The current applications of Machine Learning (ML) are widespread: from deciding which trades to execute on Wall Street, determining credit decisions, optimizing inventory, improving product recommendations, predicting whether a user will click an ad, or Google’s ability to improve cooling efficiency at data centers. And that just scratches the surface. The heart of what makes ML possible is vast amounts of data, meaning if businesses don’t have access to or a good understanding of data relationships of their data assets, they will miss opportunities. Knowledge graphs can help.
Why? Companies striving to define and implement machine learning at the same time are discovering the easy part is implementing the algorithms used to make machines intelligent about a data set or problem. So what’s the hard part? Here’s a clue: data is emerging as the key differentiator in the machine learning race. Companies are scrambling to transform themselves to stay competitive digitally, and the stakes couldn’t be higher.
As a result, organizations are looking to knowledge graph technologies to enhance data search, information retrieval, and recommendations. By combining knowledge graphs with machine learning, organizations can make machine learning more ubiquitous and successful. According to Gartner research, 23% of organizations deployed graph techniques in their artificial intelligence (AI) projects. Perhaps other organizations don’t know they can combine knowledge graphs with machine learning by using platforms that are easy to adopt and scale. This makes machine learning more commonplace and more successful.
Read Vice President, Enterprise Solutions, Al Baker’s entire article at RTInsights.