We live in an age of data. Companies of all sizes are generating more data than ever, and figuring out how to mine it for valuable business insights has become big business. But even advanced data analytics tools only reveal some of those insights, and few are capable of representing that data visually in a way anyone can understand.
Collecting the data is the easy part, but gaining a real understanding of how these various data sets are connected is trickier. Being able to make those connections in an agile way and actually see how ideas, organizations, and people are linked together is the latest frontier in extracting value from data. That’s where knowledge graphs enter the picture.
Knowledge graphs present complex datasets in visual models that are highly relatable to human viewers. By organizing concepts visually and breaking them down by sub-category or type, it’s now possible to draw a direct line between two disparate datasets, even in ways that aren’t immediately obvious. Knowledge graphs sit atop your existing structured or unstructured databases as a virtual layer, linking it all together. It’s not meant to take the place of existing data analytics tools, but rather to augment those tools and make the insights they unearth even more useful.
At the enterprise level, knowledge graphs allow organizations to break down various data silos by creating their own company-specific web of information or knowledge. Companies like Google, Facebook, and Microsoft all depend on knowledge graphs as essential parts of their infrastructures. Industries as diverse as pharmaceuticals, telecommunications and IT all use knowledge graphs to aid in research, identify hidden risk, and create chatbots to better serve their customers.
With knowledge graphs, datasets are represented visually using nodes. As the datasets grow, the nodes form their own clusters, with the central concept forming the nucleus around which all direct connections form. By zooming in or out on these node models, users can visualize all of their data subsets in one place. These models contextualize your data, revealing the relationships between silos and informing process improvements and better business decisions.
Visually mapping data using knowledge graphs takes data out of silos and presents it as a web of related concepts, making it easier to draw connections between datasets previously thought to be unrelated. The result is what amounts to an index of previously unsorted data.
Knowledge graphs identify relationships between complex datasets, and as more information is added, those data relationships grow, contextualizing it even further. Better contextualizing data helps reveal the bigger picture, enabling more informed decision-making.