The Next Explosion – the Eyes have it

Crossing the Chasm Diagram

Crossing the Chasm – on one sheet of A4

One of the early lessons you pick up looking at product lifecycles is that some people hold out buying any new technology product or service longer than anyone else. You make it past the techies, the visionaries, the early majority, late majority and finally meet the laggards at the very right of the diagram (PDF version here). The normal way of selling at that end of the bell curve is to embed your product in something else; the person who swore they’d never buy a Microprocessor unknowingly have one inside the controls on their Microwave, or 50-100 ticking away in their car.

In 2016, Google started releasing access to its Vision API. They were routinely using their own Neural networks for several years; one typical application was taking the video footage from their Google Maps Streetview cars, and correlating house numbers from video footage onto GPS locations within each street. They even started to train their own models to pick out objects in photographs, and to be able to annotate a picture with a description of its contents – without any human interaction. They have also begun an effort to do likewise describing the story contained in hundreds of thousands of YouTube videos.

One example was to ask it to differentiate muffins and dogs:

This is does with aplomb, with usually much better than human performance. So, what’s next?

One notable time in Natural History was the explosion in the number of species on earth that  occured in the Cambrian period, some 534 million years ago. This was the time when it appears life forms first developed useful eyes, which led to an arms race between predators and prey. Eyes everywhere, and brains very sensitive to signals that come that way; if something or someone looks like they’re staring at you, sirens in your conscience will be at full volume.

Once a neural network is taught (you show it 1000s of images, and tell it which contain what, then it works out a model to fit), the resulting learning can be loaded down into a small device. It usually then needs no further training or connection to a bigger computer nor cloud service. It can just sit there, and report back what it sees, when it sees it; the target of the message can be a person or a computer program anywhere else.

While Google have been doing the heavy lifting on building the learning models in the cloud, Apple have slipped in with their own CloudML data format, a sort of PDF for the resulting machine learning data formats. Then using the Graphics Processing Units on their iPhone and iPad devices to run the resulting models on the users device. They also have their ARkit libraries (as in “Augmented Reality”) to sense surfaces and boundaries live on the embedded camera – and to superimpose objects in the field of view.

With iOS 11 coming in the autumn, any handwritten notes get automatically OCR’d and indexed – and added to local search. When a document on your desk is photo’d from an angle, it can automatically flatten it to look like a hi res scan of the original – and which you can then annotate. There are probably many like features which will be in place by the time the new iPhone models arrive in September/October.

However, tip of the iceberg. When I drive out of the car park in the local shopping centre here, the barrier automatically raises given the person with the ticket issued to my car number plate has already paid. And I guess we’re going to see a Cambrian explosion as inexpensive “eyes” get embedded in everything around us in our service.

With that, one example of what Amazon are experimenting with in their “Amazon Go” shop in Seattle. Every visitor a shoplifter: https://youtu.be/NrmMk1Myrxc

Lots more to follow.

PS: as a footnote, an example drawing a ruler on a real object. This is 3 weeks after ARkit got released. Next: personalised shoe and clothes measurements, and mail order supply to size: http://www.madewitharkit.com/post/162250399073/another-ar-measurement-app-demo-this-time

IT Trends into 2017 – or the delusions of Ian Waring

Bowling Ball and Pins

My perception is as follows. I’m also happy to be told I’m mad, or delusional, or both – but here goes. Most reflect changes well past the industry move from CapEx led investments to Opex subscriptions of several years past, and indeed the wholesale growth in use of Open Source Software across the industry over the last 10 years. Your own Mileage, or that of your Organisation, May Vary:

  1. if anyone says the words “private cloud”, run for the hills. Or make them watch https://youtu.be/URvWSsAgtJE. There is also an equivalent showing how to build a toaster for $15,000. The economics of being in the business of building your own datacentre infrastructure is now an economic fallacy. My last months Amazon AWS bill (where I’ve been developing code – and have a one page site saying what the result will look like) was for 3p. My Digital Ocean server instance (that runs a network of WordPress sites) with 30GB flash storage and more bandwidth than I can shake a stick at, plus backups, is $24/month. Apart from that, all I have is subscriptions to Microsoft, Github and Google for various point services.
  2. Most large IT vendors have approached cloud vendors as “sell to”, and sacrificed their own future by not mapping customer landscapes properly. That’s why OpenStack is painting itself into a small corner of the future market – aimed at enterprises that run their own data centres and pay support costs on a per software instance basis. That’s Banking, Finance and Telco land. Everyone else is on (or headed to) the public cloud, for both economic reasons and “where the experts to manage infrastructure and it’s security live” at scale.
  3. The War stage of Infrastructure cloud is over. Network effects are consolidating around a small number of large players (AWS, Google Cloud Platform, Microsoft Azure) and more niche players with scale (Digital Ocean among SME developers, Softlayer in IBM customers of old, Heroku with Salesforce, probably a few hosting providers).
  4. Industry move to scale out open source, NoSQL (key:value document orientated) databases, and components folks can wire together. Having been brought up on MySQL, it was surprisingly easy to set up a MongoDB cluster with shards (to spread the read load, scaled out based on index key ranges) and to have slave replicas backing data up on the fly across a wide area network. For wiring up discrete cloud services, the ground is still rough in places (I spent a couple of months trying to get an authentication/login workflow working between a single page JavaScript web app, Amazon Cognito and IAM). As is the case across the cloud industry, the documentation struggles to keep up with the speed of change; developers have to be happy to routinely dip into Github to see how to make things work.
  5. There is a lot of focus on using Containers as a delivery mechanism for scale out infrastructure, and management tools to orchestrate their environment. Go, Chef, Jenkins, Kubernetes, none of which I have operational experience with (as I’m building new apps have less dependencies on legacy code and data than most). Continuous Integration and DevOps often cited in environments were custom code needs to be deployed, with Slack as the ultimate communications tool to warn of regular incoming updates. Having been at one startup for a while, it often reminded me of the sort of military infantry call of “incoming!” from the DevOps team.
  6. There are some laudable efforts to abstract code to be able to run on multiple cloud providers. FOG in the Ruby ecosystem. CloudFoundry (termed BlueMix in IBM) is executing particularly well in large Enterprises with investments in Java code. Amazon are trying pretty hard to make their partners use functionality only available on AWS, in traditional lock-in strategy (to avoid their services becoming a price led commodity).
  7. The bleeding edge is currently “Function as a Service”, “Backend as a Service” or “Serverless apps” typified with Amazon Lambda. There are actually two different entities in the mix; one to provide code and to pay per invocation against external events, the other to be able to scale (or contract) a service in real time as demand flexes. You abstract all knowledge of the environment  away.
  8. Google, Azure and to a lesser extent AWS are packaging up API calls for various core services and machine learning facilities. Eg: I can call Google’s Vision API with a JPEG image file, and it can give me the location of every face (top of nose) on the picture, face bounds, whether each is smiling or not). Another that can describe what’s in the picture. There’s also a link into machine learning training to say “does this picture show a cookie” or “extract the invoice number off this image of a picture of an invoice”. There is an excellent 35 minute discussion on the evolving API landscape (including the 8 stages of API lifecycle, the need for honeypots to offset an emergent security threat and an insight to one impressive Uber API) on a recent edition of the Google Cloud Platform Podcast: see http://feedproxy.google.com/~r/GcpPodcast/~3/LiXCEub0LFo/
  9. Microsoft and Google (with PowerApps and App Maker respectively) trying to remove the queue of IT requests for small custom business apps based on company data. Though so far, only on internal intranet type apps, not exposed outside the organisation). This is also an antithesis of the desire for “big data”, which is really the domain of folks with massive data sets and the emergent “Internet of Things” sensor networks – where cloud vendor efforts on machine learning APIs can provide real business value. But for a lot of commercial organisations, getting data consolidated into a “single version of the truth” and accessible to the folks who need it day to day is where PowerApps and AppMaker can really help.
  10. Mobile apps are currently dogged by “winner take all” app stores, with a typical user using 5 apps for almost all of their mobile activity. With new enhancements added by all the major browser manufacturers, web components will finally come to the fore for mobile app delivery (not least as they have all the benefits of the web and all of those of mobile apps – off a single code base). Look to hear a lot more about Polymer in the coming months (which I’m using for my own app in conjunction with Google Firebase – to develop a compelling Progressive Web app). For an introduction, see: https://www.youtube.com/watch?v=VBbejeKHrjg
  11. Overall, the thing most large vendors and SIs have missed is to map their customer needs against available project components. To map user needs against axes of product life cycle and value chains – and to suss the likely movement of components (which also tells you where to apply six sigma and where agile techniques within the same organisation). But more eloquently explained by Simon Wardley: https://youtu.be/Ty6pOVEc3bA

There are quite a range of “end of 2016” of surveys I’ve seen that reflect quite a few of these trends, albeit from different perspectives (even one that mentioned the end of Java as a legacy language). You can also add overlays with security challenges and trends. But – what have I missed, or what have I got wrong? I’d love to know your views.

Future Health: DNA is one thing, but 90% of you is not you


One of my pet hates is seeing my wife visit the doctor, getting hunches of what may be afflicting her health, and this leading to a succession of “oh, that didn’t work – try this instead” visits for several weeks. I just wonder how much cost could be squeezed out of the process – and lack of secondary conditions occurring – if the root causes were much easier to identify reliably. I then wonder if there is a process to achieve that, especially in the context of new sensors coming to market and their connectivity to databases via mobile phone handsets – or indeed WiFi enabled, low end Bluetooth sensor hubs aka the Apple Watch.

I’ve personally kept a record of what i’ve eaten, down to fat, protein and carb content (plus my Monday 7am weight and daily calorie intake) every day since June 2002. A precursor to the future where devices can keep track of a wide variety of health signals, feeding a trend (in conjunction with “big data” and “machine learning” analyses) toward self service health. My Apple Watch has a years worth of heart rate data. But what signals would be far more compelling to a wider variety of (lack of) health root cause identification if they were available?

There is currently a lot of focus on Genetics, where the Human Genome can betray many characteristics or pre-dispositions to some health conditions that are inherited. My wife Jane got a complete 23andMe statistical assessment several years ago, and has also been tested for the BRCA2 (pronounced ‘bracca-2’) gene – a marker for inherited pre-disposition to risk of Breast Cancer – which she fortunately did not inherit from her afflicted father.

A lot of effort is underway to collect and sequence the complete Genome sequences from the DNA of hundreds of thousands of people, building them into a significant “Open Data” asset for ongoing research. One gotcha is that such data is being collected by numerous organisations around the world, and the size of each individuals DNA (assuming one byte to each nucleotide component – A/T or C/G combinations) runs to 3GB of base pairs. You can’t do research by throwing an SQL query (let alone thousands of machine learning attempts) over that data when samples are stored in many different organisations databases, hence the existence of an API (courtesy of the GA4GH Data Working Group) to permit distributed queries between co-operating research organisations. Notable that there are Amazon Web Services and Google employees participating in this effort.

However, I wonder if we’re missing a big and potentially just as important data asset; that of the profile of bacteria that everyone is dependent on. We are each home to approx. 10 trillion human cells among the 100 trillion microbial cells in and on our own bodies; you are 90% not you.

While our human DNA is 99.9% identical to any person next to us, the profile of our MicroBiome are typically only 10% similar; our age, diet, genetics, physiology and use of antibiotics are also heavy influencing factors. Our DNA is our blueprint; the profile of the bacteria we carry is an ever changing set of weather conditions that either influence our health – or are leading indicators of something being wrong – or both. Far from being inert passengers, these little organisms play essential roles in the most fundamental processes of our lives, including digestion, immune responses and even behaviour.

Different MicroBiome ecosystems are present in different areas of our body, from our skin, mouth, stomach, intestines and genitals; most promise is currently derived from the analysis of stool samples. Further, our gut is only second to our brain in the number of nerve endings present, many of them able to enact activity independently from decisions upstairs. In other areas, there are very active hotlines between the two nerve cities.

Research is emerging that suggests previously unknown links between our microbes and numerous diseases, including obesity, arthritis, autism, depression and a litany of auto-immune conditions. Everyone knows someone who eats like a horse but is skinny thin; the composition of microbes in their gut is a significant factor.

Meanwhile, costs of DNA sequencing and compute power have dropped to a level where analysis of our microbe ecosystems costs from $100M a decade ago to some $100 today. It should continue on that downward path to a level where personal regular sampling could become available to all – if access to the needed sequencing equipment plus compute resources were more accessible and had much shorter total turnaround times. Not least to provide a rich Open Data corpus of samples that we can use for research purposes (and to feed back discoveries to the folks providing samples). So, what’s stopping us?

Data Corpus for Research Projects

To date, significant resources are being expended on Human DNA Genetics and comparatively little on MicroBiome ecosystems; the largest research projects are custom built and have sampling populations of less than 4000 individuals. This results in insufficient population sizes and sample frequency on which to easily and quickly conduct wholesale analyses; this to understand the components of health afflictions, changes to the mix over time and to isolate root causes.

There are open data efforts underway with the American Gut Project (based out of the Knight Lab in the University of San Diego) plus a feeder “British Gut Project” (involving Tim Spector and staff at University College London). The main gotcha is that the service is one-shot and takes several months to turn around. My own sample, submitted in January, may take up 6 months to work through their sequencing then compute batch process.

In parallel, VC funded company uBiome provide the sampling with a 6-8 week turnaround (at least for the gut samples; slower for the other 4 area samples we’ve submitted), though they are currently not sharing the captured data to the best of my knowledge. That said, the analysis gives an indication of the names, types and quantities of bacteria present (with a league table of those over and under represented compared to all samples they’ve received to date), but do not currently communicate any health related findings.

My own uBiome measures suggest my gut ecosystem is more diverse than 83% of folks they’ve sampled to date, which is an analogue for being more healthy than most; those bacteria that are over represented – one up to 67x more than is usual – are of the type that orally administered probiotics attempt to get to your gut. So a life of avoiding antibiotics whenever possible appears to have helped me.

However, the gut ecosystem can flex quite dramatically. As an example, see what happened when one person contracted Salmonella over a three pay period (the green in the top of this picture; x-axis is days); you can see an aggressive killing spree where 30% of the gut bacteria population are displaced, followed by a gradual fight back to normality:

Salmonella affecting MicroBiome PopulationUnder usual circumstances, the US/UK Gut Projects and indeed uBiome take a single measure and report back many weeks later. The only extra feature that may be deduced is the delta between counts of genome start and end sequences, as this will give an indication to the relative species population growth rates from otherwise static data.

I am not aware of anyone offering a faster turnaround service, nor one that can map several successively time gapped samples, let alone one that can convey health afflictions that can be deduced from the mix – or indeed from progressive weather patterns – based on the profile of bacteria populations found.

My questions include:

  1. Is there demand for a fast turnaround, wholesale profile of a bacterial population to assist medical professionals isolating a indicators – or the root cause – of ill health with impressive accuracy?
  2. How useful would a large corpus of bacterial “open data” be to research teams, to support their own analysis hunches and indeed to support enough data to make use of machine learning inferences? Could we routinely take samples donated by patients or hospitals to incorporate into this research corpus? Do we need the extensive questionnaires the the various Gut Projects and uBiome issue completed alongside every sample?
  3. What are the steps in the analysis pipeline that are slowing the end to end process? Does increased sample size (beyond a small stain on a cotton bud) remove the need to enhance/copy the sample, with it’s associated need for nitrogen-based lab environments (many types of bacteria are happy as Larry in the Nitrogen of the gut, but perish with exposure to oxygen).
  4. Is there any work active to make the QIIME (pronounced “Chime”) pattern matching code take advantage of cloud spot instances, inc Hadoop or Spark, to speed the turnaround time from Sequencing reads to the resulting species type:volume value pairs?
  5. What’s the most effective delivery mechanism for providing “Open Data” exposure to researchers, while retaining the privacy (protection from financial or reputational prejudice) for those providing samples?
  6. How do we feed research discoveries back (in English) to the folks who’ve provided samples and their associated medical professionals?

New Generation Sequencing works by splitting DNA/RNA strands into relatively short read lengths, which then need to be reassembled against known patterns. Taking a poop sample with contains thousands of different bacteria is akin to throwing the pieces of many thousand puzzles into one pile and then having to reconstruct them back – and count the number of each. As an illustration, a single HiSeq run may generate up to 6 x 10^9 sequences; these then need reassembling and the count of 16S rDNA type:quantity value pairs deduced. I’ve seen estimates of six thousand CPU hours to do the associated analysis to end up with statistically valid type and count pairs. This is a possible use case for otherwise unused spot instance capacity at large cloud vendors if the data volumes could be ingested and processed cost effectively.

Nanopore sequencing is another route, which has much longer read lengths but is much more error prone (1% for NGS, typically up to 30% for portable Nanopore devices), which probably limits their utility for analysing bacteria samples in our use case. Much more useful if you’re testing for particular types of RNA or DNA, rather than the wholesale profiling exercise we need. Hence for the time being, we’re reliant on trying to make an industrial scale, lab based batch process turn around data as fast we are able – but having a network accessible data corpus and research findings feedback process in place if and when sampling technology gets to be low cost and distributed to the point of use.

The elephant in the room is in working out how to fund the build of the service, to map it’s likely cost profile as technology/process improvements feed through, and to know to what extent it’s diagnosis of health root causes will improve it’s commercial attractiveness as a paid service over time. That is what i’m trying to assess while on the bench between work contracts.

Other approaches

Nature has it’s way of providing short cuts. Dogs have been trained to be amazingly prescient at assessing whether someone has Parkinson’s just by smelling their skin. There are other techniques where a pocket sized spectrometer can assess the existence of 23 specific health disorders. There may well be other techniques that come to market that don’t require a thorough picture of a bacterial population profile to give medical professionals the identity of the root causes of someone’s ill health. That said, a thorough analysis may at least be of utility to the research community, even if we get to only eliminate ever rarer edge cases as we go.

Coming full circle

One thing that’s become eerily apparent to date is some of the common terminology between MicroBiome conditions and terms i’ve once heard used by Chinese Herbal Medicine (my wife’s psoriasis was cured after seeing a practitioner in Newbury for several weeks nearly 20 years ago). The concept of “balance” and the existence of “heat” (betraying the inflammation as your bacterial population of different species ebbs and flows in reaction to different conditions). Then consumption or application of specific plant matter that puts the bodies bacterial population back to operating norms.

Lingzhi Mushroom

Wild mushroom “Lingzhi” in China: cultivated in the far east, found to reduce Obesity

We’ve started to discover that some of the plants and herbs used in Chinese Medicine do have symbiotic effects on your bacterial population on conditions they are reckoned to help cure. With that, we are starting to see some statistically valid evidence that Chinese and Western medicine may well meet in the future, and be part of the same process in our future health management.

Until then, still work to do on the business plan.

Mobile Phone User Interfaces and Chinese Genius

Most of my interactions with the online world use my iPhone 6S Plus, Apple Watch, iPad Pro or MacBook – but with one eye on next big things from the US West Coast. The current Venture Capital fads being on Conversational Bots, Virtual Reality and Augmented Reality. I bought a Google Cardboard kit for my grandson to have a first glimpse of VR on his iPhone 5C, though spent most of the time trying to work out why his handset was too full to install any of the Cardboard demo apps; 8GB, 2 apps, 20 songs and the storage list that only added up to 5GB use. Hence having to borrow his Dad’s iPhone 6 while we tried to sort out what was eating up 3GB. Very impressive nonetheless.


The one device I’m waiting to buy is an Amazon Echo (currently USA only). It’s a speaker with six directional microphones, an Internet connection and some voice control smarts; these are extendable by use of an application programming interface and database residing in their US East Datacentre. Out of the box, you can ask it’s nom de plume “Alexa” to play a music single, album or wish list. To read back an audio book from where you last left off. To add an item to a shopping or to-do list. To ask about local outside weather over the next 24 hours. And so on.

It’s real beauty is that you can define your own voice keywords into what Amazon term a “Skill”, and provide your own plumbing to your own applications using what Amazon term their “Alexa Skill Kit”, aka “ASK”. There is already one UK Bank that have prototyped a Skill for the device to enquire their users bank balance, primarily as an assist to the visually impaired. More in the USA to control home lighting and heating by voice controls (and I guess very simple to give commands to change TV channels or to record for later viewing). The only missing bit is that of identity; the person speaking can be anyone in proximity to the device, or indeed any device emitting sound in the room; a radio presenter saying “Alexa – turn the heating up to full power” would not be appreciated by most listeners.

For further details on Amazon Echo and Alexa, see this post.

However, the mind wanders over to my mobile phone, and the disjointed experience it exposes to me when I’m trying to accomplish various tasks end to end. Data is stored in application silos. Enterprise apps quite often stop at a Citrix client turning your pocket supercomputer into a dumb (but secured) Windows terminal, where the UI turns into normal Enterprise app silo soup to go navigate.

Some simple client-side workflows can be managed by software like IFTTT – aka “IF This, Then That” – so I can get a new Photo automatically posted to Facebook or Instagram, or notifications issued to be when an external event occurs. But nothing that integrates a complete buying experience. The current fad for conversational bots still falls well short; imagine the workflow asking Alexa to order some flowers, as there are no visual cues to help that discussion and buying experience along.

For that, we’d really need to do one of the Jeff Bezos edicts – of wiping the slate clean, to imagine the best experience from a user perspective and work back. But the lessons have already been learnt in China, where desktop apps weren’t a path on the evolution of mobile deployments in society. An article that runs deep on this – and what folks can achieve within WeChat in China – is impressive. See: http://dangrover.com/blog/2016/04/20/bots-wont-replace-apps.html

I wonder if Android or iOS – with the appropriate enterprise APIs – could move our experience on mobile handsets to a similar next level of compelling personal servant. I hope the Advanced Development teams at both Apple and Google – or a startup – are already prototyping  such a revolutionary, notifications baked in, mobile user interface.

Corning Glass, Android, Amazon then – surprise!

3D Glasses

One piece of uncharted territory in the mobile phone and tablet industry relates to how much Gorilla Glass (used for touch screens) that Corning manufacture, compared to an estimate of how many devices are physically shipped. Corning routinely publish the total area of glass produced, from which analysts attempt to triangulate with the relative sizes, and volumes, of the products that employ the technology.

The biggest estimated gap appears to relate to glass used to power “media tablets” in China. These tend to run the Open Source version of Android (aka “AOSP” – Android Open Source Project), don’t use any of the Google Play services (hence never need to authenticate with Google), and are assumed to be personal TVs that feed content from WiFi. Or suitable capacity SD memory cards traded (illicitly?) in some Chinese markets, preloaded with films or video from other sources.

The existence of these low cost WiFi personal TVs would explain why Apple, with a seemingly sub 15% unit market share, still drive a vastly disproportionate amount of web and e-commerce traffic that operators experience. However, such tablets – Kindle Fire being the most prominent exception – are fairly rare outside China and India.

There are rumours that Amazon are about to release a mobile phone – I don’t even think they’ve said phone themselves – but on their announcement invite video, folks are rocking their heads from side to side looking at a handheld device. All the bets are on showing items in 3D, as demonstrated by this (now Google) employee – who conjured the effect using a Nintendo Wii remote and matching sensor bar. A fascinating (less than 5 minutes) demonstration of what was possible some months back here: Head Tracking for Desktop VR Displays

Of course, by the time you read this, Amazon will have likely blown your head away with a ready to ship (soon) device, and some compelling content or applications. As an Amazon Prime customer, i’m looking forward to it. Not least having a 3D display without the need for special glasses!

Footnote: the Amazon Fire Phone was announced, two of it’s features described (in 80 seconds) in this BBC video. This neglected to mention that the WiFi can wind up to full dual channel 801.11ac speeds (as fast 300Mb/s), and that it already supports the UK LTE and HSPA+ bands out of the box. You can also throw video to your TV using Miracast (as present in a lot of modern TVs already, and in many set-top boxes). At the moment, like the Fire TV set top box, it has been announced for the US only.

I must admit, I did tap the US off contract price into Google: 649 usd in gbp – and it comes out £381.52 + VAT = £458 or so. As in the USA, that’s a 32GB phone for the price of a 16GB iPhone 5S. Then told myself off for doing this, as the USA cellular market is a strange beast (most business in $80/month contracts including handset subsidies – where the handset cost is $200 up front). Everything about the hardware is great, and the source of initial moans by the tech community around US pricing, being tied to AT&T for contract sales, no sign of a rumoured bundled carrier data contract etc – are things that Amazon could iterate at blinding speed – both in the USA and elsewhere.

It is a shopaholics dream phone – it can look up from a selection of millions of items visually, or by listening to music or TV shows – and to be able to order them for you (and deliver on a bundled Amazon Prime service) in a very, very slick fashion. About the only thing it can’t do yet is to value antiques. Or can it?

The Moving Target that is Enterprise IT infrastructures

Docker Logo

A flurry of recent Open Source Enterprise announcements, one relating to Docker – allowing Linux containers containing all their needed components to be built, distributed and then run atop Linux based servers. With this came the inference that Virtualisation was likely to get relegated to legacy application loads. Docker appears to have support right across the board – at least for Linux workloads – covering all the major public cloud vendors. I’m still unsure where that leaves the other niche that is Windows apps.

The next announcement was that of Apache Mesos, which is the software originally built by ex-Google Twitter engineers – largely the replicate the Google Borg software used to fire up multi-server workloads across Google’s internal infrastructure. This used to good effect to manage Twitters internal infrastructure and to consign their “Fail Whale” to much rarer appearances. At the same time, Google open sourced a version of their software – I’ve not yet made out if it’s derived from the 10+ year old Borg or more recent Omega projects – to do likewise, albeit at smaller scale than Google achieve inhouse. The one thing that bugs me is that I can never remember it’s name (i’m off trying to find reference to it again – and now I return 15 minutes later!).

“Google announced Kubernetes, a lean yet powerful open-source container manager that deploys containers into a fleet of machines, provides health management and replication capabilities, and makes it easy for containers to connect to one another and the outside world. (For the curious, Kubernetes (koo-ber-nay’-tace) is Greek for “helmsman” of a ship)”.

That took some finding. Koo-ber-nay-tace. No exactly memorable.

However, it looks like it’ll be a while before these packaging, deployment and associated management technologies get ingrained in Enterprise IT workloads. A lot of legacy systems out there are simply not architected to run on scale-out infrastructures yet, and it’s a source of wonder what the major Enterprise software vendors are running in their own labs. If indeed they have an appetite to disrupt themselves before others attempt to.

I still cringe with how one ERP system I used to use had the cost collection mechanisms running as a background batch process, and the margins of the running business went all over the place like a skidding car as orders were loaded. Particularly at end of quarter customer spend spikes, where the complexity of relational table joins had a replicated mirror copy of the transaction system consistently running 20-25 minutes behind the live system. I should probably cringe even more given there’s no obvious attempt by startups to fundamentally redesign an ERP system from the ground up using modern techniques. At least yet.

Startups appear to be much more heavily focussed on much lighter mobile based applications – of which there are a million different bets chasing VC money. Moving Enterprise IT workloads into much more cost effective (but loosely coupled) public cloud based infrastructure – and that take full advantage of its economics – is likely to take a little longer. I sometimes agonise over what change(s) would precipitate that transition – and whether that’s a monolith app, or a network of simple ones daisy chained together.

I think we need a 2014 networked version of Silicon Office or Hypercard to trigger some progress. Certainly their abject simplicity is no more, and we’re consigned to the lower level, piecemeal building bricks – like JavaScript – which is what life was like in assembler before high level languages liberated us. Some way to go.

Start with the needs of the end user, and work back from there…

Great Customer Service

A bit of a random day. I learnt something about the scale of construction taking place in China; not just the factoid that they’re building 70 airports at the moment, but a much more stunning one. That, in the last 3 years, the Chinese have used more cement than the USA did in the 100 years between 1900 and 2000. The very time when all the Interstate and Road networks were built, in addition to construction in virtually every major city.

5 of the top 10 mobile phone vendors are Chinese (it’s not just an Apple vs Samsung battle now), and one appears to be breaking from the pack in emerging markets – Xiaomi (pronounced show – as in shower – and me). Their business model is to offer Apple-class high end phones at around cost, target them at 18-30 year “fans” in direct sales (normally flash sales after a several 100,000 unit production run), and to make money from ROM customisations and add-on cloud services. I’ve started hearing discussions with Silicon Valley based market watchers who are starting to cite Xiaomi’s presence in their analyses, not least as in China, they are taking market share from Samsung – the first alternative Android vendor to consistently do so. I know their handsets, and their new tablet, do look very nice and very cost effective.

That apart, I have tonight read a fantastic blog post from Neelie Kroes, Vice President of the European Commission and responsible for the Digital Agenda for Europe – talking specifically about Uber and this weeks strikes by Taxi drivers in major cities across Europe. Well worth a read in full here.

Summarised:

  • Let me respond to the news of widespread strikes and numerous attempts to limit or ban taxi app services across Europe. The debate about taxi apps is really a debate about the wider sharing economy.
  • It is right that we feel sympathy for people who face big changes in their lives.
  • Whether it is about cabs, accommodation, music, flights, the news or whatever.  The fact is that digital technology is changing many aspects of our lives. We cannot address these challenges by ignoring them, by going on strike, or by trying to ban these innovations out of existence
  • a strike won’t work: rather than “downing tools” what we need is a real dialogue
  • We also need services that are designed around consumers.
  • People in the sharing economy like drivers, accommodation hosts, equipment owners and artisans – these people all need to pay their taxes and play by the rules.  And it’s the job of national and local authorities to make sure that happens.
  • But the rest of us cannot hide in a cave. 
  • Taxis can take advantage of these new innovations in ways consumers like – they can arrive more quickly, they could serve big events better, there could be more of them, their working hours could be more flexible and suited to driver needs – and apps can help achieve that.
  • More generally, the job of the law is not to lie to you and tell you that everything will always be comfortable or that tomorrow will be the same as today.  It won’t. Not only that, it will be worse for you and your children if we pretend we don’t have to change. If we don’t think together about how to benefit from these changes and these new technologies, we will all suffer.
  • If I have learnt anything from the recent European elections it is that we get nowhere in Europe by running away from hard truths. It’s time to face facts:  digital innovations like taxi apps are here to stay. We need to work with them not against them.

It is absolutely refreshing to have elected representatives working for us all and who “get it”. Focus on consumers, being respectful of those afflicted by changes, but driving for the collective common good that Digital innovations provide to society. Kudos to Neelie Kroes; a focus on users, not entrenched producers – a stance i’ve only really heard with absolute clarity before from Jeff Bezos, CEO of Amazon. It does really work.

 

What if Quality Journalism isn’t?

Read all about it

Carrying on with the same theme as yesterdays post – the fact that content is becoming disaggregated from a web sites home page – I read an excellent blog post today: What if Quality Journalism isn’t? In this, the author looks at the seemingly divergent claims from the New York Times, who claim:

  • They are “winning” at Journalism
  • Readership is falling, both on web and mobile platforms
  • therefore they need to pursue strategies to grow their audience

The author asks “If its product is ‘the world’s best journalism‘, why does it have a problem growing its audience?”. You can’t be the world’s best and fail at the same time. Indeed. And then goes into a deeper analysis.

I like the analogue of the supermarket of intent (Amazon) versus a supermarket of interest (social) versus Niche. The central issue is how to curate articles of interest to a specific subscriber, without filling their delivery with superfluous (to the reader) content. This where Newspapers (in the authors case) typically contain 70% or more of wasted content to a typical specific user.

One comment under the article suggests one approach: existence of an open source aggregation model for the municipal bond market on Twitter via #muniland… journos from 20+ pubs, think tanks, govts, law firms, market commentators hash their story and all share.

Deep linking to useful, pertinent and interesting content is probably a big potential area if alternative approaches can crack it. Until then, i’m having to rely on RSS feeds of known authors I respect, or from common watering holes, or from the occasional flash of brilliance that crosses my twitter stream at times i’m watching it.

Just need to update Aaron Swartz’s code to spot water-cooler conversations on Twitter among specific people or sources I respect. That would probably do most of the leg work to enlighten me more productively, and without subjecting myself to pages of search engine discovery.

Starting with the end in mind: IT Management Heat vs Light

A very good place to startOne source of constant bemusement to me is the habit of intelligent people to pee in the industry market research bathwater, and then to pay handsomely to drink a hybrid mix of the result collected across their peers.

Perhaps betrayed by an early experience of one research company coming in to present to the management of the vendor I was working at, and finding in the rehearsal their conjecture that sales of specific machine sizes had badly dipped in the preceding quarter. Except they hadn’t; we’d had the biggest growth in sales of the highlighted machines in our history in that timeframe. When I mentioned my concern, the appropriate slides were corrected in short order, and no doubt the receiving audience impressed with the skill in their analysis that built a forecast starting with an amazingly accurate, perceptive (and otherwise publicly unreported) recent history.

I’ve been doubly nervous ever since – always relating back to the old “Deep Throat” hints given in “All the Presidents Men” – that of, in every case, “to follow the money”.

Earlier today, I was having some banter on one of the boards of “The Motley Fool” which referenced the ways certain institutions were imposing measures on staff – well away from a useful business use that positively supported better results for their customers. Well, except of providing sound bites to politicians. I can sense that in Education, in some elements of Health provision, and rather fundamentally in the Police service. I’ve even done a drains-up some time ago that reflected on the way UK Police are measured, and tried trace the rationale back to source – which was a senior politician imploring them to reduce crime; blog post here. The subtlety of this was rather lost; the only control placed in their hands was that of compiling the associated statistics, and to make their behaviours on the ground align supporting that data collection, rather than going back to core principles of why they were there, and what their customers wanted of them.

Jeff Bezos (CEO of Amazon) has the right idea; everything they do aligns with the ultimate end customer, and everything else works back from there. Competition is something to be conscious of, but only to the extent of understanding how you can serve your own customers better. Something that’s also the central model that W. Edwards Deming used to help transform Japanese Industry, and in being disciplined to methodically improve “the system” without unnecessary distractions. Distractions which are extremely apparent to anyone who’s been subjected to his “Red Beads” experiment. But the central task is always “To start with the end in mind”.

With that, I saw a post by Simon Wardley today where Gartner released the results of a survey on “Top 10 Challenges for I&O Leaders”, which I guess is some analogue of what used to be referred to as “CIOs”. Most of which felt to me like a herd mentality – and divorced from the sort of issues i’d have expected to be present. In fact a complete reenactment of this sort of dialogue Simon had mentioned before.

Simon then cited the first 5 things he thought they should be focussed on (around Corrective Action), leaving the remainder “Positive Action” points to be mapped based on that appeared upon that foundation. This in the assumption that those actions would likely be unique to each organisation performing the initial framing exercise.

Simon’s excellent blog post is: My list vs Gartner, shortly followed by On Capabilities. I think it’s a great read. My only regret is that, while I understand his model (I think!), i’ve not had to work on the final piece between his final strategic map (for any business i’m active in) and articulating a pithy & prioritised list of actions based on the diagram created. And I wish he’d get the bandwidth to turn his Wardley Maps into a Book.

Until then, I recommend his Bits & Pieces Blog; it’s a quality read that deserves good prominence on every IT Manager’s (and IT vendors!) RSS feed.

Recommended Bedtime Reading, and signing off for a bit…

I’ve never really been a big fiction fan. About the only author i’ve read extensively (outside high technology and business stuff – don’t yawn) was by Michael Crichton. At least the books that have yet to be turned into films. Well, all except “Disclosure”, where Demi Moore sexually harasses Michael Douglas and then throws the company’s political establishment against him when he refuses to succumb to her charms. But I digress.

There’s been a lot of comment on the blogs and twitter feeds I follow on the West Coast of the USA that keep on citing a new book by Andy Weir called “The Martian”. I tried to buy it on my last trip abroad, thinking i’d go buy the voiced version on Audible to listen to, but baulked at it’s then £20+ price tag. However, it appeared on an Amazon email last week for under £10 in hardback form, so I bought it.

Finished it today (like many of the USA folks, completely immersed in it for two days between work bursts). I’m completely with them; it is a fantastic book, and would make a great film. A modern day Robinson Crusoe, but one accidentally left behind on Mars. At least Crusoe had to worry more about Cannibals than continuously working around all the life support systems, and food, to last long enough to be rescued. If indeed NASA didn’t just leave him behind to eat his poison pills. Thoroughly recommended, and superbly written throughout.

Tomorrow, i’m off to Cornwall for a short break before I start my next assignment, which will start on June 2nd. Really looking forward to it. As such, the frequency of my blog posts are, with effect from today, going to drop to one per week. I think my daily posts have now caught up with my brain nuances, and the newsflow in High Technology has started to slow. At least until Apple have their Worldwide Developers Conference at the start of June, and Google do their matching I/O conference a week or two later.

In the three months or so i’ve been writing this blog, a few articles keep on getting lots of page views well after their posting data. The Crossing the Chasm one got reposted on LinkedIn by the original author of the book i’d summarised, and I started to get warnings from WordPress that I appeared to have an incoming tidal wave for 3 days running.

For some reason, my mention of Chromecast working on the Tesco Hudl tablet gets regular traffic, nominally by hoards of people querying Google to see if Tesco sell Chromecast in the UK.

Surprisingly few look at my tips for spotting the 4 key trends to look at with any business, in order to suss out what dimensions are and are not working. Or the other post about how to conduct yourself in a price war (there are only two things you focus on, and all paths to action stem from there).

I’m gone for a week, and to see how adept my 2 year old granddaughter has got on her iPad Mini we bought her (a necessity, as when she visits us, I never could get it back until she leaves again). She is impressively native on it with photos and with YouTube. Even tries to swipe “Skip Ad” on ITV on the telly.

So, signing off until May 30th. See you once i’m back.