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On the Edge of Something Big

In this contributed article, Tim Parker, VP of Network Strategy at Flexential, provides the top four reasons your organization needs an edge strategy now. Edge computing enables efficient data processing near the source to minimize latency, reduce bandwidth usage and lower costs while improving compliance, security and resiliency.

How AI is Shifting Workplace Dynamics and Winning Top Talent

In this special guest feature, Claus Jepsen, Deputy CTO at Unit4, AI has risen as a main focus point for its ability to accelerate processes and help organizations remain competitive in today’s unpredictable landscape. However, the benefits of AI go far beyond improved business operations and its potential to improve the internal workplace experience is often overlooked.

Conceptualizing the 2020’s: The Decade of the Internet of Things

In this contributed article, editorial consultant Jelani Harper highlights several lofty predictions about the number of connected devices in the IoT begin this year; many developments directly impacting its adoption rates will flourish in the coming 10 years, rendering it the premier expression of data management.

How to Translate Big Data into Big Business Value

In this contributed article, Dr. Michael Zeller, secretary and treasurer for ACM SIGKDD, and CEO of Dynam.AI, offers 4 important steps for businesses looking to turn big data into big value. While (big) data serves as the foundation, smarter, data-driven decisions deliver the business value.

What Employers Should Consider in Big Data Hiring

In this contributed article, freelance human Avery Phillips discusses the many things you need to consider when hiring for big data. Thinking about the nature of your organization, the potential roles, and responsibilities of your data employees, what you aim to achieve through the use of big data, and finally, considering budget; is a good place to start when it comes to big data hiring.

Meeting The Data Demands of Automation

In this special guest feature, Lori McKellar, Senior Director, Product Marketing for AppWorks at OpenText, discusses how organizations across a plethora of industries are understanding the true importance of digitalization, and have started embarking on their automation journey in some shape or form. However, as a result of this process, organizations are seeing a significant increase in the volume, velocity and variety of data, including the rise of unstructured content that is meant to be automated.

Why Partnerships Beat Outsourcing In Data Labeling

In this contributed article, Mohammad Musa, Founder & CEO of Deepen AI, discusses how good data labeling leads to better results, whether it’s in autonomous cars, medical imaging, or any other industry where AI thrives. Done poorly, the entire system suffers. Inefficiencies and inaccuracies become inevitable, while major safety risks caused by poor labeling can derail an entire project.

Teacher Pay is Stagnating. Data and Analytics Could Give it a Boost

In this special guest feature, University of San Diego Assistant Professor in Economics, Alison Sanchez, argues that economic questions like how to increase teachers’ pay can be answered through large-scale data analysis. There is not one single answer or explanation behind the widespread salary drop for teachers, but data and analytics can reveal individual causes of teacher pay stagnation and provide customized solutions to address them.

Reality Bites: 3 Misconceptions that Can Lead to Microservice Mayhem

In this contributed article, Eric D. Schabell, Global Technology Evangelist and Portfolio Architect Director at Red Hat, discusses how microservices are core to organizations’ flexibility and agility in the digital world. But that doesn’t mean that microservices are right for every use case or even for every organization—at least, not right now.

Tomorrow’s Machine Learning Today: Topological Data Analysis, Embedding, and Reinforcement Learning

In this contributed article, editorial consultant Jelani Harper highlights how certain visual approaches of graph aware systems will significantly shape the form machine learning takes in the near future, exponentially increasing its value to the enterprise. Developments in topological data analysis, embedding, and reinforcement learning are not only rendering this technology more useful, but much more dependable for a broader array of use cases.