DATAVERSITY

2 weeks ago | dataversity.net | Tami Fertig

The latest installment in our Q&A series with women leaders in data features Susan Featherston, MPH, senior strategic consultant for Vantage Technology Consulting Group. (Read our previous Q&A here.)Susan Featherston, MPH, has been working with data for the past 20 years, across a variety of fields – from non-profits and higher education to federal government and academic research.

3 weeks ago | dataversity.net | Samuel Bocetta

Data used to be a physical thing, stored on paper and in ledgers. Security was also a physical thing, with valuable data protected by actual locks and guards. Today, we navigate a landscape dominated by code, algorithms, and digital streams of data, a far cry from those early days. Yet, despite these transformative changes, the essence of what makes data “smart” remains rooted in principles that are as old as time: accuracy, confidentiality, and integrity.

4 weeks ago | dataversity.net | Don Boxley

Artificial intelligence is changing everything – and its impact on high availability (HA) clustering is no exception. The way in which AI and HA are coming together is making clusters more resilient, self-sustaining, and increasingly smarter at handling workloads. In other words, instead of IT professionals continuously putting out fires – constantly adjusting and working to fix things – AI-driven HA clusters are optimizing and balancing workloads across clouds and other infrastructure.

1 month ago | dataversity.net | Tami Fertig

The latest installment in our Q&A series with women leaders in data features Prosasty Chaudhuri, CEO and founder of Cuspide Data. (Read our previous Q&A here.)Prosasty Chaudhuri spent over two decades working in the data management industry before founding Cuspide Data last year. Previously, she served as the chief data officer for several leading financial institutions, spearheading data strategy and governance efforts to align with business goals.

1 month ago | dataversity.net | Nahla Davies

Data-driven decision-making is essential in all organizations, but using low-quality data can potentially be more harmful than using no data at all. Unreliable or outdated data can have huge negative consequences for even the best-laid plans, especially if you’re not aware there were issues with the data in the first place. That’s why data observability is so important: It’s a proactive approach to ensuring your data remains accurate, consistent, and trustworthy. But how does one achieve it?