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Jamie Dimon is Afraid

Jamie Dimon is Afraid

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First they ignore you, then they ridicule you, then they fight you, goes the saying.

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JP Morgan (not Jamie Dimon)

Jamie Dimon is CEO of JP Morgan Chase, arguably the most important money center bank. He recently called Bitcoin a “fraud”, heaping ridicule upon, and fighting with Bitcoin as well, with a single phrase. Because he is “afraid”.

It certainly is not a fraud. It is in fact a more advanced form of money our fiat currency; it is Money 3.0.

JP Morgan Chase, the combined bank, has been around since 1799 (Chase portion), over 200 years, and has a market cap of around $331 billion. Bitcoin and other cryptocurrencies have been around for less than 9 years and have a market cap of around $150 billion. That is over 1/2 of Visa’s market cap.

It would not be a surprise to see the total cryptocurrency market cap exceed that of JPM by the end of the decade.

Why did Jamie call Bitcoin a “fraud”? Because he knows he has to fight it. Cryptocurrency, or the Internet of Money as Andreas Antonopoulos likes to call it, is a steamroller that will severely disrupt banking as practiced today.

Who is really happy with their bank? You think it is your money you have in your bank, right? No, you have lent your money to the bank so they can lend it to others and make hefty profits on the spread. Why don’t you lend directly? You can use Bitcoin to do that, or dollars as well, through direct lending sites.

Try taking all of your savings and checking funds out of your bank tomorrow in cash. They probably won’t let you if you have more than $10,000. They have know-your-customer regulations and anti-money laundering regulations and many other restrictions. They don’t keep much cash on hand. You might hear “we can give you $2500 today, then come back next week”.

In his book Internet of Money, Antonopoulos tells a story about how he gave a talk at the Deutsche Bank. This is the equivalent of the Federal Reserve, for Germany, their national bank. He asked to be paid in Bitcoin; they couldn’t do it. Ok, could they send him dollars to his US bank. Okay SWIFT code, etc. Without going through all the gory details, the whole transaction took 16 days! His bank in the U.S. said, who is the Deutsche Bank?

You can easily transfer a few thousand dollars or more via Bitcoin in an hour or so. If you haven’t used Bitcoin yet, now is the time to learn about it, it will only grow in importance.

The real frauds here? JP Morgan and Jamie Dimon. They have paid over $30 billion in fines for multiple financial crimes since the Great Recession. Around 10% of their company market cap. But that huge sum is less than half the market cap of Bitcoin today.

http://www.dividend.com/dividend-education/a-brief-history-of-jp-morgans-massive-fines-jpm/

IoT is the superset trend

Secure IoT is the superset trend in tech, the ultimate hashtag soup, here’s why

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There are more Things than anything else! And they’re going online in droves, adding capability but also vulnerability, and making the Secure Internet of Things (IoT), the super set of the technology trends. Secure IoT is the place with the most interdisciplinary, and therefore, the most difficult challenges. If you believe, as you should, that no-compromise cybersecurity is table stakes, then IoT means “secure IoT” and IoT by itself becomes the ultimate hashtag soup. Here is why.

IoT and Security are End to End

IoT and security require an end-to-end mindset. Today’s solutions are fragmented. Integrating them is hard and requires blending of many technologies and algorithms. It requires a team with a very broad set of expertise who have managed to understand each other and employ each other’s strengths.

#IoT

Things come in many shapes and sizes. We have to make a distinction between Small-t things, sensors and micro devices, Big-T Things, which often form meta things or Systems, or All-caps THINGS, meta systems and the so-called Distributed Autonomous Organizations (DAOs). Managing them in a coherent manner requires an expertise rubber band that has to stretch across from the microscopic to the astronomic:

  • Size: Tiny Things to large Things
  • Data: Simple signals to structured data to context-sensitive streams
  • Mobility: stationary Things to Things that are carried by people to self-propelled Things
  • Autonomy: Things that are controlled by people to the so-called DAOs
#Cybersecurity

Security is not just a technology and practice but a non-negotiable requirement that cuts across all aspect of tech. Small-t things require a whole a new approach to device and data security all the way up the supply chain, a difficult technological and operational task an area of intense innovation, for example by blending AI, IoT, and Cybersecurity disciplines or pursuing entirely new approaches.  Big-T Things remain hard to secure but have more built-in resources and processing capability, making them eligible to  be handled like existing IT systems.

Physical #Security

Things, scattered around, are exposed to physical tampering so it is important to physically secure them and detect any unauthorized physical access or near-field or far-field monitoring. As is often said, “security is security” so physical security and cybersecurity must be viewed and implemented as one thing: all under the umbrella of risk and threat management and #sustainability.

#Mobility

Many Things move, either by themselves (like #drones or robots) or carried by other things (pick a form of transportation) or by people (personal device or #wearables). Mobility issues from initial on-boarding to authenticated secure transmission to data streaming and data semantics to over-the-air (OTA) updates all become critical in IoT.

#Blockchain

Distributed Things that generate lots of data, require secure authenticated communications, are possibly subject to compliance regulations, may need digital rights management (#DRM), or need verifiable delivery… are the kind of use case that Blockchain technology can address very well. IoT applications are often natural candidates. (see also the OrionX Download podcast series on Blockchain.)

#BigData

Connected devices generate data. Making sense of that data requires Big Data practices and technologies: data lakes, data science, storage, search, apps, etc.

#AI

When you have so much data, invariably you will find an opportunity for AI (see the OrionX/ai page for a repository of reports), either via #MachineLearning which uses the data, or #ExpertSystems for policy management which decides what to do about the insight that the data generates. AI framewroks, especially the #DeepLearning variety, are #HPC oriented and require knowledge of HPC systems and algorithms.

There will come a time when another emerging trend, #QuantumComputing, will be useful for AI and cybersecurity.

#Robotics

If Things are smart enough and can move, they become robots. Distributed Autonomous Objects (DAOs) use and generate data and require a host of security, processing, control, policy, etc.

#Cloud

All of this processing happens in the cloud somewhere, so all cloud technologies come into play. The required expertise will cover the gamut: app development, streaming data, dev-ops, elasticity, service level assurance, API management, microservices, data storage, multi-cloud reliability, etc.

Legal Framework

IoT provides new kinds of information extraction, manipulation, and control. Just the AI and Robotics components are enough to pose a challenge for existing legal systems. Ultimately, it requires an ethics framework that can be used to create a proper legal system all the while ensuring widespread communication so organizational structures and culture can keep up with it.

Summary

Not every IoT deployment will need all of the above, but as you try to stitch together a strategy for your IoT project, it is imperative to do so with a big picture in mind. IoT is the essence of #DigitalTransformation and touches all aspects of your organization. It’s quite a challenge to make it seamless, and you’ll need specialists, generalists, and not just analysts but also “synthesists”.

In the OrionX 2016 Technology Issues and Predictions blog, we said “If you missed the boat on cloud, you can’t miss it on IoT too”, and “IoT is where Big Data Analytics, Cognitive Computing, and Machine Learning come together for entirely new ways of managing business processes.” Later, in February of 2016, my colleague Stephen Perrenod’s blog IoT: The Ultimate Convergence, further set the scene. Today, we see those predictions have become reality while IoT is poised for even more convergence.

We will cover these topics in future episodes of The OrionX Download podcast, identifying the salient points. As usual, we will go one or two levels below the surface to understand not just what these technologies do, but how they do it and how they came about. That will in turn, help put new developments in perspective and explain why they may or may not address a pain point.

 

 

 

 

 

data-loss-drain

Data Loss, A Big Problem in Digital Transformation

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Data loss is a subject whose time has come! Digital Transformation is upon us and with it the beginnings of a vibrant data economy. Extracting, manipulating, and making sense of the information content from physical assets and business processes is the essence of this new era. With it, come two growing problems: downtime, and data loss.

Downtime

Downtime is interruption, the opposite of business continuity. In a digital enterprise, more than ever, business continuity depends on high availability (HA) and disaster recovery (DR). Both of those depend on fast data replication.

Downtime has been studied for decades. Preventing it is better understood, but it continues to happen from time to time.  Sometimes it’s kept quiet and sometimes it’s spectacularly public. But it  always causes damage. When a major airline experienced IT failures recently, it not only made the headlines but it cost the company millions of dollars in lost revenue, and perhaps also in damaged brand image. Search online for “IT outage” and you will see sobering stories. Downtime is serious business.

Data Loss

Digital organizations are at the nexus of massive data flowing in and out of them. As they become more common, the value of an organization’s data assets become correspondingly more significant. As a result, one aspect of downtime that is getting more attention is the value of the data that is lost or not captured.

What is the cost to your organization if you lose your payroll, or your customer transactions for a day’s business? These painful types of data loss are harder to quantify and more importantly, ones your company doesn’t want to encounter.

OrionX Research Paper

In our recent research paper, The Cost of Data Loss and How to Avoid It, my colleague Dan Olds examines the drivers and cost categories of data loss. He has also looked at what solutions are available to prevent data loss, and highlights a particularly effective one that is based on Open Source technologies and has been downloaded an incredible 1.7 million times.

Browse through all of our free Constellation papers, or go directly to  the full Data Loss paper to learn more.

New podcast for big ideas in tech

New podcast for big ideas in tech

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Here at OrionX.net, our research agenda is driven by the latest developments in technology. We are also fortunate to work with many tech leaders in nearly every part of the “stack”, from chips to apps, who are driving the development of such technologies.

In recent years, we have had projects in Cryptocurrencies, IoT, AI, Cybersecurity, HPC, Cloud, Data Center, … and often a combination of them. Doing this for several years has given us a unique perspective. Our clients see value in our work since it helps them connect the dots better, impacts their investment decisions and the options they consider, and assists them in communicating their vision and strategies more effectively.

We wanted to bring that unique perspective and insight directly to you. “Simplifying the big ideas in technology” is how we’d like to think of it.

Thus was born our first podcast/slidecast series: The OrionX Download™

TheOrionXDownload-podcast-cohosts
The OrionX Download is both a video slidecast (visuals but no talking heads) and an audio podcast in case you’d like to listen to it.

Every two weeks, co-hosts Dan Olds and Shahin Khan, and other OrionX analysts, discuss some of the latest and most important advances in technology. If our research has a specially interesting finding, we’ll invite guests to probe the subject and add their take.

Please give it a try and let us know what we can do better or if you have a specific question or topic that you’d like us to cover.

Top-10 Data Center Predictions for 2017

Top-10 Data Center Predictions for 2017

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A variation of this article was originally published in The Register, “Wow, what an incredible 12 months: 2017’s data center year in review”, Feb 6, 2017.

The data center market is hot, especially now that we are getting a raft of new stuff, from promising non-Intel chips and system architectures to power and cooling optimizations to new applications in Analytics, IoT, and Artificial Intelligence.

Here is my Top-10 Data Center Predictions for 2017:

1) Data center optimization is here

Data centers are information factories with lots of components and moving parts. There was a time when companies started becoming much more complex, which fueled the massive enterprise resource planning market. Managing everything in the data center is in a similar place. To automate, monitor, troubleshoot, plan, optimize, cost-contain, report, etc, is a giant task, and it is good to see new apps in this area.

Data center infrastructure management provides visibility into and helps control IT. Once it is deployed, you’ll wonder how you did without it. Some day, it will be one cohesive thing, but for now, because it’s such a big task, there will be several companies addressing different parts of it.

2) Azure will grow faster than AWS

Cloud is the big wave, of course, and almost anything that touches it is on the right side of history. So, private and hybrid clouds will grow nicely and they will even temper the growth of public clouds. But the growth of public clouds will continue to impress, despite increasing recognition that they are not the cheapest option, especially for the mid-size users.

AWS will lead again, capturing most new apps. However, Azure will grow faster, on the strength of both landing new apps and also bringing along existing apps where Microsoft maintains a significant footprint.

Moving Exchange, Office, and other apps to the cloud, in addition to operating lots of regional data centers and having lots of local feet on the ground will help.

Some of the same dynamics will help Oracle show a strong hand and get close to Google and IBM. And large telcos will stay very much in the game. Smaller players will persevere and even grow, but they will also start to realize that public clouds are their supplier or partner, not their competition! It will be cheaper for them to OEM services from bigger players, or offer joint services, than to build and maintain their own public cloud.

3) Great Wall of persistent memory will become a thing

Just as the hottest thing in enterprise becomes Big Data, we find that the most expensive part of computing is moving all that data around. Naturally, we start seeing what OrionX calls “In-Situ Processing” (see page 4 of the report in the link): instead of data going to compute, compute would go to data, processing it locally wherever data happens to be.

But as the gap between CPU speed and storage speed separates apps and data, memory becomes the bottleneck. In comes storage class memory (mostly flash, with a nod to other promising technologies), getting larger, faster and cheaper. So, we will see examples of apps using a Great Wall of persistent memory, built by hardware and software solutions that bridge the size/speed/cost gap between traditional storage and DRAM. Eventually, we expect programming languages to naturally support byte-addressable persistent memory.

4) System vendors will announce racks, not servers

Vendors already configure and sell racks, but they often populate them with servers that are designed as if they’d be used stand-alone. Vendors with rack-level thinking have been doing better because designing the rack vs the single node lets them add value to the rack while removing unneeded value from server nodes.

So server vendors will start thinking of a rack, not a single-node, as the system they design and sell. Intel’s Rack Scale Architecture has been on the right track, a real competitive advantage, and an indication of how traditional server vendors must adapt. The server rack will become the next level of integration and what a “typical system” will look like. Going forward, multi-rack systems are where server vendors have a shot at adding real value. HPC vendors have long been there.

5) Server revenue growth will be lower than GDP growth

Traditional enterprise apps – the bulk of what runs on servers – will show that they have access to enough compute capacity already. Most of that work is transactional, so their growth is correlated with the growth in GDP, minus efficiencies in processing.

New apps, on the other hand, are hungry, but they are much more distributed, more focused on mobile clients, and more amenable to what OrionX calls “High-Density Processing”: algorithms that have a high ops/bytes ratio running on hardware that provides similarly high ops/byte capability – ie, compute accelerators like GPUs, FPGAs, vector processors, manycore CPUs, ASICs, and new chips on the horizon.

On top of that, there will be more In-Situ Processing: processing the data wherever it happens to be, say locally on the client vs sending it around to the backend. This will be made easier by the significant rise in client-side computing power and more capable data center switches and storage nodes that can do a lot of local processing.

We will also continue to see cloud computing and virtualization eliminate idle servers and increase the utilization rates of existing systems.

Finally, commoditization of servers and racks, driven by fewer-but-larger buyers and standardization efforts like the Open Compute Project, put pressure on server costs and limit the areas in which server vendors can add value. The old adage in servers: “I know how to build it so it costs $1m, but don’t know how to build it so it’s worth $1m” will be true more than ever.

These will all combine to keep server revenues in check. We will see 5G’s wow-speeds but modest roll-out, and though it can drive a jump in video and some server-heavy apps, we’ll have to wait a bit longer.

6) OpenPOWER will emerge as the viable alternative to x86

The real battle in server architecture will be between Intel’s in-house coalition and what my colleague Dan Olds has called the Rebel Alliance: IBM’s OpenPower industry coalition. Intel brings its all-star team: Xeon Phi, Altera, Omni-Path (plus Nervana/Movidius) and Lustre, while OpenPower counters with a dream team if its own: POWER, Nvidia, Xilinx, and Mellanox (plus TrueNorth) and GPFS (Spectrum Scale). Then there is Watson which will become more visible as a differentiator, and a series of acquisitions by both camps as they fill various gaps.

The all-in-house model promises seamless integration and consistent design, while the extended team offers a best-of-breed approach. Both have merits. Both camps are pretty formidable. And there is real differentiation in strategy, design, and implementation. Competition is good.

7) Beware IoT Security

One day in the not too distant future, your fancy car will break down on the highway for no apparent reason. It will turn out that the auto entertainment system launched a denial of service attack on the rest of the car, in a bold attempt to gain control. It even convinced the nav system to throw in with it! This is a joke, of course, but could become all too real if/when human and AI hackers get involved.

IoT is coming, and with it will come all sorts of security issues. Do you know where your connected devices are? Can you really trust them?

8) More chips than Vegas; riskier too

For the first time in decades, there is a real market opening for new chips. What drove this includes:

  • The emergence of new apps, led by AI and IoT. The part of AI that is computationally interesting and different is High-Performance AI, since it intersects with HPC. On the IoT side, backend apps are typically Analytics to make sense of sensor data. These new apps will run where they can run better/faster/cheaper. They will be too new to be burdened by any allegiance or bonds to a particular chip.
  • The fact that many existing apps have no clue what hardware they run on, and operate on the upper layers of a tall stack.
  • The possibility to build a complete software stack from open source software components.
    The presence of very large customers like cloud providers or top supercomputing sites. They buy in seriously large volumes and have the wherewithal to build the necessary software stack, so they can afford to pick a new chip and bolster, if not guarantee, its viability.

This will be a year when many new chips became available and tested, and there is a pretty long list of them, showing just how big the opportunity is, how eager investors must have been to not miss out, and how many different angles there are.

In addition to AI, there are a few important general-purpose chips being built. The coolest one is by startup Rex Computing, which is working on a chip for exascale, focused on very high compute-power/electric-power ratios. Qualcomm and Caviuum have manycore ARM processors, Oracle is advancing SPARC quite well, IBM’s POWER continues to look very interesting, and Intel and AMD push the X86 envelope nicely.

With AI chips, Intel already has acquired Nervana and Movidius. Google has its TPU, and IBM its neuromorphic chip, TrueNorth. Other AI chip efforts include Mobileye (the company is being bought by Intel since this article was written), Graphcore, BrainChip, TeraDeep, KnuEdge, Wave Computing, and Horizon Robotics. In addition, there are several well-publicized and respected projects like NeuRAM3, P-Neuro, SpiNNaker, Eyeriss, and krtkl going after different parts of the market. That’s a lot of chips, but most of these bets, of course, won’t pay off.

9) ARM server market share will stay below 3 per cent

Speaking of chips, ARM servers will remain important but elusive. They will continue to make a lot of noise and point to significant wins and systems, but fail to move the needle when it comes to revenue market share in 2017.

As a long-term play, ARM is an important phenomenon in the server market – more so now with the backing of SoftBank, a much larger company apparently intent on investing and building, and various supercomputing and cloud projects that are great proving grounds.

But at the end, you need differentiation and ARM has the same problem against X86 as Intel’s Atom had against ARM. Atom did not differentiate vs ARM any more than ARM is differentiating vs Xeon.

Most systems end up being really good at something, however, and there are new apps and an open-source stack to support existing apps, which will help find specific workloads where ARM implementations could shine. That will help the differentiation become more than “it’s not X86”.

10) Is it an app, or is it a fabric? More cloud fabrics introduced

What is going on with big new apps? They keep getting more modular (microservices), more elastic (scale out), and more real-time (streaming). They’ve become their own large graph, in the computer science sense, and even more so with IoT (sensors everywhere plus In-Situ Processing).

As apps became services, they began resembling a network of interacting modules, depending on each other and evolving together. When the number of interacting pieces keeps increasing, you’ve got yourself a fabric. But as an app, it’s the kind of fabric that evolves and has an overall purpose (semantic web).

Among engineering disciplines, software engineering already doesn’t have a great reputation for predictability and maintainability. More modularity is not going to help with that.

But efforts to manage interdependence and application evolution have already created standards for structured modularity like the OSGi Alliance for Java. Smart organizations have had ways to reduce future problems (technical debt) from the get-go. So, it will be nice to see that type of methodology get better recognized and better generalized.

OK, we stop here now, leaving several interesting topics for later.

What do you think?

OrionX Research Framework

Research from OrionX

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Today, we are announcing the OrionX Constellation™ research framework as part of our strategy services.

Paraphrasing what I wrote in my last blog when we launched 8 packaged solutions:

One way to see OrionX is as a partner that can help you with content, context, and dissemination. OrionX Strategy can create the right content because it conducts deep analysis and provides a solid perspective in growth segments like Cloud, HPC, IoT, Artifical Intelligence, etc. OrionX Marketing can put that content in the right context by aligning it with your business objectives and customer needs. And OrionX PR can build on that with new content and packaging to pursue the right dissemination, whether by doing the PR work itself or by working with your existing PR resources.

Even if you only use one of the services, the results are more effective because strategy is aware of market positioning and communication requirements; marketing benefits from deep industry knowledge and technical depth; and PR can set a reliable trajectory armed with a coherent content strategy.

Constellation™: Research from OrionX

The Constellation research is all about high quality content. That requires the ability to go deep on technologies, market trends, and customer sentiment. Over the past two years, we have built such a capability.

Given the stellar connotations of the name Orion (not to mention our partner Stephen Perrenod’s book and blog about quantum and astro- physics), it is natural that we would see the players in each market segment as stars in a constellation. Constellations interact and evolve, and stars are of varying size and brightness, their respective positions dependent on cosmic (market) forces and your (customers’) point of view. Capturing that reality in a relatively simple framework has been an exciting achievement that we are proud to unveil.

OrionX Constellation Research Model

Types of Data

The OrionX strategy process collects and organizes data in three categories: market, customer, and product. The Constellation process further divides that into two parameters in each category:

  • Market presence and trends: a vendor’s presence in the segment and its ability to shape or embrace market trends
  • Customer needs and readiness: a typical customer’s current needs and readiness to adopt a particular product
  • Product capabilities and roadmap: a product’s existing capabilities and competitive standing as well as its expected enhancements and replacements in the future

This data is then analyzed to identify salient points which are in turn synthesized into the coherent insights that are necessary for a clear understanding of the dynamics at play and effective market conversations and change management.

Types of Report

The resulting content leads to five types of reports that capture the OrionX perspective on a segment. We call these the five Es:

  • Event: industry milestones and periodic or seasonal topics)
  • Evolution: historical perspective on a technology or market as a way of defining the segment and understanding the parameters that impact customer sentiment
  • Environment: the players (stars) in the segment that is in focus
  • Evaluation: how customer needs have evolved and how they evaluate (or should evaluate) the products in a segment
  • Excellence: how OrionX analysts see the segment and score each vendor according to six parameters in the three categories of data, two parameters in each category.

OrionX Constellation Decision Tool

Visualizing six distinct parameters, each with its own axis, in three categories cannot be done with the traditional 2×2 or 3×3 diagrams. Indeed, existing models in the industry do quite a good job of providing simple diagrams that rank vendors in a segment.

To simplify this visualization, we have developed the OrionX Constellation Decision Tool. This diagram effectively visualizes the six parameters that capture the core of technology customers’ selection process. (A future blog will drill down into the diagram.)

Sample Reports

While OrionX is not a typical industry analyst firm, we will offer samples of our reports from time to time. We hope they are useful to you and provide a glimpse of our work. They will complement our blog.

As always, thank you for your support and friendship. Please track our progress on social media or contact us for a free consultation or with any comments. We’d love to hear from you.

OrionX 2016 Technology Issues and Predictions

OrionX 2016 Technology Issues and Predictions

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Here at OrionX.net, we are fortunate to work with tech leaders across several industries and geographies, serving markets in Mobile, Social, Cloud, and Big Data (including Analytics, Cognitive Computing, IoT, Machine Learning, Semantic Web, etc.), and focused on pretty much every part of the “stack”, from chips to apps and everything in between. Doing this for several years has given us a privileged perspective. We spent some time to discuss what we are seeing and to capture some of the trends in this blog: our 2016 technology issues and predictions. We cut it at 17 but we hope it’s a quick read that you find worthwhile. Let us know if you’d like to discuss any of the items or the companies that are driving them.

1- Energy technology, risk management, and climate change refashion the world

Energy is arguably the most important industry on the planet. Advances in energy efficiency and sustainable energy sources, combined with the debate and observations of climate change, and new ways of managing capacity risk are coming together to have a profound impact on the social and political structure of the world, as indicated by the Paris Agreement and the recent collapse in energy prices. These trends will deepen into 2016.

2- Cryptocurrencies drive modernization of money (the original virtualization technology)

Money was the original virtualization technology! It decoupled value from goods, simplified commerce, and enabled the service economy. Free from the limitations of physical money, cryptocurrencies can take a fresh approach to simplifying how value (and ultimately trust, in a financial sense) is represented, modified, transferred, and guaranteed in a self-regulated manner. While none of the existing implementations accomplish that, they are getting better understood and the ecosystem built around them will point the way toward a true digital currency.

3- Autonomous tech remains a fantasy, technical complexity is in fleet networks, and all are subordinate to the legal framework

Whether flying, driving, walking, sailing, or swimming, drones and robots of all kinds are increasingly common. Full autonomy will remain a fantasy except for very well defined and constrained use cases. Commercial success favors technologies that aim to augment a human operator. The technology complexity is not in getting one of them to do an acceptable job, but in managing fleets of them as a single network. But everything will be subordinate to an evolving and complex legal framework.

4- Quantum computing moves beyond “is it QC?” to “What can it do?”

A whole new approach to computing (as in, not binary any more), quantum computing is as promising as it is unproven. Quantum computing goes beyond Moore’s law since every quantum bit (qubit) doubles the computational power, similar to the famous wheat and chessboard problem. So the payoff is huge, even though it is, for now, expensive, unproven, and difficult to use. But new players will become more visible, early use cases and gaps will become better defined, new use cases will be identified, and a short stack will emerge to ease programming. This is reminiscent of the early days of computing so a visit to the Computer History Museum would be a good recalibrating experience.

5- The “gig economy” continues to grow as work and labor are better matched

The changing nature of work and traditional jobs received substantial coverage in 2015. The prospect of artificial intelligence that could actually work is causing fears of wholesale elimination of jobs and management layers. On the other hand, employers routinely have difficulty finding talent, and employees routinely have difficulty staying engaged. There is a structural problem here. The “sharing economy” is one approach, albeit legally challenged in the short term. But the freelance and outsourcing approach is alive and well and thriving. In this model, everything is either an activity-sliced project, or a time-sliced process, to be performed by the most suitable internal or external resources. Already, in Silicon Valley, it is common to see people carrying 2-3 business cards as they match their skills and passions to their work and livelihood in a more flexible way than the elusive “permanent” full-time job.

6- Design thinking becomes the new driver of customer-centric business transformation

With the tectonic shifts in technology, demographic, and globalization, companies must transform or else. Design thinking is a good way to bring customer-centricity further into a company and ignite employees’ creativity, going beyond traditional “data driven needs analysis.” What is different this time is the intimate integration of arts and sciences. What remains the same is the sheer difficulty of translating complex user needs to products that are simple but not simplistic, and beautiful yet functional.

7- IoT: if you missed the boat on cloud, you can’t miss it on IoT too

Old guard IT vendors will have the upper hand over new Cloud leaders as they all rush to claim IoT leadership. IoT is where Big Data Analytics, Cognitive Computing, and Machine Learning come together for entirely new ways of managing business processes. In its current (emerging) condition, IoT requires a lot more vertical specialization, professional services, and “solution-selling” than cloud computing did when it was in its relative infancy. This gives traditional big (and even small) IT vendors a chance to drive and define the terms of competition, possibly controlling the choice of cloud/software-stack.

8- Security: Cloud-native, Micro-zones, and brand new strategies

Cybercrime is big business and any organization with digital assets is vulnerable to attack. As Cloud and Mobile weaken IT’s control and IoT adds many more points of vulnerability, new strategies are needed. Cloud-native security technologies will include those that redirect traffic through SaaS-based filters, Micro-Zones to permeate security throughout an app, and brand new approaches to data security.

9- Cloud computing drives further consolidation in hardware

In any value chain, a vendor must decide what value it offers and to whom. With cloud computing, the IT value chain has been disrupted. What used to be a system is now a piece of some cloud somewhere. As the real growth moves to “as-a-service” procurements, there will be fewer but bigger buyers of raw technology who drive hardware design towards scale and commoditization.

10- Composable infrastructure matures, leading to “Data Center as a System”

The computing industry was down the path of hardware partitioning when virtualization took over, and dynamic reconfiguration of hardware resources took a backseat to manipulating software containers. Infrastructure-as-code, composable infrastructure, converged infrastructure, and rack-optimized designs expand that concept. But container reconfiguration is insufficient at scale, and what is needed is hardware reconfiguration across the data center. That is the next frontier and the technologies to enable it are coming.

11- Mobile devices move towards OS-as-a-Service

Mobile devices are now sufficiently capable that new features may or may not be needed by all users and new OS revs often slow down the device. Even with free upgrades and pre-downloaded OS revs, it is hard to make customers upgrade, while power users jailbreak and get the new features on an old OS. Over time, new capabilities will be provided via more modular dynamically loaded OS services, essentially a new class of apps that are deeply integrated into the OS, to be downloaded on demand.

12- Social Media drives the Analytics Frontier

Nowhere are the demands for scale, flexibility and effectiveness for analytics greater than in social media. This is far beyond Web Analytics. The seven largest “populations” in the world are Google, China, India, Facebook, WhatsApp, WeChat and Baidu, in approximately that order, not to mention Amazon, Apple, Samsung, and several others, plus many important commercial and government applications that rely on social media datasets. Searching through such large datasets with very large numbers of images, social commentary, and complex network relationships stresses the analytical algorithms far beyond anything ever seen before. The pace of algorithmic development for data analytics and for machine intelligence will accelerate, increasingly shaped by social media requirements.

13- Technical Debt continues to accumulate, raising the cost of eventual modernization

Legacy modernization will get more attention as micro-services, data-flow, and scale-out elasticity become established. But long-term, software engineering is in dire need of the predictability and maintainability that is associated with other engineering disciplines. That need is not going away and may very well require a wholesale new methodology for programming. In the meantime, technologies that help automate software modernization, or enable modular maintainability, will gain traction.

14- Tools emerge to relieve the DB-DevOps squeeze

The technical and operational burden on developers has been growing. It is not sustainable. NoSQL databases removed the time-delay and complexity of a data schema at the expense of more complex codes, pulling developers closer to data management and persistence issues. DevOps, on the other hand, has pulled developers closer to the actual deployment and operation of apps with the associated networking, resource allocation, and quality-of-service (QoS) issues. This is another “rubber band” that cannot stretch much more. As cloud adoption continues, development, deployment, and operations will become more synchronized enabling more automation.

15- In-memory computing redefines straight-through apps

The idea of a “memory-only architecture” dates back several decades. New super-large memory systems are finally making it possible to hold entire data sets in memory. Combine this with Flash (and other emerging storage-class memory technologies) and you have the recipe for entirely new ways of achieving near-real-time/straight-through processing.

16- Multi-cloud will be used as a single cloud

Small and mid-size public cloud providers will form coalitions around a large market leader to offer enterprise customers the flexibility of cloud without the lock-in and the risk of having a single supplier for a given app. This opens the door for transparently running a single app across multiple public clouds at the same time.

17- Binary compatibility cracks

It’s been years since most app developers needed to know what CPU their app runs on, since they work on the higher levels of a tall software stack. Porting code sill requires time and effort but for elastic/stateless cloud apps, the work is to make sure the software stack is there and works as expected. But the emergence of large cloud providers is changing the dynamics. They have the wherewithal to port any system software to any CPU thus assuring a rich software infrastructure. And they need to differentiate and cut costs. We are already seeing GPUs in cloud offerings and FPGAs embedded in CPUs. We will also see the first examples of special cloud regions based on one or more of ARM, OpenPower, MIPS, and SPARC. Large providers can now offer a usable cloud infrastructure using any hardware that is differentiated and economically viable, free from the requirement of binary compatibility.

The Big Rules of Big Data

The Big Rules of Big Data

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Data is good. Big Data is big, but what does it take to make it good?

All else being equal, it’s better to have data and dismiss it than not have it and miss out.  No data, and you’re driving blind. But too much data, and you might as well be driving blind. Sensory overload!

The digitization of human life is just beginning. Information Age is coming and is changing everything. So the best days of Data are ahead of it. We’d better get used to data, lots of it.

Here at OrionX.net, We’ve done several projects to help define business and solution strategies in Big Data, IoT, and Cloud markets. Along the way, some rules and truisms have emerged.

Here is a snapshot of my Big Rules of Big Data (after Big Rules of Marketing, and Big Rules of Competitive Intelligence).

1- Data is cheaper to keep than to delete. Multiple copies, in fact. #NoDelete

In a way, Big Data is enabled by the economics of keeping it around. Nobody dares delete anything because it’s cheap enough to keep, you never know if you’ll need it later, and there may be legal consequences in deleting it.

2- Whatever caused you to collect data will cause you to collect a lot more data. #PlanForScale

Most data collection is focused on ongoing activities so it’s streaming in. Furthermore, as you learn what to do with data, the appetite for even more data usually grows.


If content is king
          context is kingdom!

3- Big Data systems start small, show promise, go big. #NoMiddle

There are few mid-size Big Data deployments. Once the proof of concept for a project looks promising, they go big and then grow incrementally from there, while spawning new projects.

4- Data must flow to be valuable. Just how valuable is a function of context. #Workflow

Sitting data is an idle asset that is likely depreciating in value. And some contexts are more valuable than others. Think of Big Data as workflow and consider that if content is king, then context is kingdom.

5- Never assume that you know what is cause and what is effect. #ConfirmationBias

In most cases where using Big Data is worth the effort, cause and effect relationships are complex, the data is incomplete, and the users’ biases get in the way.


Reminds me of an epigraph I read years ago:
          “If there is a will to condemn, the evidence will be found.”
6- The ratio of relevant data to irrelevant data will asymptotically approach zero. #Haystack

One way to say this is: there’s only one needle, and lots of haystack. The more data you collect, the more haystack you’re adding. But the real point here is that for a given context, irrelevant data accumulates faster than relevant data.

7- The ultimate purpose of analysis is synthesis.  #Synthetics

When you’re done with analytics, you’re going to want “synthetics”! This is where Machine Learning and Cognitive Computing come in, but also the kind of lateral thinking and connecting-the-dots that only humans seem able to do.

8- Time = Money = Data. There is always a context in which a piece of data is valuable. #ReturnOnData

How valuable is your data and how rapidly does it lose its value? Data is an asset and while it can appreciate in value, it usually depreciates as new data displaces old data and as historical data becomes less likely to be relevant. What is the “interest rate” for your data?

9- Volume-Velocity-Variety-Value, meet Irreproducible-Irrelevant-Incomplete-Incorrect. #4Vs4Is

The quality of the insight is a direct function of the quality of data (and the interpretation of that data).

10- Given enough data, you can simultaneously “prove” opposites. #BeautifulMind #Multiverse

The evidence to support any hypothesis will grow with the size of data, asymptotically approaching 100%.

A fully scientific methodology can guard against wrong conclusions, but complexity, (im)proper motivation, malice, or ignorance can lead to invalid conclusions. The more data, the better the odds that one can get confused and make an innocent mistake, cherry-pick to advance a desired belief, or twist the facts to achieve sinister ends.  It reminds me of an epigraph I read years ago: “If there is a will to condemn, the evidence will be found.”

In addition, correlation not being causation, totally wrong but interesting correlations are plentiful and should be a warning sign!

11- Most conclusions will be either uninteresting or invalid. Big Data starts with interesting-but-useless and graduates to valid-and-useful. #InsightWins

We live in a world of new media and viral memes where the interesting-but-shallow can trump the insightful-but-boring. Occasionally, something is both interesting and insightful, but long-term, viral witticism will saturate its space and we’ll hopefully get too used to linkbait patterns to be moved. Big Data is about deeper understandings that can improve things beyond one’s immediate mood.

12- Big Data and HPC converge as data volume grows. #Analytics

If you have 200 rows of data, you have a spreadsheet; if you have 2 billion rows, you have HPC!  As the size of data grows, you need math and science to make sense of it. Value is increasingly in analytics (and “synthetics” as in item 7 above), which in turn is about math and scientific models. Check out what my colleague Stephen Perrenod wrote in a 2-part series on this topic here and here.

Are these consistent with what you see?  Share your insights please.

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