Enhancing Business Innovation and Operational Efficiency Through Historical Data

In this contributed article, Adrian Kunzle, Chief Technology Officer at Own Company, discusses strategies around using historical data to understand their businesses better and fill gaps are often overlooked. When organizations maximize historical data, they can improve AI-driven decisions, reduce the overhead of data warehouses and ETL processes, while simultaneously driving portability and automation.

The Transformative Role of AI in the Healthcare Industry

In this feature article, Daniel D. Gutierrez, insideAInews Editor-in-Chief & Resident Data Scientist, discusses how the integration of AI into healthcare systems promises to enhance patient care, streamline clinical operations, and foster innovative research, marking a significant shift in how medical services are delivered and experienced.

The Rise of Streaming Data and Its Cost Efficiency – How Did We Get Here?

In this contributed article, Sijie Guo, Founder and CEO of Streamnative, believes that with remote work entrenched in the post-pandemic enterprise, organizations are restructuring their technology stack and software strategy for a new, distributed workforce. Real-time data streaming has emerged as a necessary and cost efficient way for enterprises to scale in an agile way. There are two sides to this coin with dual cost advantages – architectural and operational.

Advocating Collaboration in Safe AI Management

In this contributed article, Rosanne Kincaid-Smith, Group COO at Northern Data, delves into the ethical considerations of ensuring AI safety and emphasizes the need for a collective approach to AI management – involving a mixture of technical and societal bodies who understand its far-reaching impact. The piece sheds light on the growing concerns surrounding the emergence of next-generation AI technologies and underscores the new collaborative efforts of the US and UK in addressing safety concerns linked to the integration of AI into business operations.

Unlocking the True Power of AI by Turning Conventional ML Wisdom On Its Head

In this contributed article, Iain Wallace, Director of Machine Learning and Tracking Research at Ultraleap, discusses how rethinking your approach to machine learning can drive true AI innovation.

Personalizing Employee Experiences with Product Analytics

In this contributed article, Vara Kumar, co-founder and head of R&D and pre-sales at Whatfix, discusses how in today’s competitive landscape, harnessing the full potential of product analytics is pivotal for companies seeking to optimize their internal and external product usage. There are multifaceted benefits of leveraging product analytics,
showcasing its ability to provide profound insights into product utilization across an organization.

Low Code/No Code

In this contributed article, Ben Kliger, CEO and co-founder, Zenity, explores the connection between AI and no/low code development and how to bring application security measures to the new world of low-code/no-code app development.

In the Era of Cloud and AI, Hard Drives are More Critical than Ever Before 

In this contributed article, Jason Feist, Seagate’s Senior Vice President of Products and Markets, believes that amidst the data center boom and growth of AI, data storage is more important than ever, and it’s high time we revisit the HDD vs. SSD debate. While flash offers latency advantages and prices dropped temporarily, SSDs haven’t – and
never will – replace HDDs,

AI+BI: Bridging Cognitive and Usability Gaps in Business Intelligence

In this contributed article, Saurabh Abhyankar, EVP and Chief Product Officer, MicroStrategy, explains the synergy between the two technologies and how they come together to revolutionize how we understand data, make decisions, and envision the future of business.

From ER Diagrams to AI-Driven Solutions

In this contributed article, Ovais Naseem from Astera, takes a look at how the journey of data modeling tools from basic ER diagrams to sophisticated AI-driven solutions showcases the continuous evolution of technology to meet the growing demands of data management. Understanding how data modeling tools have changed over time gives us important insights into why organizing and analyzing data well is so important.