CES 2019 Ramifications for Enterprise IT

Vendors and Organizations Mentioned: IBM, Ose, WindRiver, Velodyne, UV Partners, TDK Corporation, Chirp Microsystems, Qualcomm, Intel, Zigbee Alliance, Thread Group, Impossible Foods

The CES (Consumer Electronics Show) is traditionally known as the center of consumer technology. Run by the CTA (Consumer Technology Association) in Las Vegas, this show brings out enormous volumes of new technology ranging from smart cars to smart homes to smart sports equipment to smart… well, you get the picture. But within all of these announcements, there were also a number of important announcements that affect the enterprise IT world and the definition of IT that will be important for tech professionals to think about in 2019. Amalgam Insights went through hundreds of different technology press releases and announcements to find the most important announcements that will affect your professional career.

Come along with me as we look at Quantum Computing, Gender Equality, Autonomous Vehicles, Disinfected Smartphones, Low Power Virtual Reality, Neural Net Chips, Internet of Things Interoperability, and, yes, the Impossible Burger.

Quantum Computing

On January 8th, 2019, IBM announced IBM Q System One, the “first integrated universal approximate quantum computing system” designed for commercial use. From a practical perspective, this will allow R&D departments to actually have their own quantum computers. Today, the vast majority of quantum computing work is done based on remote access either to quantum computers or quantum computing emulators, which provide limits on the experimenters’ abilities to customize and configure their computing environments.

To create a quantum computing system, IBM had to bring together hardware that provided high-quality and low-error rate qubits, cryogenic equipment to cool the hardware and quantum activity, as well as the electronics, firmware, and traditional computing capabilities needed to support a quantum environment. Of course, IBM is not new to quantum computing and has been a market leader in this emerging category.

Quantum computing fundamentally matters because we are running up against the physical limits of material science that allow microprocessors to get smaller and faster, which we typically sum up as Moore’s Law. In addition, quantum computing potentially allows both for more secure encryption or the ability to quickly decrypt extremely secure technologies, depending on whether one takes a white-hat or black-hat approach. But the ramifications mean that it is important for security organizations to both start understanding quantum computing and to either stay ahead of black-hat quantum computing efforts or provide white-hat security answers to stay ahead.

Gender Equality at CES

At CES, a woman-designed sex toy originally given an innovation award (Warning: may not be Safe For Work) had its award revoked. The Ose vibrator designed by Lora DiCarlo was entered in the robotics and drone category based on its design by a robotics lab at Oregon State University and eight patents pending for a variety of robotic and biomimicry capabilities.

The product was undoubtedly risque. But CES has previously allowed virtual reality pornography to be shown within the show as well as other anatomical simulations designed for sex.

Given CES’ historical standards for other exhibitors to present similar products and objects, the revoking of this award looks biased. This is an important lesson that the answer to providing a gender-equal environment is not necessarily to simply remove all sexual content. The goal is to eliminate harassment and abuse while providing equal opportunity across gender. As long as sex is a part of consumer technology, CES needs to provide equal opportunity for all genders to present.

Autonomous Vehicles

There were a number of announcements associated with Lidar sensors and edge computing innovations. Two that got Amalgam Insights’ attention included:

WindRiver’s integration of its Chassis automotive software with its TitaniumCloud virtualization software. This announcement hints at the need for the car, as computing system, to be integrated with the cloud. This integration will be important as car manufacturers seek to upgrade car capabilities. As we continue to think about the car both as an autonomous data center of its own and set of computing and processing workloads that need to be upgraded on a regular basis, we will need to consider how the operational technologies associated with autonomous vehicles and other “Things” integrate with carrier-grade and public clouds.

Velodyne announced an end-to-end Lidar solution that includes both a hemisphere Lidar sensor called VelaDome as well as its Velia software. This launch reflects the need for hardware components and software to be integrated in the vehicle world, just as it is in the appliances and virtual machines we often use in the world of IT. This is another data point showing how autonomous vehicles are coming closer to our world of IT both in creating integrated solutions and in requiring IT-like support in the future.

Disinfected Smartphones

UV Partners announced a new product called the UV Angel Aura Clean & Charge, which combines both wireless charging with ultraviolet light disinfection. This product matters because, quite frankly, mobile phones tend to be filthy. That’s what happens when people are holding them for hours a day and rarely wash or disinfect the phones. So, this device will be useful for germophobes.

But there is also the practical aspect of being able to clean phone surfaces with this object more easily. This may lead to being able to use the phone to detect biological matter or changes more effectively without additional dirt and biocontaminants. This could make phones or other sensors more accurate in trying to detect trace elements or compounds and increase the functionality of both phones and “Things” as a result.

Low Power Virtual Reality

TDK Corporation announced its work with Qualcomm through the group company of Chirp Microsystems to improve controller tracking for mobile virtual reality and augmented reality headsets (). Most importantly, the tracking system used for these devices is only several miiliwatts, which is a small fraction of the total power within a standard smartphone battery. This compares to several hundred milliwatts for a standard optical tracking system. With this primary technology in development, both AR and VR experiences become more usable simply because they will take significantly less power to support.

This change may not sound exciting, but Amalgam Insights believes that one of the key challenges to the adoption of AR and VR is simply the battery life needed to use these applications for any extended amount of time. This breakthrough could significantly extend the life of AR and VR apps.

Artificial Intelligence

Intel made a number of chip announcements. Amalgam Insights is not a hardware analyst firm, so most of the mobile and laptop-based announcements are beyond our coverage. But the announcement that got our attention was the Intel Nervana Neural Network Processor. This chip, developed with Facebook, is developed to accelerate the detection of inference associated with the algorithmic processing of neural nets and will drive higher performance machine learning and artificial intelligence efforts.

At a time when every chip player is trying to get ahead with GPUs and TPUs, Intel is making its mark by focusing on the detection of iterative inference, which is a necessary part of the “intelligence” of AI. Amalgam Insights looks forward to seeing how the Nervana processor is made available for commercial use and as a cloud-based capability for the enterprise world.

Internet of Things Interoperability

The Zigbee Alliance and Thread Group announced completing the Dotdot 1.0 specification, which will improve interoperability across smart home devices and networks made by different vendors. By providing a standard application layer that works across a wide variety of vendors and works on an IP networking standard, Dotdot brings a level of standardization to application-level configuration, testing, and certification.

This standard is an important step forward for companies working on Smart Home devices or related Smart Office devices and seeking a common way to ensure that new devices will be able to communicate with existing device investments. Amalgam Insights looks forward to seeing how this standard revolutionizes Smart Buildings and the Future of Work.

And, the Impossible Burger

The belle of the ball, so to speak, at CES was the Impossible Burger 2.0, a soy-based protein held together by heme with iron and protein content similar to beef.

So, this is very cool, but why is this relevant to IT? First, this burger reminds us that food is now tech. Think about both how interesting and weird this is. A company has made custom proteins to build a new type of food designed to replace the taste and role of beef. Or at least that’s where they are today.

Meanwhile in the IT world, identity is increasingly based on biometrics: eyes, fingerprints, facial recognition. It is only a matter of time before either protein or DNA profiles are added to this mix. There will undoubtedly be some controversies and hiccups as this happens, but it is almost inevitable given the types of sensors we have and the evolution of DNA technologies like CRISPR that rapidly sequence and cut up DNA.

So, as we get better at replicating the nutrition and texture of meat with plant-based proteins at the same time that our physical bodies are increasingly used to provide access to our accounts… yes, this gets weird. But we’re probably five-to-ten years away from being hacked by some combination of these technbologies as the DNA, protein, and biometric worlds keep coming closer and closer together.

For now, this is just cool to watch. And the Impossible Burger 2.0 sounds like a great vegan alternative to a burger. But putting the pieces together, identity in 2030 is going to be extremely difficult to manage.

Observations on the Future of Red Hat from Red Hat Analyst Day

On November 8th, 2018, Amalgam Insights analysts Tom Petrocelli and Hyoun Park attended the Red Hat Analyst Day in Boston, MA. We had the opportunity to visit Red Hat’s Boston office in the rapidly-growing Innovation District, which has become a key tech center for enterprise technology companies. In attending this event, my goal was to learn more about the Red Hat culture that is being acquired as well as to see how Red Hat was taking on the challenges of multi-cloud management.

Throughout Red Hat’s presentations throughout the day, there was a constant theme of effective cross-selling, growing deal sizes including a record 73 deals of over $1 million in the last quarter, over 600 accounts with over $1 million in business in the last year, and increased wallet share year-over-year for top clients with 24 out of 25 of the largest clients increasing spend by an average of 15%. The current health of Red Hat is undeniable, regardless of the foibles of the public market. And the consistency of Red Hat’s focus on Open Source was undeniable across infrastructure, integration, application development, IT automation, IT optimization, and partner solutions, which demonstrated how synchronized and focused the entire Red Hat executive team presenters were, including

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Is IBM’s Acquisition of Red Hat the Biggest Acquihire of All Time?

Estimated Reading Time: 11 minutes

Internally, Amalgam Insights has been discussing why IBM chose to acquire Red Hat for $34 billion dollars fairly intensely. Our key questions included:

  • Why would IBM purchase Red Hat when they’re already partners?
  • Why purchase Red Hat when the code is Open Source?
  • Why did IBM offer a whopping $34 billion, $20 billion more than IBM currently has on hand?

As a starting point, we posit that IBM’s biggest challenge is not an inability to understand its business challenges, but a fundamental consulting mindset that starts with the top on down. By this, we mean that IBM is great at identifying and finding solutions on a project-specific basis. For instance, SoftLayer, Weather Company, Bluewolf, and Promontory Financial are all relatively recent acquisitions that made sense and were mostly applauded at the time. But even as IBM makes smart investments, IBM has either forgotten or not learned the modern rules for how to launch, develop, and maintain software businesses. At a time when software is eating everything, this is a fundamental problem that IBM needs to solve.

The real question for IBM is whether IBM can manage itself as a modern software company.

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Tom Petrocelli Provides Context for IBM’s Acquisition of Red Hat

In light of yesterday’s announcement that IBM is planning to acquire Red Hat for $34 billion, we’d like to share with you some of our recent coverage and mentions of Red Hat to provide context for this gargantuan acquisition. In February, DevOps Research Fellow Tom Petrocelli explained how Red Hat’s purchase of CoreOS was transformative for…

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Data Science Platforms News Roundup, September 2018

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Anaconda, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, DominoElastic, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.

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Why It Matters that IBM Announced Trust and Transparency Capabilities for AI


Note: This blog is a followup to Amalgam Insights’ visit to the “Change the Game” event held by IBM in New York City.

On September 19th, IBM announced its launch of a portfolio of AI trust and transparency capabilities. This announcement got Amalgam Insight’s attention because of IBM’s relevance and focus in the enterprise AI market throughout this decade.  To understand why IBM’s specific launch matters, take a step back in considering IBM’s considerable role in building out the current state of the enterprise AI market.

IBM AI in Context

Since IBM’s public launch of IBM Watson on Jeopardy! in 2011, IBM has been a market leader in enterprise artificial intelligence and spent billions of dollars in establishing both IBM Watson and AI. This has been a challenging path to travel as IBM has had to balance this market-leading innovation with the financial demands of supporting a company that brought in $107 billion in revenue in 2011 and has since seen this number shrink by almost 30%.

In addition, IBM had to balance its role as an enterprise technology company focused on the world’s largest workloads and IT challenges with launching an emerging product better suited for highly innovative startups and experimental enterprises. And IBM also faced the “cloudification” of enterprise IT in general, where the traditional top-down purchase of multi-million dollar IT portfolios is being replaced by piecemeal and business-driven purchases and consumption of best-in-breed technologies.

Seven years later, the jury is still out on how AI will ultimately end up transforming enterprises. What we do know is that a variety of branches of AI are emerging, including

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Todd Maddox Ph.D.’s Top Four Scientific Observations on HR Tech 2018

HR Tech gets bigger and bigger every year. HR Tech 2018 was no exception. It broke all of the previous records. More importantly, the quality of the offerings, presentations and savvy of the clients continues to grow. I had a great time at HR Tech 2018, and I am already looking forward to 2019. It…

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Data Science Platforms News Roundup, June 2018

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include:

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What Data Science Platform Suits Your Organization’s Needs?

This summer, my Amalgam Insights colleague Hyoun Park and I will be teaming up to address that question. When it comes to data science platforms, there’s no such thing as “one size fits all.” We are writing this landscape because understanding the processes of scaling data science beyond individual experiments and integrating it into your business is difficult. By breaking down the key characteristics of the data science platform market, this landscape will help potential buyers choose the appropriate platform for your organizational needs. We will examine the following questions that serve as key differentiators to determine appropriate data science platform purchasing solutions to figure out which characteristics, functionalities, and policies differentiate platforms supporting introductory data science workflows from those supporting scaled-up enterprise-grade workflows.

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Lynne Baer: Clarifying Data Science Platforms for Business

Word cloud of data science software and terms

My name is Lynne Baer, and I’ll be covering the world of data science software for Amalgam Insights. I’ll investigate data science platforms and apps to solve the puzzle of getting the right tools to the right people and organizations.

“Data science” is on the tip of every executive’s tongue right now. The idea that new business initiatives (and improvements to existing ones) can be found in the data a company is already collecting is compelling. Perhaps your organization has already dipped its toes in the data discovery and analysis waters – your employees may be managing your company’s data in Informatica, or performing statistical analysis in Statistica, or experimenting with Tableau to transform data into visualizations.

But what is a Data Science Platform? Right now, if you’re looking to buy software for your company to do data science-related tasks, it’s difficult to know which applications will actually suit your needs. Do you already have a data workflow you’d like to build on, or are you looking to the structure of an end-to-end platform to set your data science initiative up for success? How do you coordinate a team of data scientists to take better advantages of existing resources they’ve already created? Do you have coders in-house already who can work with a platform designed for people writing in Python, R, Scala, Julia? Are there more user-friendly tools out there your company can use if you don’t? What do you do if some of your data requires tighter security protocols around it? Or if some of your data models themselves are proprietary and/or confidential?

All of these questions are part and parcel of the big one: How can companies tell what makes a good data science platform for their needs before investing time and money? Are traditional enterprise software vendors like IBM, Microsoft, SAP, SAS dependable in this space? What about companies like Alteryx, H2O.ai, KNIME, RapidMiner? Other popular platforms under consideration should also include Anaconda, Angoss (recently acquired by Datawatch), Domino, Databricks, Dataiku, MapR, Mathworks, Teradata, TIBCO. And then there’s new startups like Sentenai, focused on streaming sensor data, and slightly more established companies like Cloudera looking to expand from their existing offerings.

Over the next several months, I’ll be digging deeply to answer these questions, speaking with vendors, users, and investors in the data science market. I would love to speak with you, and I look forward to continuing this discussion. And if you’ll be at Alteryx Inspire in June, I’ll see you there.