Every company today is data-driven or at least claims to be. Business decisions are no longer made based on hunches or anecdotal trends as they were in the past. In this environment, data quality is a necessary conversation.

Leaders must take pragmatic and targeted actions to improve their enterprise data quality if they want to accelerate their organization's digital transformation.

A case study that walks through the practical issues faced when trying to reduce maintenance costs with a manufacturing digital twin

This paper on electronic rail digital twins, presented at the Power Electronics, Drive Systems, and Technologies Conference focuses on the need for precision to execute an exact simulation of the performance of real physical system.

Report on a digital twin developed to understand the structural health of a 47-year-old pumped storage station (first operational in 1974), and determine if the asset could continue to operate safely into the future.

An article heralding the launch of a scaled oil & gas digital twin involving up to 300 workers processing up to 250,000 B/D of oil

Data quality management systems are thoroughly researched but the advent of Big Data might pose some serious questions pertaining to the applicability of existing data quality concepts. This paper aims to investigate various components and activities forming part of data quality management such as dimensions, metrics, data quality rules, data profiling and data cleansing.

An introduction to the cost of unstructured data.

The key is in the title – 8 individual perspectives on the value of interoperability.

The Digital Twin Capabilities Periodic Table (CPT) is an architecture and technology agnostic requirements definition framework. It is aimed at organizations who want to design, develop, deploy and operate digital twins based on use case capability requirements versus the features of technology solutions.