• Seven Steps to Effective AI Adoption for Your Enterprise

    In this contributed article, Rinat Gareev, a Solution Architect and ML Practice Lead at Provectus, details 7 important steps that can help your organization overcome the barriers to successful AI adoption. Today many executives realize that the future success of their business may depend on the ability to effectively implement an AI strategy, to keep pace with rapid AI-driven digitalization across almost all sectors and industries. 

Featured Stories

  • Faces as the Future of AI

    In this contributed article, Dr. Sergey I. Nikolenko, Head of AI at Synthesis AI, discusses how in AI, problems related to human faces are coming to the forefront of computer vision. The article considers some of them, discusses the current state of the art, and introduces a common solution that might advance it in the near future.

  • 5 Expert Tips to Help Build and Maintain a Powerful Customer Dashboard

    In this special guest feature, Dave Hurt, CEO and Co-founder at Verbdata, discusses how building, implementing, and maintaining dashboards requires a deep understanding of how to invest resources and time in product and engineering. While this task can be laborious, to begin with, planning ahead not only ensures customer satisfaction and optimum performance but provides actionable insights to assist the decision-making process. 

  • 2022 Trends in Data Modeling: The Interoperability Opportunity

    In this contributed article, editorial consultant Jelani Harper offers some intriguing trends for 2022 centered around data modeling and the interoperability opportunity. A plethora of methods including data fabrics, revamped cloud native Master Data Management capabilities, and governance frameworks employing cognitive computing to point-and-click at sources for detailed cataloging of their data are viable means of implementing data models across the heterogeneity of the modern enterprise’s data.

Featured Resource

Interview: Global Technology Leader PNY

The following whitepaper download is a reprint of the recent interview with our friends over at PNY to discuss a variety of topics affecting data scientists conducting work on big data problem domains including how “Big Data” is becoming increasingly accessible with big clusters with disk-based databases, small clusters with in-memory data, single systems with in-CPU-memory data, and single systems with in-GPU-memory data. Answering our inquiries were: Bojan Tunguz, Senior System Software Engineer, NVIDIA and Carl Flygare, NVIDIA Quadro Product Marketing Manager, PNY.

All Recent News

Industry Perspectives

  • Your Business’s Data Strategy is Hosed, You Just May Not Know It Yet

    In this special guest feature, Nick Bonfiglio, CEO of Syncari, discusses the key takeaway of a recent cross-functional executive panel: data interoperability is the key to effective operational data. Cloud data warehouses are here to stay, so rather than dedicating them to reporting business intelligence insights, businesses should think about their warehouse as a part of an overall data solution and unified data model.

  • Balancing the Benefits of Hyperautomation and Need for Discretion

    In this special guest feature, Loren Goodman, Co-founder and Chief Technology Officer of InRule Technology, discusses how hyperautomation is a new generation of tooling to automate the acceleration of automation. With hyperautomation, businesses can easily automate monotonous or repeatable tasks. Additionally, the combination of RPA with low code can enable the creation of multi-system integrated solutions.

RSS Featured from insideHPC

  • SiPearl Picks Intel Ponte Vecchio GPUs for European Exascale
    SiPearl, designing the microprocessor for European supercomputers, today announced it will incorporate Intel’s forthcoming “Ponte Vecchio” GPU, along with Intel’s oneAPI cross-architecture programming model, in a partnership for the first European exascale supercomputers. SiPearl said the Intel partnership allows European customers “the possibility” to combine SiPearl’s HPC CPU, called Rhea, with Intel’s family of general-purpose […]

Editor’s Choice

  • The insideBIGDATA IMPACT 50 List for Q4 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…]

  • The Amazing Applications of Graph Neural Networks

    In this contributed article, editorial consultant Jelani Harper points out that a generous portion of enterprise data is Euclidian and readily vectorized. However, there’s a wealth of non-Euclidian, multidimensionality data serving as the catalyst for astounding machine learning use cases.

  • Infographic: The Rise of No-Code Development Platforms

    Our friends over at Saas Platform company in Ireland called TeamKonnect have developed new infographic called “The Rise of No-Code Development Platforms” which is provided below. This infographic is a 101 guide to No-Code Development Platforms. Rising in popularity in the last decade, these platforms offer an exciting opportunity for businesses and organizations to develop apps that meet their needs without the engagement of software engineers.

  • 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.

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