Why Knowledge Graphs Matter
Posted: Tue Feb 11, 2025 6:24 am
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.
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.