You can start small with a single project dedicated to product and engineering lifecycle management for example. The possible use cases for your knowledge graph are practically limitless. Determine the use cases for your knowledge graph You could be a data scientist, a machine-learning specialist, a data engineer, or anyone else who works with data or knowledge bases. Whatever your scenario, and whoever you are, start here. You may be an evangelist for “change within your organization,” or perhaps management asked you to investigate this “new trend.” Or maybe you’re already sold on the knowledge graph approach and you want to follow the best methodology possible. An Enterprise Knowledge Graph is a linchpin to how businesses can maintain (or begin) a data-centric strategy while operating in a more complex infrastructure. As an Enterprise Knowledge Graph platform, it connects and enables all your data and data systems-governance, storage, visualizations, and security. Data management systems haven’t kept pace. Relational databases and even graph databases, which were built for storage, can’t achieve connection and context alone. They work best with consistency, and real-world data is anything but. ETL processes rely heavily on other systems and manpower to run queries. Or, the catalog is overly broad and superficial. There are low adoption rates when it comes to populating them with accurate metadata.
Data catalogs are expensive to purchase and lack the context needed to create knowledge.
So, what choices do you have? A data catalog? ETL? Other forms of data movement? A graph database? They all fall short of creating a well-connected enterprise. The primary goal of knowledge workers is to derive actionable knowledge from mere data to make better decisions-the true hallmark of a connected enterprise. In other words, what matters more than data is business meaning. To be helpful it must be related to the business context of the enterprise that owns it. Raw, uninterpreted data in a system somewhere isn’t very helpful. But organizations struggle to leverage that data for a competitive advantage. Do you really need a knowledge graph?ĭata rules the world. Increase your number of users, as needed. Scale all those use cases that have been inspired by data science. Practical steps for building knowledge graphs: powerful tools for linked data, data integration, and data management.
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