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Interview: Prat Moghe, CEO of Cazena

I recently caught up with Prat Moghe, CEO of cloud data lake leader Cazena to get his take on how getting off the ground with cloud data lakes continues to be a major frustration for enterprises. We’re seeing such deployments taking at least six months and millions of dollars of annual spend for in-house development and management. There’s got to be a better way. Gartner has estimated the failure rate of big data projects as high as 80%. What can you do about companies that stubbornly hang on to legacy data strategies, using analytics/BI approaches that put them ever-more behind competitors who are modernizing their data stack with AI/ML/etc? In this interview, we’ll get some valuable perspectives for you to follow in accelerating your time-to-analytics.

NVIDIA A100, A40 and NVIDIA RTX A6000 Ampere Architecture-Based Professional GPUs Transform Data Science and Big Data Analytics

Scientists, researchers, and engineers are solving the world’s most important scientific, industrial, and big data challenges with AI and high-performance computing (HPC). Businesses, even entire industries, harness the power of AI to extract new insights from massive data sets, both on-premises and in the cloud. NVIDIA Ampere architecture-based products, like the NVIDIA A100 or the NVIDIA RTX A6000, designed for the age of elastic computing, deliver the next giant leap by providing unmatched acceleration at every scale, enabling innovators to push the boundaries of human knowledge and creativity forward.

How Third-party Data Can Enable Better Business Insights

Join this virtual event with Stephen Orban, author of Ahead in the Cloud and General Manager of AWS Data Exchange. He will lead a panel discussion with AWS customers to discuss the innovative techniques being used in data pipelines, with data analysis, and for data visualization. You will hear about real-world initiatives and breakthroughs being enabled by third-party data.

How ML Powers Data Access Governance with Immuta & Databricks

If data isn’t accessible for real-time analytics, is it still valuable? Immuta’s native Databricks integration avoids this dilemma by using ML to streamline data access governance, and deliver analytics-ready data quickly and securely. For Databricks users leveraging Immuta, ML drives sensitive data discovery, dynamic access control, and consistent policy enforcement.

Garbage in, Garbage Out – How We Got Here and Why We Must Get Out Now

This whitepaper, “Garbage in, Garbage Out – How We Got Here and Why We Must Get Out Now,” from our friends over at Profisee, reflects on why the state of data in most organizations is as dismal as it is, and why there is such a challenge involved in demonstrating the value of trusted data available across mission-critical operations and analytics in an enterprise.

Factoring the User Into Supply Chain Data Presentation

In this special guest feature, Jono Marcus, Behavioral Insights Director and Digital Project Owner for AtSource.io, Olam’s sustainability insights platform, at Olam International Ltd., explores how behavioral science can make complex data meaningful and useful.

Be (More) Wrong Faster – Dumbing Down Artificial Intelligence with Bad Data

In this white paper,”Be (More) Wrong Faster – Dumbing Down Artificial Intelligence with Bad Data,” our friends over at Profisee discuss how Master Data Management (MDM) will put your organization on the fast track to automating processes and decisions while minimizing resource requirements, while simultaneously eliminating the risks associated with feeding AI and ML data that is not fully trusted. In turn, your digital business transformation will be accelerated and your competitive edge will be rock solid.

How Automation Helps You Exploit the Value in Big Data

In this sponsored post, Simon Shah spearheads marketing at Redwood Software to support continued market growth and innovation for their cloud-based IT and business process automation solutions. He believes that by using automation to collect and manage your big data processes, you will truly exploit its value for the business.

Analyze-then-Store: The Journey to Continuous Intelligence – Part 6

This multi-part article series by our friends at Swim is intended for data architects and anyone else interested in learning how to design modern real-time data analytics solutions. It explores key principles and implications of event streaming and streaming analytics, and concludes that the biggest opportunity to derive meaningful value from data – and gain continuous intelligence about the state of things – lies in the ability to analyze, learn and predict from real-time events in concert with contextual, static and dynamic data. This article series places continuous intelligence in an architectural context, with reference to established technologies and use cases in place today.

Analyze-then-Store: The Journey to Continuous Intelligence – Part 5

This multi-part article series by our friends at Swim is intended for data architects and anyone else interested in learning how to design modern real-time data analytics solutions. It explores key principles and implications of event streaming and streaming analytics, and concludes that the biggest opportunity to derive meaningful value from data – and gain continuous intelligence about the state of things – lies in the ability to analyze, learn and predict from real-time events in concert with contextual, static and dynamic data. This article series places continuous intelligence in an architectural context, with reference to established technologies and use cases in place today.