Contributor
Jelani Harper is an editorial consultant servicing the information technology market. He specializes in data-driven applications focused on semantic technologies, data governance, and analytics.
Once, data science was an untamed, emergent discipline without rules, restrictions, or repercussions. Organizations simply wanted accurate advanced analytics; those fortunate enough to procure scarce data scientists gave them boundless …
Read More
The inner workings of data science—much like that of multi-parameter, opaque machine learning models—have traditionally been an enigma to the average business user. While the latter simply desires accurate predictions …
Read More
All data-driven processes are intrinsically binary. No matter what the application or use case might be, depending on data involves risks as well as potential gains. Reducing the former tends …
Read More
With all the media attention focused on contemporary Artificial Intelligence applications of computer vision, conversational AI, and knowledge graphs, it’s easy to lose sight of the basic data management mainstays …
Read More
An inordinate amount of some of the most vital aspects of Artificial Intelligence—from data engineering to data science, data preparation to machine learning—rely on one indispensable prerequisite: data modeling. Without …
Read More
The dynamic software agents of Robotic Process Automation represent many things to the modern enterprise. They’re simultaneously a digital workforce of tireless, ubiquitous employees, a cogent means of actuating statistical …
Read More
The edge computing paradigm has long been considered at the forefront of the Internet of Things. The ability to process data at the fringe of enterprise networks in today’s distributed …
Read More
The motion towards cloud computing is swiftly reaching a crescendo. It’s the most viable architecture for accommodating the scale of statistical Artificial Intelligence, one of the most effective mediums for …
Read More
Knowledge graphs may not be as lauded as machine learning, as well known as Natural Language Processing, or as futuristic as their synthesis in conversational AI applications, but they’re an …
Read More
The rapid retrieval of data—at scale—required for many cognitive computing use cases depends on several factors, only one of which is the vaunted computational power many attribute to the current …
Read More