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A collection of big data white papers reviewed by the editors of insideBIGDATA. Please visit the insideBIGDATA White Paper Library for a comprehensive list of white papers focus on big data strategies.

The 2018 State of Data Management

Profisee, a global leading modern data management technology company, released the results of its first annual data management report, “The 2018 State of Data Management.” The survey, conducted between January and April of 2018, aims to provide insights into data management, strategy, challenges, trends, benchmarks and ‘how others are doing it’. It is Profisee’s hope that data management professionals across the global data management community can use this information to help drive new initiatives.

Trends in Entertainment Industry Push Enterprises Toward AI Tools

This is the second entry in a five-part guide series that explores how artificial intelligence is revolutionizing and evolving the media and entertainment industries. This post explores how trends in the media and entertainment industries are leading enterprises to increasingly adopt AI tools. 

Combining the Benefits of Commercial & Open Analytics

A new e-book explores how organizations in many industries are using open source analytics and SAS, getting the most from both, and what role SAS plays throughout the analytics life cycle.

Improve Your Business with Modern Analytics Infrastructure

A new report from SAS explores how a modern analytics infrastructure and IT department can help you increase productivity, maintain the highest data quality through a standard data governance model, and make critical decisions faster and with more confidence.

Explore How to Detect and Address Machine Learning, AI Bias

Alegion is fully aware of the potential for machine learning bias because as they produce AI training data, the company is on the lookout for biases that can influence machine learning. A new white paper from Alegion, “Four Sources of Machine Learning Bias,” explores the four sources of AI bias, and how to mitigate these challenges for your AI systems. 

Ready, Set, Fail? Avoiding Setbacks in the Intelligent Automation Race

Corporate plans for AI likely to fail? A new study, “Ready, set, fail? Avoiding Setbacks in the Intelligent Automation Race” from our friends over at KPMG asserts that most corporate artificial intelligence projects underway or currently in the pipeline will fail.

The Speed of Data: A Survey of Data Decision Makers

Streamlio, the intelligent platform for fast data, announced results of a new “Speed of Data” survey conducted with Dimensional Research. The survey of hundreds of corporate decision makers worldwide found near universal agreement about the criticality of faster data processing, along with a huge gap between where companies need to be and where they are today.

Best of insideBIGDATA Technology Guides

Over the years since I became Managing Editor for insideBIGDATA, we’ve had some amazing partner firms contract with us to write custom technology guides, white papers, and advertorials. In many cases, I authored the materials myself and had a great time working on the project and getting to know the client subject matter experts. These resources consistently rank high every month in our vast library of content. In this article I wanted to provide an easy access list of the top 10 white papers of all time. The themes cut across the most popular in the big data industry. Enjoy!

Accelerate Business Value with Serverless Computing

In order to answer questions about serverless computing, Continuous Data Platform Iguazio offers a whitepaper “Accelerate Business Value with Serverless Computing” that weighs the pros and cons of function-as-a-service, or serverless computing in the cloud. The report defines exactly what serverless means (newsflash: it’s a misnomer), examining the offerings and challenges of the technology, and giving software engineers a look at how they can weigh their options when deciding how to engage with the hottest new areas in cloud computing.

Data-Efficient Machine Learning

From Quadrant (a D-Wave business), this whitepaper “Data-Efficient Machine Learning” describes a practical impediment to the application of deep neural network models when large training data sets are unavailable. Encouragingly however, it is shown that recent machine learning advances make it possible to obtain the benefits of deep neural networks by making more efficient use of training data that most practitioners do have.