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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.

Three Steps to Data Protection – And How They Differ for Structured vs Unstructured Data

In this special guest feature, Scott Lucas, Head of Marketing at Concentric, suggests that compliance is a complex topic, and in this article he addresses the surface of what you’ll need for your particular data and regulatory environment. Having a clear understanding of how to discover, assess and protect structured and unstructured data, and their differences, gives you the foundation you need for an effective and manageable program to protect the PII you manage.

Avoiding the Negative Impacts of Dirty Marketing Data

In this contributed article, Sky Cassidy, CEO of MountainTop Data, highlights how consumers are interested in receiving relevant, meaningful messages but too often the data base a company relies on is filled with incorrect and inconsistent information – meaning those messages are lost. Dirty data can have a negative effect on a company’s bottom line, with some business leaders estimating erroneous online accounts have cost them 12% of their overall revenue.

Consumption-derived Data Governance is Difficult to Flank

In this special guest feature, Doug Wick, Vice President of Product at ALTR, believes that data consumption governance, implemented at the query level to observe and control consumption of sensitive data, could help organizations to take full advantage of the cloud while reducing the associated risks.

The ModelOps Movement: Streamlining Model Governance, Workflow Analytics, and Explainability

In this contributed article, editorial consultant Jelani Harper discusses how the ModelOps movement either directly or indirectly addresses each of the following three potential barriers to cognitive computing success: model governance, explainability, and workflow analytics.

How HR Specialists Can Use AI to Support their Remote Workforce During Lockdowns

In this special guest feature, Ricardo Michel Reyes, CTO and co-founder at Erudit A.I., believes that with work-from-home presenting unprecedented difficulties for the HR profession, AI can help lift the burden in a number of ways. Using AI can give HR specialists a crucial tool in supporting their remote workforce – one that is often preferred by employees.

Video Highlights: Unleashing DataOps Keynote

In this keynote presentation from the DataOps Unleashed virtual conference, innovator Kunal Agarwal, CEO of Unravel Data, describes how companies large and small are using DataOps to make their technology stacks hum, get more done at a lower cost, and improve both customer experience and the bottom line.

How Big Data Helps Us Understand Denial of Service (DoS) Attacks

In this special guest feature, Dr. James Stanger, CompTIA Chief Technology Evangelist, highlights how big data is a concept that can provide insight into DDoS attacks and equip companies with the tools they need to effectively combat this threat. Another important tool for mitigating DDoS attacks is the use of multiple, redundant systems and cloud-based data scrubbing platforms that can filter out DDoS traffic. However, hackers have businesses beat when it comes to the early implementation of big data methodologies.

New to AI Adoption? Don’t Let Data be Your Achilles Heel

In this contributed article, Jeff White is the founder and chief executive officer of Gravy Analytics, discusses the realities of big data: no data source is perfect, and despite your best efforts, issues with new technologies like machine learning and AI are bound to occur. By understanding how your underlying data is collected, cleaned, verified and assembled, organizations can derive maximum value while optimizing internal resources, improving the customer experience, and avoiding costly mistakes along the way.

The Fate of Feature Engineering: No Longer Necessary, or Much Easier?

In this contributed article, editorial consultant Jelani Harper believes that features are the definitive data traits enabling machine learning models to accurately issue predictions and prescriptions. In this respect, they’re the foundation of the statistical branch of AI. However, the effort, time, and resources required to engender those features may become obsolete by simply learning them with graph embedding so data scientists are no longer reliant on hard to find, labeled training data.