COVID-19 Impact: Temporary Surge in Sales of Big Data Analytics in Automotive Product Observed …

Big Data Analytics in Automotive Market 2018: Global Industry Insights by Global Players, Regional Segmentation, Growth, Applications, Major Drivers, Value and Foreseen till 2024

The report provides both quantitative and qualitative information of global Big Data Analytics in Automotive market for period of 2018 to 2025. As per the analysis provided in the report, the global market of Big Data Analytics in Automotive is estimated to growth at a CAGR of _% during the forecast period 2018 to 2025 and is expected to rise to USD _ million/billion by the end of year 2025. In the year 2016, the global Big Data Analytics in Automotive market was valued at USD _ million/billion.

This research report based on ‘ Big Data Analytics in Automotive market’ and available with Market Study Report includes latest and upcoming industry trends in addition to the global spectrum of the ‘ Big Data Analytics in Automotive market’ that includes numerous regions. Likewise, the report also expands on intricate details pertaining to contributions by key players, demand and supply analysis as well as market share growth of the Big Data Analytics in Automotive industry.

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Big Data Analytics in Automotive Market Overview:

The Research projects that the Big Data Analytics in Automotive market size will grow from in 2018 to by 2024, at an estimated CAGR of XX%. The base year considered for the study is 2018, and the market size is projected from 2018 to 2024.

The report on the Big Data Analytics in Automotive market provides a bird’s eye view of the current proceeding within the Big Data Analytics in Automotive market. Further, the report also takes into account the impact of the novel COVID-19 pandemic on the Big Data Analytics in Automotive market and offers a clear assessment of the projected market fluctuations during the forecast period. The different factors that are likely to impact the overall dynamics of the Big Data Analytics in Automotive market over the forecast period (2019-2029) including the current trends, growth opportunities, restraining factors, and more are discussed in detail in the market study.

Leading manufacturers of Big Data Analytics in Automotive Market:

The key players covered in this study

Advanced Micro Devices

Big Cloud Analytics

BMC Software

Cisco Systems

Deloitte

Fractal Analytics

IBM Corporation

Rackspace

Red Hat

SmartDrive Systems

Market segment by Type, the product can be split into

Hardware

Software

Services

Managed

Professional

Market segment by Application, split into

Product Development

Manufacturing & Supply Chain

After-Sales, Warranty & Dealer Management

Connected Vehicles & Intelligent Transportation

Marketing, Sales & Other Applications

Market segment by Regions/Countries, this report covers

North America

Europe

China

Japan

Southeast Asia

India

Central & South America

The study objectives of this report are:

To analyze global Big Data Analytics in Automotive status, future forecast, growth opportunity, key market and key players.

To present the Big Data Analytics in Automotive development in North America, Europe, China, Japan, Southeast Asia, India and Central & South America.

To strategically profile the key players and comprehensively analyze their development plan and strategies.

To define, describe and forecast the market by type, market and key regions.

In this study, the years considered to estimate the market size of Big Data Analytics in Automotive are as follows:

History Year: 2015-2019

Base Year: 2019

Estimated Year: 2020

Forecast Year 2020 to 2026

For the data information by region, company, type and application, 2019 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.

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Some important highlights from the report include:

  • The report offers a precise analysis of the product range of the Big Data Analytics in Automotive market, meticulously segmented into applications
  • Key details concerning production volume and price trends have been provided.
  • The report also covers the market share accumulated by each product in the Big Data Analytics in Automotive market, along with production growth.
  • The report provides a brief summary of the Big Data Analytics in Automotive application spectrum that is mainly segmented into Industrial Applications
  • Extensive details pertaining to the market share garnered by each application, as well as the details of the estimated growth rate and product consumption to be accounted for by each application have been provided.
  • The report also covers the industry concentration rate with reference to raw materials.
  • The relevant price and sales in the Big Data Analytics in Automotive market together with the foreseeable growth trends for the Big Data Analytics in Automotive market is included in the report.
  • The study offers a thorough evaluation of the marketing strategy portfolio, comprising several marketing channels which manufacturers deploy to endorse their products.
  • The report also suggests considerable data with reference to the marketing channel development trends and market position. Concerning market position, the report reflects on aspects such as branding, target clientele and pricing strategies.
  • The numerous distributors who belong to the major suppliers, supply chain and the ever-changing price patterns of raw material have been highlighted in the report.
  • An idea of the manufacturing cost along with a detailed mention of the labor costs is included in the report.

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The Questions Answered by Big Data Analytics in Automotive Market Report:

  • What are the Key Manufacturers, raw material suppliers, equipment suppliers, end users, traders And distributors in Big Data Analytics in Automotive Market ?
  • What are Growth factors influencing Big Data Analytics in Automotive Market Growth?
  • What are production processes, major issues, and solutions to mitigate the development risk?
  • What is the Contribution from Regional Manufacturers?
  • What are the Key Market segment, market potential, influential trends, and the challenges that the market is facing?

And Many More….

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WHO launches Blockchain platform to combat COVID19

As the world faces the ongoing deadly coronavirus pandemic, government around the world are looking for alternative tools in order to contain the spread of the virus.

Authorities are now looking towards tech and blockchain companies to help them track data from health workers. Authorities are looking to use this data in order to create a map that will help them track people who have high risk of exposure and infection.

The World Health Organization, and the United States Center for Disease Control, along with other international agencies are now looking towards IBM’s Blockchain Platform. Authorities said that IBM’s platform will provide the necessary support for them to be able to stream data into the MiPasa Project.

Information from the World Health Organization, the Center for Disease Control, and other similar agencies said that IBM’s Blockchain Platform would support a data streaming service for the MiPasa Project.

IBM has been engaged by purpose-driven entities, having meaningful projects like MiPasa that are designed to have an impact on the outcomes during this time of crisis.

MiPasa: Integrating data at scale

The MiPasa Blockchain technology uses big data analytics to analyze data provided by health workers on the Covid-19 pandemic.

The WHO press release revealed that the blockchain platform was made to ease the synthesizing of data sources. It is designed to address inconsistencies and identify errors or misreporting. The new platform also allows the integration of trusted new information.

Furthermore, the creators of Mipasa hope that this tool can help technologists, data scientists, and public health officials by giving them the data they need at scale to respond. It is also expected to help in formulating solutions useful in controlling the covid-19 pandemic.

The blockchain-based platform is slated to host an array soon of publicly accessible analytics tools too. Mipasa describes the new platform’s reliability and accessibility as a “verifiable information highway.”

Officials help the Mipasa platform and vice versa

The Mipasa platform is supported by a variety of professionals in many specialized fields, including health, software and app development, and data privacy, in making it easy to gather reliable and quality data. The group aims to make the data accessible to appropriate entities that support Mipasa.

The onboarding is done through the Unbounded Network, which is running a production version of The Linux Foundation’s Foundation’s Hyperledger Fabric on multiple clouds, IBM has been amongst the early supporters.

IBM helps more participants to collaborate openly, through permissioned and non-permissioned blockchains and has been in production since 2018. The blockchain-based platform was created for the attested coronavirus data built on Hyperledger Fabric.

MiPasa can already access information from agencies that integrates their platform with the simple use of APIs. These organizations include the World Health Organization, the Center for Disease Control, the Israeli Public Health Ministry, and other qualified agencies.

The WHO believes that the study, collate, and collection of Covid data, including spread and containment, is much easier with the use of the MiPasa platform. The project is useful in monitoring and forecasting local and global trends about the pandemic. The WHO also believes that the MiPasa project helps detect asymptomatic carriers through sharing big data on infection records and occurrences globally to powerful AI processors around the globe.

MiPasa was launched with the collaboration of private companies, including IBM, Oracle and Microsoft, and other supporters like and the John Hopkins University. A robust data platform lays a foundation for helping to solve many other problems; MiPasa is starting to get off the ground.

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Manufacturing & Industrial Automation Lead The Way

I’m always surprised that some people think of manufacturing as stodgy, old school and slow to change – in my view, nothing could be further from the truth! All the evidence shows that the manufacturing industry has consistently led the way from mechanical production, powered by steam in the 18th century, to mass production in the 19th century, followed by 20th century automated production.

The data center merging with the factory floor

Fast forward to today. The fourth industrial revolution is well underway, driven by IoT, edge computing, cloud and big data. And once again, manufacturers are at the forefront of intelligent production, leading the way in adopting technologies like augmented reality, 3D printing, robotics, artificial intelligence, cloud-based supervisory control and data acquisition systems (SCADA) plus programmable automation controllers (PACs). Watch the video below that addresses how manufacturers are changing to embrace Industry 4.0.

In fact, I always visualize the fourth industrial revolution, otherwise known as Industry 4.0, as the data center merging with the factory floor, where you have the perfect blend of information and operational technology working together in tandem. Let’s look at a couple of examples.

Helping monitor and manage industrial equipment

One of our customers, Emerson, a fast-growing Missouri-based company with more than 200 manufacturing locations worldwide, provides automation technology for thousands of chemical, power, and oil & gas organizations around the world. Today, Emerson customers are demanding more than just reliable control valves. They need help performing predictive maintenance on those valves.

To address these needs, Emerson worked with Dell Technologies OEM | Embedded & Edge Solutions to develop and deploy an industrial automation solution that collects IoT data to help its customers better monitor, manage and troubleshoot critical industrial equipment. With our support, Emerson successfully developed a new wireless-valve monitoring solution and brought it to market faster than the competition. This is just the first step in what Emerson sees as a bigger journey to transform services across its entire business. You can read more about our work together here.

Bringing AI to the supply chain to reduce waste and energy

Meanwhile, San-Francisco based Noodle.ai has partnered with us to deliver the world’s first “Enterprise AI” data platform for manufacturing and supply chain projects.

This solution allows customers to anticipate and plan for the variables affecting business operations, including product quality, maintenance, downtime, costs, inventory and flow. Using AI, they can mitigate issues before they happen, solve predictive challenges, reduce waste and material defects as well as cutting the energy required to create new products.

For example, one end-customer, a $2 billion specialty steel manufacturer, needed to increase profit per mill hour, meet increasing demand for high quality steel at predictable times, and reduce the amount of energy consumed. Using the “Enterprise AI” data platform, the customer reported $80 million savings via reduced energy costs, freight costs, scrapped product, and raw material input costs.

Helping design innovative and secure voting technology

Yet, another customer, Democracy Live wanted to deliver a secure, flexible, off-the-shelf balloting device that would make voting accessible to persons with disabilities and that could replace outdated, proprietary and expensive voting machines.

After a comprehensive review of vendors and products, Democracy Live asked us to design a standardized voting tablet and software image. Our Dell Latitude solution complete with Intel processors and pre-loaded with Democracy Live software and Windows 10 IoT Enterprise operating system provides strong security and advanced encryption.

And the good news for Democracy Live that we take all the headaches away by managing the entire integration process, including delivery to end-users. The result? Secure, accessible voting with up to 50 percent savings compared with the cost of proprietary voting machines. Read what Democracy Live has to say about our collaboration here.

Change is constant

Meanwhile, the revolution continues. Did you know that, according to IDC, by the end of this year 60 percent of plant workers at G2000 manufacturers will work alongside robotics, while 50 percent of manufacturing supply chains will have an in-house or outsourced capability for direct-to-consumption shipments and home delivery? More details available here.

Unlock the power of your data

Don’t get left behind! Dell Technologies OEM | Embedded & Edge Solutions is here to help you move through the digital transformation journey, solve your business challenges and work with you to re-design your processes. We can help you use IoT and embedded technologies to connect machines, unlock the power of your data, and improve efficiency and quality on the factory floor.

And don’t forget we offer the broadest range of ruggedized and industrial grade products, designed for the most challenging environments, including servers, edge computing, laptops and tablets. We’d love to hear from you – contact us here and do stay in touch.

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Data Lake, Data Warehouse and Database…What’s the Difference?

There are so many buzzwords these days regarding data management. Data lakes, data warehouses, and databases – what are they? In this article, we’ll walk through them and cover the definitions, the key differences, and what we see for the future.

Start building your own data lake with a free trial

Data Lake Definition

If you want full, in-depth information, you can read our article called, “What’s a Data Lake?” But here we can tell you, “A data lake is a place to store your structured and unstructured data, as well as a method for organizing large volumes of highly diverse data from diverse sources.”

The data lake tends to ingest data very quickly and prepare it later, on the fly, as people access it.

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Data Warehouse Definition

A data warehouse collects data from various sources, whether internal or external, and optimizes the data for retrieval for business purposes. The data is usually structured, often from relational databases, but it can be unstructured too.

Primarily, the data warehouse is designed to gather business insights and allows businesses to integrate their data, manage it, and analyze it at many levels.

Database Definition

Essentially, a database is an organized collection of data. Databases are classified by the way they store this data. Early databases were flat and limited to simple rows and columns. Today, the popular databases are:

  • Relational databases, which store their data in tables
  • Object-oriented databases, which store their data in object classes and subclasses

Data Mart, Data Swamp and Other Terms

And, of course, there are other terms such as data mart and data swamp, which we’ll cover very quickly so you can sound like a data expert.

Enterprise Data Warehouse (EDW): This is a data warehouse that serves the entire enterprise.

Data Mart: A data mart is used by individual departments or groups and is intentionally limited in scope because it looks at what users need right now versus the data that already exists.

Data Swamp: When your data lake gets messy and is unmanageable, it becomes a data swamp.

The Differences Between Data Lakes, Data Warehouses, and Databases

Data lakes, data warehouses and databases are all designed to store data. So why are there different ways to store data, and what’s significant about them? In this section, we’ll cover the significant differences, with each definition building on the last.

The Database

Databases came about first, rising in the 1950s with the relational database becoming popular in the 1980s.

Databases are really set up to monitor and update real-time structured data, and they usually have only the most recent data available.

The Data Warehouse

But the data warehouse is a model to support the flow of data from operational systems to decision systems. What this means, essentially, is that businesses were finding that their data was coming in from multiple places—and they needed a different place to analyze it all. Hence the growth of the data warehouse.

For example, let’s say you have a rewards card with a grocery chain. The database might hold your most recent purchases, with a goal to analyze current shopper trends. The data warehouse might hold a record of all of the items you’ve ever bought and it would be optimized so that data scientists could more easily analyze all of that data.

The Data Lake

Now let’s throw the data lake into the mix. And because it’s the newest, we’ll talk about this one more in depth. The data lake really started to rise around the 2000s, as a way to store unstructured data in a more cost-effective way. The key phrase here is cost effective.

Although databases and data warehouses can handle unstructured data, they don’t do so in the most efficient manner. With so much data out there, it can get expensive to store all of your data in a database or a data warehouse.

In addition, there’s the time-and-effort constraint. Data that goes into databases and data warehouses needs to be cleansed and prepared before it gets stored. And with today’s unstructured data, that can be a long and arduous process when you’re not even completely sure that the data is going to be used.

That’s why data lakes have risen to the forefront. The data lake is mainly designed to handle unstructured data in the most cost-effective manner possible. As a reminder, unstructured data can be anything from text to social media data to machine data such as log files and sensor data from IoT devices.

Data Lake Example

Going back to the grocery example that we used with the data warehouse, you might consider adding a data lake into the mix when you want a way to store your big data. Think about the social sentiment you’re collecting, or advertising results. Anything that is unstructured but still valuable can be stored in a data lake and work with both your data warehouse and your database.

Note 1: Having a data lake doesn’t mean you can just load your data willy-nilly. That’s what leads to a data swamp. But it does make the process easier, and new technologies such as having a data catalog will steadily make it simpler to find and use the data in your data lake.

Note 2: If you want more information on the ideal data lake architecture, you can read the full article we wrote on the topic. It describes why you want your data lake built on object storage and Apache Spark, versus Hadoop.

What’s the Future of Data Lakes, Data Warehouses, and Databases?

Will one of these technologies rise to overtake the others?

We don’t think so.

Here’s what we see. As the value and amount of unstructured data rises, the data lake will become increasingly popular. But there will always be an essential place for databases and data warehouses.

You’ll probably continue to keep your structured data in the database or data warehouse. But these days, more companies are moving their unstructured data to data lakes on the cloud, where it’s more cost effective to store it and easier to move it when it’s needed.

This workload that involves the database, data warehouse, and data lake in different ways is one that works, and works well. We’ll continue to see more of this for the foreseeable future.

If you’re interested in the data lake and want to try to build one yourself, we’re offering a free data lake trial with a step-by-step tutorial. Get started today, and don’t forget to subscribe to the Oracle Big Data blog to get the latest posts sent to your inbox

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What’s the Connection Between Big Data and AI?

When people talk about big data, are they simply referring to numbers and metrics?

Yes.

And no.

Technically, big data is simply bits and bytes—literally, a massive amount (petabytes or more) of data. But to dismiss big data as mere ones and zeroes misses the point. Big data may physically be a collection of numbers, but when placed against proper context, those numbers take on a life of their own.

This is particularly true in the realm of artificial intelligence (AI). AI and big data are intrinsically connected; without big data, AI simply couldn’t learn. From the perspective of the team in charge of Oracle’s Cloud Business Group (CBG) Product Marketing, they liken big data to the human experience. On Oracle’s Practical Path To AI podcast episode Connecting the Dots Between Big Data and AI, team members compare the AI learning process to the human experience.

The short version: the human brain ingests countless experiences every moment. Everything that is taken in by senses is technically a piece of information or data—a note of music, a word in a book, a drop of rain, and so on. Infant brains learn from the very beginning they start taking in sensory information, and the more they encounter, the more they are able to assimilate and process, then respond in new and informed ways.

AI works similarly. The more data an AI model encounters, the more intelligent it can become. Over time, as more and more data processes through the AI model, it becomes increasingly significant. In that sense, AI models are trained by big data, just as human brains are trained by the data accumulated through multiple experiences.

And while this may all seem scary at first, there’s a definite public shift toward trusting AI-driven software. This is discussed further by Oracle’s CBG team on the podcast episode, and it all goes back to the idea of human experiences. In the digital realm, people now have the ability to document, review, rank, and track these experiences. This knowledge becomes data points in big data, thus fed into AI models which start validating or invalidating the experiences. With enough of a sample size, a determination can be made based on “a power of collective knowledge” that grows and creates this network.

However, that doesn’t mean that AI is the authority on everything, even with all the data in the world.

To hear more about this topic—and why human judgment is still a very real and very necessary part of, well, everything—listen to the entire podcast episode Connecting the Dots Between Big Data and AI and be sure to visit Oracle’s Big Data site to stay on top of the latest developments in the field of big data.

Guest author Michael Chen is a senior manager, product marketing with Oracle Analytics.

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‘Personal Data Network’ Veriglif Launches, Seeks Funds

‘Personal Data Network’ Veriglif Launches, Seeks Funds

May 22 2019

Veriglif, a New York-based company which promises to ‘unlock value for buyers, sellers & creators of personal data’, is emerging from ‘stealth’ mode and beginning a rapid push towards full commercialization.

James WilsonBacked among others by insights publisher and IIeX organizer GreenBook, Veriglif says it does not directly compete with anyone in the existing research, data and insights ecosystem but offers a ‘network of networks’ allowing consumer data to be verified and exchanged with full permission for the mutual benefit of all. The platform can validate, link, store and transact any permissioned data at the individual level including behavioral, passive, transactional, geolocation, social media and opinion data, but ensures network participants have full control over every transaction using it. As such, it promises significant improvements to targeted surveys, data augmentation, audience analytics and personalized marketing; and aims to address the ever-more challenging regulatory environment post-GDPR.

The network is built on IBM’s Hyperledger blockchain protocols, and the firm says it has worked with the IT giant and more than 100 leaders in the marketing insights & analytics value chain over the past nine months to design and build its soution. In addition to Hyperledger, it makes use of a series of automated API integrations, an AI-driven data inventory solution, and a transactional processing portal.

The firm cites ‘significant demands worth billions of dollars for verified, accurate, and permissioned consumer data that meets the requirements of increasingly stringent privacy legislation’ – and says there is currently ‘no existing platform that offers validated, privacy-compliant data linked to the same consumer across multiple suppliers that also pays incentives to the consumer’. CEO James Wilson (pictured) says the founding team of eleven industry professionals have ‘a detailed behind-the-scenes understanding of this problem, and what the solution needs to be. Veriglif will fundamentally change how the world deals with individual-level data’.

Having raised $464k to date from early stage investors, the company has just launched an equity crowdfunding campaign via WeFunder to get the solution to market – accessed via https://wefunder.com/veriglif .

GreenBook’s Lenny Murphy says his organisation’s support is in line with its mission to ‘connect supply and demand via innovative new models that can support the future growth of the industry’. He adds: ‘In this case we are applying that to the personal data supply chain by creating a new platform that we believe will solve many of the challenges with the current paradigm, while working ‘within the system’ vs. trying to replace it’.

Web site: www.veriglif.com .

All articles 2006-19 written and edited by Mel Crowther and/or Nick Thomas.

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Data & AI: The Crystal Ball into Your Future Success

Years ago, the future was much opaquer. Now, it’s tangible, visible and rising up all around us. It seems to be taking shape in real time, much of which can be attributed to innovation in data and infrastructure, across their respective and collective aspects.

As innovation in these areas accelerates, it rapidly gains in capabilities, particularly for enterprises who have reached a point of digital maturity, ensuring access to quality data and accelerated infrastructure at scale. Yet, for others, their data and analytics initiatives are still lacking. As their data continues to expand, they do not have the right building blocks in place to grow and change with it. In fact, a recent McKinsey survey of more than 500 executives found that more than 85% acknowledged they are only somewhat effective at meeting the goals they set for their data and analytics initiatives.

With both growing and mature data sets, the effects of enterprise deep learning and machine learning can be significant – automating processes, identifying trends in historical data and uncovering valuable intelligence that strengthens fast and accurate decision-making abilities – all of which can be used as a virtual crystal ball to refine predictions about the future and potentially its successes.

To do this correctly, companies should look at using their data AI and analytics capabilities to not only improve their core operations, but also to launch entirely new business models and applications. First, they must solve for problems in the way data is generated, collected, organized and acted upon. Because, while the mechanics are important, the ultimate value of data doesn’t come from merely collecting it, but acting on the insights derived from it.

The key lies in a fundamental mind shift of evolving your organization into a technology company with a data-first mentality.

In my experience, there are three certainties for every company:

  1. Your data is going to grow faster than you expected.
  2. The use cases for this data are going to change.
  3. The business is always going to expect outcomes to be delivered faster.

The first step in the journey to becoming a technology company is simplifying the infrastructure by moving from legacy data systems to a more nimble, flexible modernized data architecture that can bridge both structured and unstructured data to deliver deeper insights and performance at scale. Once consolidated onto a single, scalable, analytics platform, the pace of discovery and learning can be accelerated to drive a more accurate strategic vision for both today and tomorrow.

At Dell EMC, we are dedicated to bringing new and differentiated value and opportunities to our customers globally. We are always looking toward current and future trends and technologies that will help customers better manage and take advantage of their growing data sets with deep learning and machine learning at scale.

Dell EMC Isilon does just that.

As an industry leading scale-out network-attached storage, designed for demanding enterprise data sets, Isilon simplifies management and gives you access to all your data, scaling from tens of terabytes to tens of petabytes per cluster. We also deliver all-flash performance and file concurrency up to the millions, allowing us to support the bandwidth needs of 1000’s of GPUs running the most complex neural networks available. As a bonus, we accomplish this this very economically, with over 80% storage utilization, data compression and automated-tiering across flash and disk in a single cluster. Finally, Isilon based AI increases operational flexibility with multiprotocol support, allowing you to bring analytics to the data to accelerate AI innovation with faster cycles of learning, higher model accuracy and improved GPU utilization.

In an era of change and ongoing data expansion, creating a crystal ball for your business is not a matter of luck or fortune telling. It takes place through a focused strategy for doing more with the data you have at hand. By offering innovative new ways to store, manage, protect and use data at scale, Isilon moves customers that much closer to both becoming technology companies and future proofing their businesses.

To learn more, attend our April 1st webinar event, “Your Future Self is Calling, Will You Pick Up? with Dell EMC, NVIDIA & Mastercard. We look forward to seeing you there.

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When Three Worlds Collide: HPC, Analytics and AI

High-performance computing, data analytics and artificial intelligence are converging, and that’s good news for today’s enterprises. When people talk about high-performance computing, data analytics and artificial intelligence, they tend to treat this trio of technologies as three separate entities, each living in its own world. While that’s true to some extent, this view of disparate technologies misses the digital-transformation forest for the technology trees. That’s because these three complementary technologies are rapidly converging, and anymore, it’s hard to see where one ends and the other begins. If HPC, data analytics and AI are so different, then … READ MORE

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