The 3 Reasons Enterprises Need an AI Operating System for Intelligent process Automation
This new whitepaper, “The 3 Reasons Enterprises Need an AI Operating System for Intelligent process Automation,” from Veritone highlights how evolving technology meets enterprise demand for agile, intelligence-based solutions in the shape of AI-based operating systems (OS) across three areas: (i) AI OS for automation of human work; (ii) AI OS for process automation across all data sources; and (iii) AI OS for democratization of AI across the enterprise.
Featured Stories
Webinar: Using External Data to Accelerate Business in a Post-Vaccinated World
Join this webinar to learn how companies are developing insights to better prepare for growth opportunities, improve business performance and mitigate risk in a post-pandemic economy. Wednesday, June 24th, 2021 at 11:00 AM PT (2:00 PM ET)
Solidifying Absolute and Relative Data Quality with Master Data Management
In this contributed article, editorial consultant Jelani Harper highlights that contrary to popular belief, data are not the oil, fuel, energy, or life force coursing through the enterprise to inform decision-making, engender insights, and propel timely business action rooted in concrete facts. Data quality is.
A Laundry List for Cleaning Messy Data and Making It Business Ready
In this special guest feature, Mark Palmer, TIBCO SVP & GM of Analytics, Data Science & Data Virtualization, believes that companies who learn to leverage their data will beat out their competition. Making data-driven decisions for business strategy is essential in today’s tech-centric environment, and anyone who is not taking advantage of the information they’ve gathered will fall behind.
Featured Resource
Understanding Intention – Using Content, Context, and the Crowd to Build Better Search Applications
This white paper by enterprise search specialists Lucidworks, points out that unlike consumer search, which has become a seamless part of our everyday lives, the enterprise side might as well still be running Windows 95. Imagine if Amazon, Google, or Facebook treated every user the same, regardless of who they are, where they are, what they’re searching for, and what they’ve clicked. Your users expect that same sophistication in their enterprise apps.
Industry Perspectives
How AI and Behavioral Science are Addressing Vaccine Hesitancy
In this special guest feature, Ram Prasad, Co-Founder, The Final Mile, a Fractal Analytics Company, discusses how AI can help identify problem areas and how behavioral science could be pivotal in understanding why individuals have delayed immunization.
Before Jumping to Machine Learning, You Need a Solid Analytics Foundation
In this special guest feature, Michael Simms, Practice Director for Data & Analytics at Columbus Global Services, discusses four important elements of an effective data journey. Mapping a data journey provides awareness and understanding of where your organization is to ultimately get where you want to go, with enablement and adoption of the technology throughout. Without the clarity provided by a data journey, your organization won’t be positioned to successfully deploy the latest technology.
Featured from insideHPC
- NVIDIA Launches AI LaunchPad: AI Enterprise InfrastructureNVIDIA today unveiled NVIDIA AI LaunchPad, a program delivered through hybrid-cloud providers designed to give enterprises access to NVIDIA-powered AI infrastructure and software. Digital infrastructure company Equinix will be the first in the AI LaunchPad program, providing NVIDIA-powered hybrid-cloud solutions globally on Platform Equinix. NVIDIA said the Equinix infrastructure deploys in minutes, “allowing enterprises to access […]
News from insideHPC
- DOE Funding $29M to Develop Advanced Chemical Sciences Software
- Xilinx Announces FPGA EDA Tools with ML
- CPG Adds ML Capabilities to Data Center Battery Monitoring Service
- NVIDIA Launches AI LaunchPad: AI Enterprise Infrastructure
- DOE: 2021 Ernest Orlando Lawrence Award Call for Nominations
- insideHPC Guide to HPC Fusion Computing Model – A Reference Architecture for Liberating Data
- SiPearl Opens Site in Barcelona
Editor’s Choice
The insideBIGDATA IMPACT 50 List for Q2 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.
Hats Over Hearts
It is with great sadness that we announce the death of Rich Brueckner. His passing is an unexpected and enormous blow to both his family and the HPC Community. Rich was an institution in the HPC community. You couldn’t go to an event without seeing his red hat bobbing in the crowd, usually trailed by a fast-moving video crew. He’d be darting into booths, conducting interviews, and then speeding away to his next appointment.
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.
