WTF – Tim O’Reilly – Lightbulbs On!

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

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

Danger, Will Robinson, Danger

One thing that bemused the hell out of me – as a Software guy visiting prospective PC dealers in 1983 – was our account manager for the North UK. On arrival at a new prospective reseller, he would take a tape measure out, and measure the distance between the nearest Directors Car Parking Slot, and their front door. He’d then repeat the exercise for the nearest Visitors Car Parking Spot and the front door. And then walk in for the meeting to discuss their application to resell our range of Personal Computers.

If the Directors slot was closer to the door than the Visitor slot, the meeting was a very short one. The positioning betrayed the senior managements attitude to customers, which in countless cases I saw in other regions (eventually) to translate to that Company’s success (or otherwise). A brilliant and simple leading indicator.

One of the other red flags when companies became successful was when their own HQ building became ostentatious. I always wonder if the leaders can manage to retain their focus on their customers at the same time as building these things. Like Apple in a magazine today:

Apple HQ

And then Salesforce, with the now tallest building in San Francisco:

Salesforce Tower

I do sincerely hope the focus on customers remains in place, and that none of the customers are adversely upset with where each company is channeling it’s profits. I also remember a Telco Equipment salesperson turning up at his largest customer in his new Ferrari, and their reaction of disgust that unhinged their long term relationship; he should have left it at home and driven in using something more routine.

Modesty and Frugality are usually a better leading indicator of delivering good value to folks buying from you. As are all the little things that demonstrate that the success of the customer is your primary motivation.

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.

Crossing the Chasm on One Page of A4 … and Wardley Maps

Crossing the Chasm Diagram

Crossing the Chasm – on one sheet of A4

The core essence of most management books I read can be boiled down to occupy a sheet of A4. There have also been a few big mistakes along the way, such as what were considered at the time to be seminal works, like Tom Peter’s “In Search of Excellence” — that in retrospect was an example summarised as “even the most successful companies possess DNA that also breed the seeds of their own destruction”.

I have much simpler business dynamics mapped out that I can explain to fast track employees — and demonstrate — inside an hour; there are usually four graphs that, once drawn, will betray the dynamics (or points of failure) afflicting any business. A very useful lesson I learnt from Microsoft when I used to distribute their software. But I digress.

Among my many Business books, I thought the insights in Geoffrey Moores Book “Crossing the Chasm” were brilliant — and useful for helping grow some of the product businesses i’ve run. The only gotcha is that I found myself keeping on cross referencing different parts of the book when trying to build a go-to-market plan for DEC Alpha AXP Servers (my first use of his work) back in the mid-1990’s — the time I worked for one of DEC’s Distributors.

So, suitably bored when my wife was watching J.R. Ewing being mischievous in the first UK run of “Dallas” on TV, I sat on the living room floor and penned this one page summary of the books major points. Just click it to download the PDF with my compliments. Or watch the author himself describe the model in under 14 minutes at an O’Reilly Strata Conference here. Or alternatively, go buy the latest edition of his book: Crossing the Chasm

My PA (when I ran Marketing Services at Demon Internet) redrew my hand-drawn sheet of A4 into the Microsoft Publisher document that output the one page PDF, and that i’ve referred to ever since. If you want a copy of the source file, please let me know — drop a request to: ian.waring@software-enabled.com.

That said, i’ve been far more inspired by the recent work of Simon Wardley. He effectively breaks a service into its individual components and positions each on a 2D map;  x-axis dictates the stage of the components evolution as it does through a Chasm-style lifecycle; the y-axis symbolises the value chain from raw materials to end user experience. You then place all the individual components and their linkages as part of an end-to-end service on the result. Having seen the landscape in this map form, then to assess how each component evolves/moves from custom build to commodity status over time. Even newest components evolve from chaotic genesis (where standards are not defined and/or features incomplete) to becoming well understood utilities in time.

The result highlights which service components need Agile, fast iterating discovery and which are becoming industrialised, six-sigma commodities. And once you see your map, you can focus teams and their measures on the important changes needed without breeding any contradictory or conflict-ridden behaviours. You end up with a well understood map and – once you overlay competitive offerings – can also assess the positions of other organisations that you may be competing with.

The only gotcha in all of this approach is that Simon hasn’t written the book yet. However, I notice he’s just provided a summary of his work on his Bits n Pieces Blog yesterday. See: Wardley Maps – set of useful Posts. That will keep anyone out of mischief for a very long time, but the end result is a well articulated, compelling strategy and the basis for a well thought out, go to market plan.

In the meantime, the basics on what is and isn’t working, and sussing out the important things to focus on, are core skills I can bring to bear for any software, channel-based or internet related business. I’m also technically literate enough to drag the supporting data out of IT systems for you where needed. Whether your business is an Internet-based startup or an established B2C or B2B Enterprise focussed IT business, i’d be delighted to assist.

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.

On the unusability of internal systems. Ugh!

Enterprise Apps - Notes Needed

 

Saw this picture alongside an excellent blog post today. Does this look familiar?

The company have probably spent many millions buying software to automate their business processes or to fulfil all manner of other objectives. But the User Interface and Operating Nuances are so involved, the poor user has to keep a notebook to hand to help navigate around the mess served to them. And they have to interact with their ultimate customers with a smile on their face, protecting them from the mess behind the scenes.

If that was served up on a phone handset, no consumer would touch it with the longest bargepole known to man. One of the things that plays on my mind is how to disrupt these vendors. Or the companies whose directors decide to buy this stuff and inflict this (and the associated costs) to their downstream customers.

Jon Barrett had a lot of the glue to sort this phenomenon with Digital’s Jabberwocky project back in the early 1990’s, with what amounted to be an Enterprise Software Bus with some basic screen scraping functionality. At least pilot users could string together some business process interactions atop those disparate applications that behaved in a way that today’s mobile phone users might have found a bit more palatable. It’s been a long time since, and little apparent progress.

In the meantime, the blog post by Leisa Reichelt is here. Well worth a read.

Footnote: within 12 hours of posting this, I read an excellent article here on the failure of a “Choose and Book” system on which over £300m was spent. Reading the drains up, it looks like a set of top level objectives were being pursued, but with no appreciation of the unwanted constraints being placed on the users of the resulting service, so the whole thing fell into disrepute. Like the old dutch proverb: “a ship on a beach is a lighthouse to the sea”.

Nadella: Heard what he said, knew what he meant

Satya Nadella

That’s a variation of an old “Two Ronnies” song in the guise of “Jehosaphat & Jones” entitled “I heard what she said, but knew what she meant” (words or three minutes into this video). Having read Satya Nadella’s Open Letter to employees issued at the start of Microsoft’s new fiscal year, I did think it was long. However, the real delight was reading Jean-Louis Gassee – previously the CTO of Apple – not only pulling it apart, but then having a crack at showing how it should have been written:

Team,

This is the beginning of our new FY 2015 – and of a new era at Microsoft. I have good news and bad news.The bad news is the old Devices and Services mantra won’t work. For example: I’ve determined we’ll never make money in tablets or smartphones.

So, do we continue to pretend we’re “all in” or do we face reality and make the painful decision to pull out so we can use our resources – including our integrity – to fight winnable battles? With the support of the Microsoft Board, I’ve chosen the latter.

We’ll do our utmost to minimize the pain that will naturally arise from this change. Specifically, we’ll offer generous transitions arrangements in and out of the company to concerned Microsoftians and former Nokians.

The good news is we have immense resources to be a major player in the new world of Cloud services and Native Apps for mobile devices.

We let the first innings of that game go by, but the sting energizes us. An example of such commitment is the rapid spread of Office applications – and related Cloud services – on any and all mobile devices. All Microsoft Enterprise and Consumer products/services will follow, including Xbox properties.

I realize this will disrupt the status quo and apologize for the pain to come. We have a choice: change or be changed.

Stay tuned.

Satya.

Jean-Louis Gassee’s  full take-home on the original is provided here. Satya Nadella should hire him.

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.

Death of the Web Home Page. What replaces it??

Go Back You Are Going Wrong Way Sign

One of the gold nuggets on the “This week in Google” podcast this week was that some US News sites historically had 20% of their web traffic coming in through their front door home page. 80% of their traffic arrived from links elsewhere that landed on individual articles deep inside their site. More recently, that has dropped to 10%.

If they’re anything like my site, only a small proportion of these “deep links” will come from search engine traffic (for me, search sources account for around 20% of traffic most days). Of those that do, many arrive searching for something more basic than what I have for them here. By far my most popular “accident” is my post about “Google: where did I park my car?”. This is a feature of Google Now on my Nexus 5 handset, but I guess many folks are just tapping that query into Google’s search box absolutely raw (and raw Google will be clueless – you need a handset reporting your GPS location and the fact it sensed your transition from driving to walking for this to work). My second common one is people trying to see if Tesco sell the Google Chromecast, which invariably lands on me giving a demo of Chromecast working with a Tesco Hudl tablet.

My major boosts in traffic come when someone famous spots a suitably tagged Twitter or LinkedIn article that appears topical. My biggest surge ever was when Geoffrey Moore, author of “Crossing the Chasm”, mentioned my one page PDF that summarised his whole book on LinkedIn. The second largest when my post that congratulated Apple for the security depth in their CloudKit API, as a fresh change to the sort of shenanigans that several UK public sector data releases violate, appeared on the O’Reilly Radar blog. Outside of those two, I bump along at between 50-200 reads per day, driven primarily by my (in)ability to tag posts on social networks well enough to get flashes of attention.

10% coming through home pages though; that haunts me a bit. Is that indicative of a sea change to single, simple task completion by a mobile app? Or that content is being littered around in small, single article chunks, much like the music industry is seeing a transition from Album Compilations to Singles? I guess one example is this weeks purchase of Songza by Google – and indeed Beats by Apple – giving both companies access to curated playlists. Medium is one literary equivalent, as is Longreads. However, I can’t imagine their existence explains the delta between searches and targeted landing directly into your web site.

So, if a home page is no longer a valid thing to have, what takes it’s place? Ideas or answers on a postcard (or comment here) please!