Amazon

Simple Mistakes – User Experience 101 failure

ianwaring Amazon, Enterprise IT, Future Trends, Health, Management, Medical, Shares, Wardley Maps 1 Comment

Things have been very busy at work recently, but I surfaced on Saturday to take my wife into Reading to collect goods she’d ordered from Boots earlier in the week. Just replenishing a range of items so she didn’t start running out from Monday. She’d been advised to pick everything up anytime after midday from their town centre store.

You’ve probably guessed it – no sign of the order. She was just pointed at the customer service phone number on her copy of the order sheet, and asked to call them. Which she duly did outside, only to find that hundreds of customers who’d paid for their orders in the week with Mastercard were similarly in the same position. So she asked if she could cancel the order and at least buy the same things in the store while she was in town. Answer: the operator wasn’t sure if she was even allowed to cancel, but would ask and email back. That email never arrived; the only one that did a day later confirmed her items had now dispatched for the store but still no indication of arrival date – and the next 15 mile round trip needed.

I’m reminded of two things learnt from both Amazon and in producing strategy maps using the Wardley Mapping technique. The common thing when you’re involved in any product, project or business process design is that you start with the customer and optimise for their most delightful experience – then work back from there. And you only start trying to be unique if it’s directly visible to that user experience in some tangible way. Both facets together are still an incredibly important gap that I see folks miss all the time (I see that in projects at work, but that’s another story).

During the week, it was announced Amazon had purchased PillPack – a small new England company – and it sent the shares of all the big Pharmacy Chains in the USA tumbling (in market cap terms, around $13 Billion in a day). So, what do they do? Simple:

If you have a regular prescription, they put all the tablets you’re supposed to consume in a time/date labelled packet. These are printed and filled in a roll, output in chronological order – then loaded into a dispenser you receive in the mail (overnight if needed urgently):Simple! And then a set of services where they maintain your repeat needs with your doctor directly, so all the grunt work in ensuring you get your meds is done for you. They even allow you to set your holiday location if you’re away and ship there if needed to ensure you never have an unwanted gap.

Compare that to the run around most folks are exposed to with regular prescriptions and in understanding what to take and when. Instead you have a friendly, subscription based business serving your needs. (For some reason, Wall Street currently obsess about subscription based businesses – they value their stock not on Price to Earnings ratios but on Price to Sales Revenue multiples instead – and Amazon are in the thick of that too).

Personal experience here with Amazon (we’re Prime members) is that there is any problem with an order and we ask to cancel, it just happens and money immediately reimbursed. You can see why all those retail pharmacy shares took a hit with the PillPack buyout announcement by Amazon; you can see the end user experience is about to get radically better, and probably first in a number of Amazon initiatives in the Health Industry that will follow a path of relentless, customer obsessed, focus. 

Amazon already have a joint venture with Goldman Sachs and Berkshire Hathaway to work out how to provide cost effective health benefits to their combined employee populations. Something they’ll no doubt open outside the company too in time. That’s when life for CVS, Walgreens, Target and so forth (plus Boots in the UK) will get very interesting. Bring it on!

IT Trends into 2018 – or the continued delusions of Ian Waring

ianwaring Amazon, Android, Apple, Bitcoin, Blind Alleys, Cloud, Enterprise IT, Future Trends, Google, Low Energy Bluetooth, Mobiles, Wardley Maps Leave a comment

William Tell the Penguin

I’m conflicted. CIO Magazine published a list of “12 technologies that will disrupt business in 2018”, which promptly received Twitter accolades from folks I greatly respect: Leading Edge Forum, DXC Technology and indeed Simon Wardley. Having looked at it, I thought it had more than it’s fair share of muddled thinking (and they listed 13 items!). Am I alone in this? Original here. Taking the list items in turn:

Smart Health Tech (as evidenced by the joint venture involving Amazon, Berkshire Hathaway and JP Morgan Chase). I think this is big, but not for the “corporate wellness programs using remote patient monitoring” reason cited. That is a small part of it.

Between the three you have a large base of employees in a country without a single payer healthcare system, mired with business model inefficiencies. Getting an operationally efficient pilot with reasonable scale using internal users in the JV companies running, and then letting outsiders (even competitors) use the result, is meat and drink to Amazon. Not least as they always start with the ultimate consumer (not rent seeking insurance or pharma suppliers), and work back from there.

It’s always telling that if anyone were to try anti-trust actions on them, it’s difficult to envision a corrective action that Amazon aren’t already doing to themselves already. This program is real fox in the hen house territory; that’s why on announcement of the joint venture, leading insurance and pharmaceutical shares took quite a bath. The opportunity to use remote patient monitoring, using wearable sensors, is the next piece of icing on top of the likely efficient base, but very secondary at the start.

Video, video conferencing and VR. Their description cites the magic word “Agile” and appears to focus on using video to connect geographically dispersed software development teams. To me, this feels like one of those situations you can quickly distill down to “great technology, what can we use this for?”. Conferencing – even voice – yes. Shared KanBan flows (Trello), shared BaseCamp views, communal use of GitHub, all yes. Agile? That’s really where you’re doing fast iterations of custom code alongside the end user, way over to the left of a Wardley Map; six sigma, doggedly industrialising a process, over to the right. Video or VR is a strange bedfellow in the environment described.

Chatbots. If you survey vendors, and separately survey the likely target users of the technology, you get wildly different appetites. Vendors see a relentless march to interactions being dominated by BOT interfaces. Consumers, given a choice, always prefer not having to interact in the first place, and only where the need exists, to engage with a human. Interacting with a BOT is something largely avoided unless it is the only way to get immediate (or out of hours) assistance.

Where the user finds themselves in front of a ChatBot UI, they tend to prefer an analogue of a human talking them, preferably appearing to be of a similar age.

The one striking thing i’ve found was talking to a vendor who built an machine learning model that went through IT Helpdesk tickets, instant message and email interaction histories, nominally to prioritise the natural language corpus into a list of intent:action pairs for use by their ChatBot developers. They found that the primary output from the exercise was in improving FAQ sheets in the first instance. Ian thinking “is this technology chasing a use case?” again. Maybe you have a different perspective!

IoT (Internet of Things). The sample provides was tying together devices, sensors and other assets driving reductions in equipment downtime, process waste and energy consumption in “early adopter” smart factories. And then citing security concerns and the need to work with IT teams in these environments to alleviate such risks.

I see lots of big number analyses from vendors, but little from application perspectives. It’s really a story of networked sensors relaying information back to a data repository, and building insights, actions or notifications on the resulting data corpus. Right now, the primary sensor networks in the wild are the location data and history stored on mobile phone handsets or smart watches. Security devices a smaller base. Embedded simple devices smaller still. I think i’m more excited when sensors get meaningful vision capabilities (listed separately below). Until then, content to let my Apple Watch keep tabs on my heart rate, and to feed that daily into a research project looking at strokes.

Voice Control and Virtual Assistants. Alexa: set an alarm for 6:45am tomorrow. Play Lucy in the Sky with Diamonds. What’s the weather like in Southampton right now? OK Google: What is $120 in UK pounds? Siri: send a message to Jane; my eta is 7:30pm. See you in a bit. Send.

It’s primarily a convenience thing when my hands are on a steering wheel, in flour in a mixing bowl, or the quickest way to enact a desired action – usually away from a keyboard and out of earshot to anyone else. It does liberate my two youngest grandchildren who are learning to read and write. Those apart, it’s just another UI used occasionally – albeit i’m still in awe of folks that dictate their book writing efforts into Siri as they go about their day. I find it difficult to label this capability as disruptive (to what?).

Immersive Experiences (AR/VR/Mixed Reality). A short list of potential use cases once you get past technology searching for an application (cart before horse city). Jane trying out lipstick and hair colours. Showing the kids a shark swimming around a room, or what colour Tesla to put in our driveway. Measuring rooms and seeing what furniture would look like in situ if purchased. Is it Groundhog Day for Second Life, is there a battery of disruptive applications, or is it me struggling for examples? Not sure.

Smart Manufacturing. Described as transformative tech to watch. In the meantime, 3D printing. Not my area, but it feels to me low volume local production of customised parts, and i’m not sure how big that industry is, or how much stock can be released by putting instant manufacture close to end use. My dentist 3D prints parts of teeth while patients wait, but otherwise i’ve not had any exposure that I could translate as a disruptive application.

Computer Vision. Yes! A big one. I’m reminded of a Google presentation that related the time in prehistoric times when the number of different life form species on earth vastly accelerated; this was the Cambrian Period, when life forms first developed eyes. A combination of cheap camera hardware components, and excellent machine learning Vision APIs, should be transformative. Especially when data can be collected, extracted, summarised and distributed as needed. Everything from number plate, barcode or presence/not present counters, through to the ability to describe what’s in a picture, or to transcribe the words recited in a video.

In the Open Source Software World, we reckon bugs are shallow as the source listing gets exposed to many eyes. When eyes get ubiquitous, there are probably going to be little that happens that we collectively don’t know about. The disruption is then at the door of privacy legislation and practice.

Artificial Intelligence for Services. The whole shebang in the article relates back to BOTs. I personally think it’s more nuanced; it’s being able to process “dirty” or mixed media data sources in aggregate, and to use the resulting analysis to both prioritise and improve individual business processes. Things like www.parlo.io‘s Broca NLU product, which can build a suggested intent:action Service Catalogue from Natural Language analysis of support tickets, CRM data, instant message and support email content.

I’m sure there are other applications that can make use of data collected to help deliver better, more efficient or timely services to customers. BOTs, I fear, are only part of the story – with benefits accruing more to the service supplier than to the customer exposed to them. Your own mileage may vary.

Containers and Microservices. The whole section is a Minestrone Soup of Acronyms and total bollocks. If Simon Wardley was in a grave, he’d be spinning in it (but thank god he’s not).

Microservices is about making your organisations data and processes available to applications that can be internally facing, externally facing or both using web interfaces. You typically work with Apigee (now owned by Google) or 3Scale (owned by Red Hat) to produce a well documented, discoverable, accessible and secure Application Programming Interface to the services you wish to expose. Sort licensing, cost mechanisms and away. This is a useful, disruptive trend.

Containers are a standardised way of packaging applications so that they can be delivered and deployed consistently, and the number of instances orchestrated to handle variations in load. A side effect is that they are one way of getting applications running consistently on both your own server hardware, and in different cloud vendors infrastructures.

There is a view in several circles that containers are an “interim” technology, and that the service they provide will get abstracted away out of sight once “Serverless” technologies come to the fore. Same with the “DevOps” teams that are currently employed in many organisations, to rapidly iterate and deploy custom code very regularly by mingling Developer and Operations staff.

With Serverless, the theory being that you should be able to write code once, and for it to be fired up, then scaled up or down based on demand, automatically for you. At the moment, services like Amazon AWS Lambda, Google Cloud Functions and Microsoft Azure Functions (plus point database services used with them) are different enough to make applications based on one limited to that cloud provider only.

Serverless is the Disruptive Technology here. Containers are where the puck is, not where the industry is headed.

Blockchain. The technology that first appeared under Bitcoin is the Blockchain. A public ledger, distributed over many different servers worldwide, that doesn’t require a single trusted entity to guarantee the integrity (aka “one version of the truth”) of the data. It manages to ensure that transactions move reliably, and avoids the “Byzantine Generals Problem” – where malicious behaviour by actors in the system could otherwise corrupt its working.

Blockchain is quite a poster child of all sorts of applications (as a holder and distributor of value), and focus of a lot of venture capital and commercial projects. Ethereum is one such open source, distributed platform for smart contracts. There are many others; even use of virtual coins (ICO’s) to act as a substitute for venture capital funding.

While it has the potential to disrupt, no app has yet broken through to mainstream use, and i’m conscious that some vendors have started to patent swathes of features around blockchain applications. I fear it will be slow boil for a long time yet.

Cloud to Edge Computing. Another rather gobbledygook set of words. I think they really mean that there are applications that require good compute power at the network edge. Devices like LIDAR (the spinning camera atop self driving cars) is typically consuming several GB of data per mile travel, where there is insufficient reliable bandwidth to delegate all the compute to a remote cloud server. So there are models of how a car should drive itself that are built in the cloud, but downloaded and executed in the car without a high speed network connection needing to be in place while it’s driving. Basic event data (accident ahead, speed, any notable news) may be fed back as it goes, with more voluminous data shared back later when adjacent to a fast home or work network.

Very fast chips are a thing; the CPU in my Apple Watch is faster than a room size VAX-11/780 computer I used earlier in my career. The ARM processor in my iPhone and iPad Pro are 64-bit powerhouses (Apple’s semiconductor folks really hit out of the park on every iteration they’ve shipped to date). Development Environments for powerful, embedded systems are something i’ve not seen so far though.

Digital Ethics. This is a real elephant in the room. Social networks have been built to fulfil the holy grail of advertisers, which is to lavish attention on the brands they represent in very specific target audiences. Advertisers are the paying customers. Users are the Product. All the incentives and business models align to these characteristics.

Political operators, both local as well as foreign actors, have fundamentally subverted the model. Controversial and most often incorrect and/or salacious stories get wide distribution before any truth emerges. Fake accounts and automated bots further corrupt the measures to pervert the engagement indicators that drive increased distribution (noticeable that one video segment of one Donald Trump speech got two orders of magnitude more “likes” than the number of people that actually played the video at all). Above all, messages that appeal to different filter bubbles drive action in some cases, and antipathy in others, to directly undermine voting patterns.

This is probably the biggest challenge facing large social networks, at the same time that politicians (though the root cause of much of the questionable behaviours, alongside their friends in other media), start throwing regulatory threats into the mix.

Many politicians are far too adept at blaming societal ills on anyone but themselves, and in many cases on defenceless outsiders. A practice repeated with alarming regularity around the world, appealing to isolationist bigotry.

The world will be a better place when we work together to make the world a better place, and to sideline these other people and their poison. Work to do.

A small(?) task of running up a Linux server to run a Django website

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Django 1.11 on Ubuntu 16.04

I’m conscious that the IT world is moving in the direction of “Serverless” code, where business logic is loaded to a service and the infrastructure underneath abstracted away. In that way, it can be woken up from dormant and scaled up and down automatically, in line with the size of the workload being put on it. Until then, I wanted (between interim work assignments) to set up a home project to implement a business idea I had some time back.

In doing this, i’ve had a tour around a number of bleeding edge attempts. As a single page app written in JavaScript on Amazon AWS with Cognito and DynamoDB. Then onto Polymer Web Components, which I stopped after it looked like Apple were unlikely to have support in Safari on iOS in the short term. Then onto Firebase on Google Cloud, which was fine until I thought I needed a relational DB for my app (I am experienced on MongoDB from 2013, but NoSQL schemas aren’t the right fit for my app). And then to Django, which seemed to be gaining popularity these days, not least as it’s based on Python and is designed for fast development of database driven web apps.

I looked for the simplest way to run up a service on all the main cloud vendors. After half a day of research, elected to try Django on Digital Ocean, where a “one click install” was available. This looked the simplest way to install Django on any of the major cloud vendors. It took 30 minutes end to end to run the instance up, ready to go; that was until I realised it was running an old version of Django (1.08), and used Python 2.7 — which is not supported by the (then) soon to be released 2.0 version of Django. So, off I went trying to build everything ground up.

The main requirement was that I was developing on my Mac, but the production version in the cloud on a Linux instance — so I had to set up both. I elected to use PostgreSQL as the database, Nginx with Gunicorn as the web server stack, used Lets Encrypt (as recommended by the EFF) for certificates and Django 1.11 — the latest version when I set off. Local development environment using Microsoft Visual Studio Code alongside GitHub.

One of the nuances on Django is that users are normally expected to login with a username different from their email address. I really wanted my app to use a persons email address as their only login username, so I had to put customisations into the Django set-up to achieve that along the way.

A further challenge is that target devices used by customers are heavily weighted to mobile phones on other sites I run, so I elected to use Google’s Material user interface guidelines. The Django implementation is built on an excellent framework i’ve used in another project, as built by four Stanford graduates  — MaterializeCSS — and supplemented by a lot of custom work on template tags, forms and layout directives by Mikhail Podgurskiy in a package called django-material (see: http://forms.viewflow.io/).

The mission was to get all the above running before I could start adding my own authentication and application code. The end result is an application that will work nicely on phones, tablets or PCs, resizing automatically as needed.

It turned out to be a major piece of work just getting the basic platform up and running, so I noted all the steps I took (as I went along) just in case this helps anyone (or the future me!) looking to do the same thing. If it would help you (it’s long), just email me at [email protected]. I’ve submitted it back to Digital Ocean, but happy to share the step by step recipe.

Alternatively, hire me to do it for you!

WTF – Tim O’Reilly – Lightbulbs On!

ianwaring Amazon, Books, Enterprise IT, Facebook, Future Trends, Google, Intellectual Property, Internet, Journalism, Maker Movement, Management, Marketing, Metrics, Money, Patents, Politicians, Privacy, Robots, Shares, Software, Twitter, VCs Leave a comment

What's the Future - Tim O'Reilly

Best Read of the Year, not just for high technology, but for a reasoned meaning behind political events over the last two years, both in the UK and the USA. I can relate it straight back to some of the prescient statements made by Jeff Bezos about Amazon “Day 1” disciplines: the best defence against an organisations path to oblivion being:

  1. customer obsession
  2. a skeptical view of proxies
  3. the eager adoption of external trends, and
  4. high-velocity decision making

Things go off course when interests divide in a zero-sum way between different customer groups that you serve, and where proxies indicating “success” diverge from a clearly defined “desired outcome”.

The normal path is to start with your “customer” and give an analogue of what indicates “success” for them in what you do; a clear understanding of the desired outcome. Then the measures to track progress toward that goal, the path you follow to get there (adjusting as you go), and a frequent review that steps still serve the intended objective. 

Fake News on Social Media, Finance Industry Meltdowns, unfettered slavery to “the market” and to “shareholder value” have all been central to recent political events in both the UK and the USA. Politicians of all colours were complicit in letting proxies for “success” dissociate fair balance of both wealth and future prospects from a vast majority of the customers they were elected to serve. In the face of that, the electorate in the UK bit back – as they did for Trump in the US too.

Part 3 of the book, entitled “A World Ruled by Algorithms” – pages 153-252 – is brilliant writing on our current state and injustices. Part 4 (pages 255-350) entitled “It’s up to us” maps a path to brighter times for us and our descendants.

Tim says:

The barriers to fresh thinking are even higher in politics than in business. The Overton Window, a term introduced by Joseph P. Overton of the Mackinac Center for Public Policy,  says that an ideas political viability falls within a window framing a range of policies considered politically acceptable in the current climate of public opinion. There are ideas that a politician simply cannot recommend without being considered too extreme to gain or keep public office.

In the 2016 US presidential election, Donald Trump didn’t just  push the Overton Window far too to right, he shattered it, making statement after statement that would have been disqualifying for any previous candidate. Fortunately, once the window has come unstuck, it is possible to move it radically new directions.

He then says that when such things happen, as they did at the time of the Great Depression, the scene is set to do radical things to change course for the ultimate greater good. So, things may well get better the other side of Trumps outrageous pandering to the excesses of the right, and indeed after we see the result of our electorates division over BRexit played out in the next 18 months.

One final thing that struck me was how one political “hot potato” issue involving Uber in Taiwan got very divided and extreme opinions split 50/50 – but nevertheless got reconciled to everyone’s satisfaction in the end. This using a technique called Principal Component Analysis (PCA) and a piece of software called “Pol.is”. This allows folks to publish assertions, vote and see how the filter bubbles evolve through many iterations over a 4 week period. “I think Passenger Liability Insurance should be mandatory for riders on UberX private vehicles” (heavy split votes, 33% both ends of the spectrum) evolved to 95% agreeing with “The Government should leverage this opportunity to challenge the taxi industry to improve their management and quality control system, so that drivers and riders would enjoy the same quality service as Uber”. The licensing authority in Taipei duly followed up for the citizens and all sides of that industry. 

I wonder what the BRexit “demand on parliament” would have looked like if we’d followed that process, and if indeed any of our politicians could have encapsulated the benefits to us all on either side of that question. I suspect we’d have a much clearer picture than we do right now.

In summary, a superb book. Highly recommended.

The Next Explosion – the Eyes have it

ianwaring Amazon, Apple, Cloud, Enterprise IT, Future Trends, Google, Privacy, Software 1 Comment
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

ianwaring Amazon, Cloud, Digital Ocean, Future Trends, Google, Internet, Linux, Management, Metrics, Microsoft, Mobiles, Open Source, Software, Wardley Maps Leave a comment

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

ianwaring Amazon, Apple, Cloud, Courses, Diet, Enterprise IT, Fitness, Future Trends, Google, Health, Kickstarter, Low Energy Bluetooth, Medical, Open Source, Software, Statistics, Watches Leave a comment


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

ianwaring Amazon, Android, Apple, Blind Alleys, Enterprise IT, Future Trends, Google, HomeKit, Mobiles

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!

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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

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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.