Data with Relationships Yields Insights Before Analytics
Data’s utility is not rooted in amassing the largest quantities of data; it’s predicated on understanding how data relates–both to itself and to business objectives–in a timeframe that fits the speed of business.
Relationships between data can be formed by linking data based on meaningful statements within a Semantic Graph or triplestore database. These relationships provide a form of machine intelligence that is essential to expanding the understanding of how data relates to each other. Triplestores use these statements as the basis for providing further inferences about the way that data interrelates. For example, the EMR of a hospital would have data about a patient that will be expressed in triples like:
Combining the EMR data and publicly available medical drug database would have triples such as:
– Chlorpropamide has the brand name Insulase
Based on these data relationships, the reasoning in the triplestores will instantly conclude that Patient X is at risk of a drug interaction.
Such an example illustrates the instantaneous usefulness of relationships between data. Creating relationships between data enables “intelligent inferencing”: –in other words-data learning from data. Such inferencing is invaluable when leveraged at scale and accounting for the numerous subtleties existent between big data.
Leveraging data relationships quickly, cost-effectively, and in a user-friendly format across industries is the surest way to entrench data culture and maximize data’s worth to the enterprise. Whether identifying time-sensitive investment opportunities in finance, treatment possibilities in healthcare, or apposite clinical trials in life sciences, extracting value unmistakably hinges on exploiting relationships between data.
The most effectual means of determining relationships between data elements, particularly when involving unstructured and semi-structured data, is to allow the data to indicate those relationships themselves.
This capacity eludes traditional relational methods, in which users have to construct tables to define all the relationships between data they can think of prior to analyzing them. If they want to ask a question that was not previously modeled with the requisite schema or if sources or requirements change, end users have to wait on IT for reconfiguration while transitory business opportunities pass.
But in a graph-aware environment, the data determines the relationships between one another. Semantic graphs effectively link all data in a uniform fashion with an emphasis on the edges–the links between nodes, a critical determinant for ascertaining relationships that transcends the capabilities of non-semantic property graphs focused solely on the nodes themselves.
Below is an ecommerce example where three electronic retailers (eBay, Amazon, Google) sell the same anti-wrinkle cream with slightly different pictures, quantities and description. Machine learning was built into the graph database to compute similarity between products. The purple boxes show the similarity between products, the grey boxes show the prices for these products. Note how the product might be very much the same but the prices differ widely!
This approach aligns with basic human intuition for recognizing relationships outside of external parameters for traditional schema and transcends data sources, structures and types. It endows a degree of flexibility and innate relationship awareness at the pace of modern business, vastly exceeding the capabilities of other technologies.
