This is where knowledge graph architecture comes in. Overlaid on the data mesh-data fabric hybrid, it can provide an ontological and semantic map of your data, shaping it to useful and profit-driving ends.
For an analogy, let’s turn to the pharmaceutical industry. Drug companies must be able to generate quick, comprehensive answers to any question they might have about a drug. The stakes are high, given the taiwan whatsapp number data potential for patient harm and subsequent regulatory action or legal trouble. But current data paradigms make generating high-quality, actionable information about drugs a challenge.
Why? As it happens, it’s all in the name – or, more precisely, names. The lifecycle of a drug that makes it to market is long with countless distinct stages: research, trials, FDA certification, manufacturing, commercialization, post-approval monitoring, etc. At each of these stages, the drug in question might be filed under a different code name, or project number, or file number, or brand name. For instance, the drug might have five different names in five different companies, and 10 different names within a company as it is commercialized in different markets. Drugmakers need a 360-degree view of this data to make good decisions and avoid liability, but when the data they need to properly answer a query is (for instance) a layer above in their model, they are forced to operate off impartial and potentially misleading information.