relationships to -expand into newer markets, connect with customers, make better investment decisions, minimize enterprise risks or increase employee productivity.
World has come a long way since relational databases, and a large variety of NoSQL databases have now been developed, however only graph databases provide the data structure required to unlock value from data relationships.
Use of graph databases has created some of the most successful companies in the world, most famous of-course being Google. Per Wikipedia, Google “uses a graph database to provide structured and detailed information about the topic in addition to a list of links to other sites.” This graph database, has allowed Google to focus its search on things – or concepts – and understand exactly what you’re looking for based on context.
With data relationships as first class citizens, graph databases form the underlying data structure for knowledge graph. Knowledge graphs are a means of organizing and using data, which allows enterprises to better tap into the connections in their datasets. It is the only data structure that maintains the context and provides real-time processing capabilities that are imperative for enterprise decision making.
- Context: Because of their structure, knowledge graphs can capture facts related to markets, companies, industries, people, processes, applications and things, and the relationships among them. They also capture evidence that can be used to attribute the strengths of these relationships. This is where the context is derived from. Other data stores, will require a lot of superfluous information to do the same thing, but even then wouldn’t be able to provide the same impact that knowledge graphs provide. Additionally with knowledge graphs, with every new data point you enrich the entire data ecosystem, because it’s connected to everything else. The more data, the more context. And this contextual value grows exponentially as the knowledge graph senses new data from a variety of sources internal or external to organisation.
Real-time Analytics: While data connections, context and assimilation of information has been the need of enterprises for some time now, competitive advantage can only be driven if insights extraction and processing is done in real-time. Knowledge graphs ensure zero latency and real-time performance, regardless of the number or depth of relationships
As companies are going through a digital transformation driven largely by technologies such as Cloud, IoT, AI, Big Data etc. knowledge graphs need to be thought of as something that is foundational to that transformation. With their unique capability to retain context and exploit data relationships in real-time, knowledge graphs are most suited to drive the next wave of disruption in enterprise decision making.
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