Seth Earley is a sought-after speaker, writer, and influencer. His writing has appeared in IT Professional Magazine from the IEEE where, as former editor, he wrote a regular column on data analytics and information access issues and trends. He has also contributed to the Harvard Business Review, CMSWire, CustomerThink, KMWorld, Journal of Applied Marketing Analytics, and he is the award-winning author of “The AI Powered Enteprise” by Lifetree Media.
Knowledge Graphs, a Tool to Support Successful Digital Transformation Programs
Knowledge graphs are pretty hot these days. While this class of technology is getting a lot of market and vendor attention these days, it is not necessarily a new construct or approach. The core principles have been around for decades. Organizations are becoming more aware of the potential of knowledge graphs, but many digital leaders are puzzled as to how to take the next step and build business capabilities that leverage this technology. Read the article.
Is Your Data Infrastructure Ready for AI?
Creating an ontology is an essential investment to prepare your enterprise to realize the benefits of AI and machine learning. Gone are the days when businesses should simply allow a number of small AI projects to blossom independently: for these projects to be competitive they need to draw on data from across the company, data stored in many different forms in many different systems. An ontology defines these connections in a way that AI can take advantage of. Businesses will be best positioned to build ontologies if they identify and research pain points first–areas where the data connections are most needed–before beginning to set the organizing principles for the ontology itself. Read the article.
How Companies Are Benefiting from “Lite” Artificial Intelligence
AI applications range from the very complex and expensive (like self-driving cars) to more modest “AI lite” initiatives. In this article Seth lays out a path to AI that companies can undertake right now. Read the article.
The Problem with AI
Organizations are best off if they focus on understanding their own data, focus on the business problems they are trying to solve, and build the semantic layers that can allow for data portability across various platforms. This lets them take advantage of best of-breed solutions and not become locked into a particular vendor that does not abstract the business problem, analytic, data, and platform layers required to operationalize the fast-evolving advanced machine learning analytic and AI technologies. Read the full article.
There is No AI Without IA
Although AI is receiving a lot of visibility, the fact that these technologies all require some element of knowledge engineering, information architecture, and high-quality data sources is not well known. Read the full article.