Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. GenAI is also making a splash in software development and mobile applications. In fact, due ...

Recently, high-profile breaches and cybersecurity failures have brought data governance and security to the forefront. Separately from these incidents, regulators have also applied growing scrutiny to ...

Manufacturers' equipment and operations data can provide crucial insights into how leaders can improve company processes. Data analytics tools examine information and then highlight important findings ...

Data has emerged as a crucial asset for organizations aiming to gain a competitive edge. However, the value of data is heavily contingent on its quality. Poor data quality can lead to misguided ...

According to a Forrester research report, 32% of businesses lose access to over one gigabyte of CRM data at least monthly. This is a significant amount of data, and the implications are severe, ...

Organizations that treat data as a product -- not just a byproduct of business operations -- can create tremendous value. Data products turn raw data into strategic assets that affect organizational ...

Master data management (MDM) has always been important and quite frankly, we’re all sick of hearing about it after three decades. For this and other reasons, some enterprises are unable to get their ...

In today's data-driven healthcare landscape, medical imaging stands at the forefront of diagnosis and treatment planning. From X-rays and MRIs to CT scans and ultrasounds, these images provide crucial ...

We all understand the importance of data quality. Metrics—such as third-party validations, match rates, and accuracy scores—help us assess data quality on its own terms. Yet, too often, organizations ...

Google published a case study that shows how using structured data and following best practices improved discoverability and brought more search traffic. The case study was about the use of Video ...

Enabling the collection and utilization of data is crucial to successfully supporting AI projects at enterprise scale. From data integration to data pipelines, AI performance, data governance, ...