We explored the relationship between current practices in knowledge graph data editing and business performance. We’ve highlighted areas for possible improvement and how your business can benefit from enterprise knowledge graph editing. Explore the highlights here by downloading the full ebook now to transform the way you work with your knowledge graphs.
There are lots of factors that will complicate knowledge graph data editing in the coming years.
Like the exponential complexity of data coming from various sources and the shortage in the data science talent pool, among others.
It becomes cheaper and easier to ingest, store, and transform data, but without the right data management strategy, data silos rapidly emerge and can negatively impact innovation initiatives and increase the risk of data incidents.
Taking time to setting up the right processes can seem like a big investment that will take a lot of time when there’s already pressure to be publishing data. But without making time to prioritise data edition, you may end up publishing incorrect data or sacrificing quality over quantity.
In this guide you’ll learn how Hanami can provide you the right tool so your team can be aligned, productive and have confidence that the right data is being published: