• 6 Ways Big Data Is Changing HR

    In this contributed article, tech blogger Caleb Danziger believes that excellence in the human resources (HR) department can be instrumental in a company’s success. After all, HR professionals assume responsibilities like hiring and firing, on-boarding and training updates and making employees happy, productive and eager to stick around. People who work in HR often use technologies to impact their work, big data among them. He offers six ways big data in HR has had an impact.

Featured Stories

  • Best of arXiv.org for AI, Machine Learning, and Deep Learning – August 2019

    In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

  • Alternative Data in Payments Processing: The NextGen Payments Trend Increasing Customer and Retailer Loyalty

    In this special guest feature, Gary Read, Import.io CEO, discusses how leading payments processors have already discovered the value of web data and are using web data to create a better experience for both the consumer and the retailer with heightened transparency to reduce fraud and personalized shopping experiences. As more payment processors integrate web data into their mobile and web solutions, more consumers will benefit from the “frictionless finance” achieved through customized digital user experiences while retailers build brand loyalty, increase customer touch-points and create the personalized experience consumers desire.

  • insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads

    Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model [READ MORE…]

Featured Resource

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads

This new technology guide from DDN shows how optimized storage has a unique opportunity to become much more than a siloed repository for the deluge of data constantly generated in today’s hyper-connected world, but rather a platform that shares and delivers data to create competitive business value. The intended audience for this important new technology guide includes enterprise thought leaders (CIOs, director level IT, etc.), along with data scientists and data engineers who are a seeking guidance in terms of infrastructure for AI and DL in terms of specialized hardware. The emphasis of the guide is “real world” applications, workloads, and present day challenges.

All Recent News

Industry Perspectives

  • Making Use of Under-Utilized Big Data in the Insurance Industry

    In this special guest feature, Adam Bratt, CTO and Co-Founder of Indio Technologies, points out that the topic of big data in insurance has been an emerging hot topic over the last year. Insurance is directly related to most people as it deals directly with personal safety of health, life, and assets and for companies, it’s all of the company’s assets and how the business is run, the likelihood of success, etc. The implementation of big data analytics has been an important one. In the InsurTech industry, Big data analytics can be incredibly helpful for companies, however, privacy is a [READ MORE…]

  • When Data-Driven Meets Data Silos: Let the Fun Really Begin

    In this special guest feature, Ed Thompson, CTO and co-founder at Matillion, believes that on balance, the systems that lead to having many data silos are a good thing; they indicate a business has the autonomy to choose the best systems in each department. This should make the business more efficient overall. However, the business needs data from all these systems.

RSS Featured from insideHPC

  • Aliro to make Quantum Computing accessible to any Developer
    Today Aliro Technologies emerged from stealth with the closing of its $2.7 million seed round, led by Flybridge Capital Partners. "Aliro’s vision is to commercialize new software technologies that make today’s quantum hardware more accessible and useful for any coder, making hybrid classical-quantum programs the new standard. The company was spun out of Harvard’s quantum […]

Editor’s Choice

  • Field Report: KDD 2019

    As a very long time member of the ACM and their SIGKDD group, I’d always wanted to attend a KDD conference (first one occurred in 1995). This year I received a gracious invitation to attend KDD2019 in Anchorage, Alaska, August 4-8. It satisfied two of my bucket list items: witnessing a KDD first-hand and also visiting Anchorage. I was not disappointed with the experience! What follows is my “Field Report,” a travel log if you will, chronicling my observations, both technical and cultural. KDD is touted as being “the premier interdisciplinary conference bringing together researchers and practitioners from data science, [READ MORE…]

  • The Harvard Data Science Initiative and The MIT Press Launch the HARVARD DATA SCIENCE REVIEW

    The Harvard Data Science Initiative (HDSI) and the MIT Press are pleased to announce the launch of the Harvard Data Science Review (HDSR). The multimedia platform will feature leading global thinkers in the burgeoning field of data science, making research, educational resources, and commentary accessible to academics, professionals, and the interested public. With demand for data scientists booming, HDSR will provide a centralized, authoritative, and peer-reviewed publishing community to service the growing profession.

  • The insideBIGDATA IMPACT 50 List for Q3 2019

    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…]

  • Field Report: GPU Technology Conference 2019 #GTC19

    I eagerly attended my 3rd GPU Technology Conference (GTC): “Deep Learning & AI Conference,” in Silicon Valley, March 23-26 as a guest of host NVIDIA. GTC has become my favorite tech event of the year due to its highly focused topic areas that align well with my own; data science, machine learning, AI, and deep learning; plus the show has an academic feel that I appreciate.

  • insideBIGDATA 2019 Annual Executive Round Up

    Our annual insideBIGDATA Executive Round Up showcases the insights of thought leaders on the state of the big data industry, and where it is headed. In our annual 2019 round up, we examine five topics: the importance of AI explainability in 2019, what industries are making the best competitive use of AI in 2019, how enterprises are seeking to improve technological infrastructure and cloud hosting processes for supporting AI, how AI-optimized hardware solves important compute and storage requirements, and how AI plays important roles at 3 leading companies.

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