RTInsights Team

Successful enterprise architecture starts with a strong understanding of your organization’s business capabilities.

An investment in enterprise architecture is an investment in the future of your organization. Still, getting started with enterprise architecture can seem like a daunting task. It requires dedication, resources, and buy-in from across the organization to build a sustainable program from scratch.

In today’s “new normal,” getting critical business insights from ever-growing volumes of data is both a technical and an economic challenge.

As urban areas are getting busier, cities are evolving to create smarter transport solutions, growing together to meet the changing need of a connected society.

As both the world population and data continue to grow, banks and other financial institutions are left scrambling to access and make sense of the overwhelming amount of data they are faced with every day. 

The past year has been the ultimate test for supply chain networks—and many have been found to be wanting. Trade wars, shifts in global trading relationships, and the COVID-19 crisis have wreaked havoc on even the most sophisticated supply chain systems, resulting in slowdowns, missed shipments, and an inability to meet customer requirements.

Nearly every organization today is exploring ways to achieve competitive differentiation through data science and machine learning. But the success of an investment in a data science or machine learning platform depends on how well it addresses the needs and concerns of data scientists, the IT team, and executive leadership.

The BI pipeline built on top of Hadoop – from HDFS to the multitude of SQL-on-Hadoop systems and down to the BI tool – has become strained and slow.

Many medical device manufacturers face an interesting dilemma. Their products are in great demand, but as they sell more products, they must add staff to maintain and service the devices in the field. So, increased revenues are often offset by increased costs.

Edge is considered the catalyst for a lot of emerging technologies, such as LiDAR, drones, traffic management, and wind turbine control.

Many AI projects encounter problems that can derail the effort or not meet the business objects. Check out this five-step plan for undertaking successful artificial intelligence implementations.

In today’s fast-paced world, businesses must shift from the traditional reactive mode of operations (something broke, let’s fix it) to a proactive strategy. Such a strategy requires identifying anomalies in real time and taking actions before they impact users or the business.