• DataOps Dilemma: Survey Reveals Gap in the Data Supply Chain

    The survey associated with this report, commission by Immuta, focused on identifying the limiting factors in the data “supply chain” as it relates to the overall DataOps methodology of the organization. DataOps itself is the more agile and automated application of data management techniques to advance data-driven outcomes, while the data supply chain represents the technological steps and human-involved processes supporting the flow of data through the organization, from its source, through transformation and integration, all the way to the point of consumption or analysis.

Featured Stories

Featured Resource

Real-Time Analytics from Your Data Lake Teaching the Elephant to Dance

This whitepaper from Imply Data Inc. introduces Apache Druid and explains why delivering real-time analytics on a data lake is so hard, approaches companies have taken to accelerate their data lakes, and how they leveraged the same technology to create end-to-end real-time analytics architectures.

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Industry Perspectives

  • The One Thing You Don’t Want to Leave Behind on Your Digital Transformation Journey: Your Data

    In this special guest feature, Kevin Campbell, CEO of Syniti, argues that digital transformation is data transformation, and for enterprises to have a successful digital transformation, their data transformation must be a priority. Through trust and assurance of data, organizations will set themselves up for more efficient business outcomes, strategic planning, and positive returns.

  • A Hitchhiker’s Guide to AI that Actually Works for Business

    In this special guest feature, Alex Hoff, Senior VP of Product Management & Marketing at Vendavo, believes that if you want an AI or ML solution that will be of any practical use, it needs to be a white-box model that is explainable, interpretable, and it will be both more usable and effective if it allows for human insights and intelligence to be combined with the artificial intelligence and insights – a centaur, or perhaps a cyborg.

RSS Featured from insideHPC

  • LLNL Reports on Inaugural ML for Industry Forum
    LLNL held its first-ever Machine Learning for Industry Forum (ML4I) on Aug. 10-12. Co-hosted by the Lab’s High Performance Computing Innovation Center (HPCIC) and Data Science Institute (DSI), the virtual event brought together more than 500 participants from the Department of Energy (DOE) complex, commercial companies, professional societies and academia. Industry sponsors included ArcelorMittal, Cerebras Systems, Ford Motor Company, […]

Editor’s Choice

  • The insideBIGDATA IMPACT 50 List for Q3 2021

    The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting [READ MORE…]

  • Big Data Industry Predictions for 2021

    2020 has been year for the ages, with so many domestic and global challenges. But the big data industry has significant inertia moving into 2021. In order to give our valued readers a pulse on important new trends leading into next year, we here at insideBIGDATA heard from all our friends across the vendor ecosystem to get their insights, reflections and predictions for what may be coming.

  • What is the Difference Between Business Intelligence, Data Warehousing and Data Analytics

    In this contributed article, Christopher Rafter, President and COO at Inzata,, writes that in the age of Big Data, you’ll hear a lot of terms tossed around. Three of the most commonly used are “business intelligence,” “data warehousing” and “data analytics.” You may wonder, however, what distinguishes these three concepts from each other so let’s take a look.

  • The Difference Between Data Science and Data Analytics

    In this contributed article, tech writer Rick Delgado, examines the differences between the terms: data science and data analytics, where people working in the tech field or other related industries probably hear these terms all the time, often interchangeably. Although they may sound similar, the terms are often quite different and have differing implications for business.

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