The benefits of analytics are well-documented. Analytics has helped organisations transform retail experiences, map pathways for trains and trucks, discover extraterrestrial life, and even predict diseases. However, over the past few years, organisations across the globe have wrestled with just how much human error has permeated their analytics attempts, often ending with disastrous results. From crashing spacecraft to sinking ships, transferring billions of dollars to unintended recipients, and causing deaths due to overdose of medication, human error in data analysis has far-reaching ramifications for organisations. The reason for human error in data analysis could be many, such as lack of experience, fatigue or loss of attention, lack of knowledge, or the all-too-common biases in interpreting data. However, what's common among these errors is that they are related to humans reading, processing, analysing, and interpreting data.

The benefits of analytics are well-documented. Analytics has helped organisations transform retail experiences, map pathways for trains and trucks, discover extraterrestrial life, and even predict diseases. However, over the past few years, organisations across the globe have wrestled with just how much human error has permeated their analytics attempts, often ending with disastrous results. From crashing spacecraft to sinking ships, transferring billions of dollars to unintended recipients, and causing deaths due to overdose of medication, human error in data analysis has far-reaching ramifications for organisations. The reason for human error in data analysis could be many, such as lack of experience, fatigue or loss of attention, lack of knowledge, or the all-too-common biases in interpreting data. However, what's common among these errors is that they are related to humans reading, processing, analysing, and interpreting data.

It was soon evident that embracing digital technologies and using AI-driven analytics was the only way to remain buoyant and navigate the disruptions. Several companies worldwide have already transitioned to the work-from-home concept and have adapted to the modern distributed work ecosystem (See: How is digital transformation shaping the new future?). For CIOs, realigning priorities and accelerating enterprise innovations continue to be a roller-coaster experience amidst these unprecedented times. More and more enterprises are now leaning on data science and analytics to optimize business performance and drive growth. With virtual communication taking the center stage, there is a growing emphasis on implementing AI-based workforce analytics and business intelligence solutions to fast-track digital transformation and generate deeper operational insights to respond faster and steer the volatile economic landscape.

Smart & Final is rolling out Hypersonix's AI-driven analytics platform to support the company's enterprise analytics and digital transformation initiatives. The two companies started working together sixty days ago on a successful pilot program. With this announcement, Smart & Final officially joins a handful of early adopters in the grocery and consumer-commerce industries turning to the innovative company to help navigate the post-COVID-19 market. "Hypersonix is a key ingredient in leveraging actionable analytics that can be operationalized by our business teams as part of our on-going digital transformation," said Ed Wong, EVP and Chief Digital Officer at Smart & Final. "We established a great innovation-centric collaboration with Hypersonix where we are finding new ways to address our needs in key strategic areas for our business."

If you've worked in industry for any length of time, you've experienced wave after wave of digital technologies washing over the plant floor, from robots to tablets. The next decade will see a new technological wave breaking over the landscape, as artificial intelligence expands from a narrow set of tasks to far more sophisticated applications that transform industry. Many companies know that the wave is coming, and are starting to think about how it will impact operations, workforce development, and their ability to compete in the marketplace. According to Gartner, by 2021 artificial intelligence will create nearly $3 trillion in business value. One can point to a number of factors, including AI's progression from simple, pre-programmed routines to advanced machine learning algorithms, as well as the introduction of AI platforms and service offerings that ease the design and deployment of new applications. But when it comes to manufacturing, there's another factor that comes into play: The rise of the industrial Internet of Things.

This year I took on a couple of ambitious tasks. One was part of my professional development, to learn everything I could about artificial intelligence (AI) and marketing, and the other focused on annual native ad tech research, similar to what was presented here last year – the 2017 Native Advertising Technology Landscape. Little did I know at the time, but an entire ebook came out of the subsequent AI research, "Everything You Need to Know About Marketing Analytics and Artificial Intelligence." It literally is everything you need to know about marketing and AI today and its impact on analytics, earned, owned and paid media. As a result, I'd like to share what I learned conducting all of this recent research in a two part series.

Gearing up for three days of coverage of BigDataNYC 2016 at 37 Pillars in New York City, the SiliconANGLE Media team and NVIDIA Corp. hosted The Future: AI-Driven Analytics, An Evening of Deep Learning. This event kicked off the conversation about deriving benefits from Big Data to advanced Artificial Intelligence (AI) and Machine Learning (ML). An event panel met to talk about deep learning, what it means, where it's headed and implications for next-gen apps. Panelists Jim McHugh, VP and GM of NVIDIA Corp.; Randy Swanberg, distinguished engineer at IBM; Ram Sriharsha, product manager, Apache Spark, at DataBricks, Inc.; and Josh Patterson, director of Field Engineering at Skymind joined host George Gilbert, (@ggilbert41), Big Data analyst at Wikibon and theCUBE cohost (from the SiliconANGLE Media team), to talk about deep learning and where is going in the future. Gilbert began the panel discussion by saying that the real advance that is impending right now is the magnitude of cores that use GPUs (Graphics Processing Unit) as auxiliary processing units, which he feels is going to change the future of where computation will go.

This event highlights the changes that NVIDIA GPU-accelerated computing has brought to the analytics, machine learning, and deep learning segments. This new computing paradigm is enabling customers to extend the benefits beyond big data with the power of deep learning and accelerated analytics. The leaders from this growing ecosystem of solutions and technologies that are delivering on this promise will lead an active discussion in the keynote and panels.