Strategy

The British Army has resurrected the Chindits, one of the special force units used in WW2 to operate behind enemy lines - in their case to operate behind Japanese lines in Burma. It was formed by Brigadier Orde Wingate following a successful usage of Boer commando style tactics in the British/Commonwealth reconquest of Italian East Africa in 1940.

The new Chindits are to fight behind the lines in the digital wars - Torygraph:

...the new unit's focus will be on "unconventional" non-lethal, non-military methods such as "shaping behaviours through the use of dynamic narratives", an Army spokesman said.

The development is part of a major restructuring of the military under the Army 2020 plan, which will see the military scale down to around 82,000 regular troops in the next five years.

The spokesman said: the brigade "is being created to draw together a host of existing and developing capabilities essential to meet the challenges of modern conflict and warfare".

It's part of the (belated, in my opinion) realisation among Western armies that from the end of the Korean War onwards, asymmetric warfare is the new "conventional" warfare. The new Chindits are designed to combine "conventional" irregular warfare psy-ops and disinformation techniques with a major digital capability to use social media and other digital information technologies.

One thing to keep in mind - the Chindit experiment ultimately failed militarily, their raids were characterised by dysentry wracked men carrying huge loads marching uselessly for days without contacting any enemy, high casualty rates due to disease not enemy action, and US (air) support was essential to keep them in the field*. They were retained more for PR purposes on the home front than any military effectiveness.

One hopes that the same will not happen now.....

(*This is not a criticism of the brave men who served in the Chindits, it's that an approach finessed for dry African conditions ultimately did not work owing to the jungle environment)

There is a crisis in the British Accident & Emergency (A&E) system at the moment, the causes are are a "perfect storm" of previous decisions, mainly around reduction in funding in the end to end care system overall, and the usual crop of winter bugs, especially ones that over-impact the ever-increasing numbers of elderly. But this week I discovered a possible self-inflicted cause - their own expert self-diagnosis system on the NHS website will erroneously send people who can safely medicate at home to A&E.

(As I understand it is the same system that the medically unqualified telephone helpers on the 111 system use for diagnosis, with similar results, it would seem).

TL;DR- I would suggest the NHS expert system is too poor a model of symptoms and then defaults to the lowest risk position - i.e. to send anybody with the slightest probabiliy of something serious to A&E, causing an unwarranted increase in demand for the most expensive part of the formal medical sytem. And that this lesson applies to similar expert systems being touted everywhere by the Technorati as the Next Big Thing.

The idea is laudable - you go online or ring up the 111 helpers and "self provision" your diagnosis, and potentially self treat, thus saving the expensive professional medical system a lot of time, capacity & money. An excellent idea, especially as the "family doctor" system in the UK only really operates for about 10 out of 24 hours at best, and even to access that in a hurry is damn difficult if you are working. So you go to the online help out of hours, or even in hours if you can't get an appointment that day.The flaw is that both the web system and the tele-helper are relying on an Expert Algorithm, that unfortunately isn't.

This is how it (doesn't) work. A member of my family started getting pretty ill with aching limbs, nausea and vomiting, and a blinding headache. If you go on the symptom website with any permutation of these symptoms, the system soon decides that you may have meningitis, a very nasty condition, and tells you to get to A&E post haste, if not sooner.

However, these symptoms are also the near-identical symptoms for Norivirus, or Winter-Flu, a nasty bug but one that can be safely self treated at home in the vast majority of cases.

Needless to say, the probability of my family member having Norivirus with those symptoms, in winter, while the bug is occurring at its max, is infinitely more likely than having meninigitis. The expert system does not seem to have this "probabiity" function however, and appears to default straight to a lowest risk stance "this could be meningitis - go to A&E now!"

Even so, this Expert System is probably OK for the single case - but now scale it to a country of 50 million or so people, make it the easiest method of getting advice, scale down on the more skilled alternative (aka "doctors") as a second opinion and and multiply me by all the other people getting hit by the current Norivrus bug going round, and you have very likely generated a large number of unnecessary visits to A&E by the "Worried-not-quite-well" who have had the bejeezus frightened out of them by having an automated system that can't discriminate properly between a dangerous condition and a nasty winter bug, and drops to the lowest risk position of "get thee to A&E".

Also, you'd expect the system to try and do a few clarifying questions, given the huge difference in potential outcomes in a diagnosis of norivirus vs meningitis. There are a few to be fair - far fewer than I'd have expected though - but even then they are odd. For example, one question is "have you flown in from abroad in the last 3 weeks". Well yes, we have, as a matter of fact. But I was then expecting the system to ask where from - we flew in from alpine Europe, not exactly a dangerous disease hothouse. Now I don't know if the lack of the "where did you fly from" question in this "Expert" system was to make it easier to use, or to not offend anyone, but to my mind it was a pretty useless way of separating norivirus from meningitis.

The main issue with the question chain though, was you sat there looking at the screen thinking that none of these options properly described the issue - where was the "other/tell us what really happened/that isn't right" button?

Anyway, our diagnosis was sorted by talking to a real expert system - our doctor - over the telephone the next morning. "Oh yes, there is a lot of it going round right now, here's a prescription coming via email, go to XXX pharmacy and pick up the medicine". Five minutes on the 'phone with a real expert system = no panicked visit to A&E, no blocking resources for those who need it more, etc etc. But to use this option you (i) have to take the risk that it isn't meningitis on yourself over an anxious night, and (ii) have the self confidence that is probably the right call despite the prevalence of lurid medical "advice" Google gives at the touch of a button. Not a good failsafe resort for a nationally deployed medical expert system.

I would therefore bet a lot of money that a lot of people, especially out of hours, used that website or 111 and, when told to get to A&E PDQ, went there in a panic and helped no little bit in creating the huge crunch in A&E over the last few days. It may have only been a small % uplift, but the way capacity constrained complex systems work is they don't degrade linearly, they degrade in a non linear (aka accelleratingly worse) mode until they collapse.

Now it is something of a belief in the Technorati that these expert systems are inevitable and will be the saviour of many services/automate many people out of jobs/be the great leap forward for mankind etc etc (choose your Future of Technology belief set) but this experience maps onto something far more prosaic that I have seen repeatedly in the 30 or so years I've been building system models and simulations, to wit that:

- no simulation ever captures the overall complexity of reality
- any error, no matter how small, is magnified massively when a high volume of "events" (the simulation term for, in this case, a user going through the diagnosis) are thrown at it
- those errors then cause huge downstream problems long before they are detected
- fixes are put in, usually with intervention by much higher cost informal side-systems
- all too often, a more compex edifice is created than the one it replaced
- the system winds up costing far more from its errors than any putative savings

In other words, be very careful of how these early-day expert systems are deployed, as their errors could cost a hell of a lot more than the theoretical savings they may generate (unless, as in the case of the banks in 2007/8, you can make the public pay of course).

It's always fun to look at all the various predictions of the Next New Things (given they vary quite widely by predictor, by year - us being no exception). Anyway, here are Gartners for the next few years (abridged by Broadstuff)

1) By 2018, digital business will require 50 percent less business process workers and 500 percent more key digital business jobs, compared with traditional models.

Near-Term Flag: By year-end 2016, 50 percent of digital transformation initiatives will be unmanageable due to lack of portfolio management skills, leading to a measurable negative lost market share.


2) By 2017, a significant disruptive digital business will be launched that was conceived by a computer algorithm.

Near-Term Flag: Through 2015, the most highly valued initial public offerings (IPOs) will involve companies that combine digital markets with physical logistics to challenge pure physical legacy business ecosystems.


3) By 2018, the total cost of ownership for business operations will be reduced by 30 percent through smart machines and industrialized services.

Near-Term Flag: By 2015, there will be more than 40 vendors with commercially available managed services offerings leveraging smart machines and industrialized services.


4) By 2020, developed world life expectancy will increase by 0.5 years due to widespread adoption of wireless health monitoring technology.

Near-Term Flag: By 2017, costs for diabetic care are reduced by 10 percent through the use of smartphones.


5) By year-end 2016, more than $2 billion in online shopping will be performed exclusively by mobile digital assistants.

Near-Term Flag: By year-end 2015, mobile digital assistants will have taken on tactical mundane processes such as filling out names, addresses and credit card information.


6) By 2017, U.S. customers' mobile engagement behavior will drive mobile commerce revenue in the U.S. to 50 percent of U.S. digital commerce revenue.

Near-Term Flag: A renewed interest in mobile payment will arise in 2015, together with a significant increase in mobile commerce (due in part to the introduction of Apple Pay and similar efforts by competitors, such as Google increasing efforts to drive adoption of its NFC-enabled Google Wallet).


7) By 2017, 70 percent of successful digital business models will rely on deliberately unstable processes designed to shift as customer needs shift.

Near-Term Flag: By the end of 2015, five percent of global organizations will design "supermaneuverable" processes that provide competitive advantage.


8 ) By 2017, 50 percent of consumer product investments will be redirected to customer experience innovations.

Near-Term Flag: By 2015, more than half of traditional consumer products will have native digital extensions.


9) By 2017, nearly 20 percent of durable goods e-tailers will use 3D printing (3DP) to create personalized product offerings.

Near-Term Flag: By 2015, more than 90 percent of durable goods e-tailers will actively seek external partnerships to support the new "personalized" product business models.


10) By 2020, retail businesses that utilize targeted messaging in combination with internal positioning systems (IPS) will see a five percent increase in sales.

Near-Term Flag: By 2016, there will be an increase in the number of offers from retailers focused on customer location and the length of time in store.

For what its worth, based on Broadstuff's advanced TTID algorithm and patented BGA prediction methodology (see end of post for definition), we can safely predict that:

1. Most of these will take far longer to play out than pedicted, and many that do play out will have far less impact than supposed
2. The further out these predictions go, the wronger they are
3. At least 30% of these definite trends will be completely different by next year, and the year after, so by 2018 the Top 10 will be unrecognisable.

Which of course is what Gartner's other great prediction system invention, the Hype Curve, tells us - only when something passes out of any hype trend, does it finally become useful.

The other thing I am left scratiching my head about, is that given the Great Hollowing Out*, if all these come to pass (most of these trends imply yet more waged employees being dumped or offshored), where will the money come from to buy all those marvellous new things these new lean businesses make? Even Henry Ford saw that one coming when he upped wages so employees could buy his cars....

* I am always amused by The Economist calling the Hollowing Out trend all a Myth in 2004, when it was patently obvious it was already happening. But of course, for that we use the MRD approach

For reference:

TTID = This Time It's Different. The algorithm states that whenever this claim is made, it isn't, and put your hand tightly on your wallet

BGA = Bill Gates Algorithm - This (X) will have far less impact in 2 years than we think, and far more in 10. We apply the Chasm upgrade though, which states that most New New things will never jump over the chasm and will be dashed on therocks in trying. About 3 of the above will be survive, on average - which 3 would you bet on?

MRD = Mandy Rice Davies corollary - "Well, They Would, Wouldn't They" - Always look carefully at where someone is coming from before following where they lead....

Richard Dawkins (respected/hated Evolutionary Biologist and loved/hated Atheist) has touched off yet another Twitterstorm, via that unfortunate habit Evolutionary Biologists (and Vulcans) have of looking at humans from a viewpoint of mass mathematical game theory participants, rather than as - well, humans. Cue yet another Twitterstorm du Jour. (Huffington Post summaries it best):

To prepare you for the inevitable repeat here are the 12 stages of any Richard Dawkins Twitter scandal:

1. The eminent biologist will employ the rigid rationalism of his discipline to a highly emotive issue – the lack of Nobel prizes for Muslims or how some types of rape are worse than others. Dawkins will then share this insight with his one million followers on Twitter.

2. A cluster of Dawkins’ devotees will debate the professor’s contention in a reasoned and scientific fashion.

3. Someone negatively affected by Dawkins’ clinical assertion will spot the tweet and take issue with his post, replying "really?? #twat".

4. A Twitter user with Jesus/crescent moon as their profile picture will call Dawkins a "c*nt", likening the biologist to Josef Mengele and/or Harold Shipman. Soon thereafter Herr Hitler will be invoked.

5. A journalist will spot the reaction, read Dawkins' original tweet and pen a quick article highlighting the "prominent atheist’s latest Twitter storm".

6. A member of the blue tick Twitter elite – a newsreader or "social commenter" – will pick up on the rumpus, tweeting how the professor’s original post was "indefensible" and how these comments are "the worst yet".

7. Twitter users with #reason, #doubt and #MissTheHitch in their profile will distance themselves from Dawkins, telling their 73 followers that The God Delusion author no longer speaks for "atheists/anti-theists”.

8. Dawkins will continue to defend his position, while other media outlets pen similar hit-focused articles on the brouhaha, many highlighting his past Twitter indiscretions. Right-wing media in the US will pick up on the tempest, decrying Dawkins as the emblem of a world "abandoned by God".

9. People personally affected by the issue of Dawkins’ original post will pen angry responses to Independent Voices and the Huffington Post, many concluding with the line: "How can such a clever man can be so stupid?"

10. Dawkins will issue an apology via his website for the "misunderstanding" and though he will concede his "phraseology" was wrong he will maintain his "logic" was sound.

The irony of Dawkins being called "immoral" by religious and various other "strong beliefs" based groups often proposing far worse things is piquant, but there is an even bigger irony here with Mr Dawkins doing this. He actually was the first person to coin the term "meme" and to postulate how they work. So, depending on your point of view on Mr D, he is either a master memeticist or a complete c*nt who has been hoisted on his own memetic petard by the #Offended on Social Media.

Oh yes - there were 12 points to the Huffpo article, and these are the clinchers I think:

11. Attempting to squeeze a few last hits out of the now-subsiding "outrage", a journalist will write a meta-piece attempting to explain the anatomy of a Dawkins Twitter scandal*.

12. Wait 90 days and repeat.

He is clearly a master memetic tactician therefore, Twitterstorms and meta-pieces being the sign of memetic success - but whether continuing to offend large numbers of people in exchange for viral Twitter publicity is a good strategic memetic play is less clear. Wildean theory says it is effective, but in a Social Media Age where everything you say remains online to be held against you, it may not be. After all, one of the first lessons of social game theory is being nice wins - eventually...

*13. Attempting to extract the last ounces of traffic, a blogger writes a snarky piece on the whole affaire...

Very interesting article in the Economist about "Entrepreneurial" vs "Innovation" economics. the whole article is well worth reading as it is one of those very rare items in the UK "Tech Startup" space - a systemic analysis with actual numbers. There are 3 key points dealing with the UK's current Startup / "Every Person is an Entrepreneur" craze:

Firstly, State money is wasted on funding too many entrpreneurial SMES with too little money, they do nothing for the economy overall:

....once you take into account the number of SME jobs lost after the first three years of their creation, there is very little net job creation by these firms. Only 1% of new enterprises have sales of more than £1 million six years after they start. Research at the University of Sussex shows that median sales of a six-year-old firm is less than £23,000 (Storey, 2006). These firms also tend to be the least productive and least innovative (R&D spending—the best measure we have for inputs in the innovation process—in Tech City is not higher than in other parts of London or Britain). Indeed, the few high growth innovative firms (about 6% of the total SME group, Nesta, 2011)—those that really should be supported—do not directly benefit from the hype that surrounds SMEs and startups: once they get the funds these are too diluted to make a difference.

Secondly, what the State should be funding is an Innovation ecosystem, not an Entrepreneurial/Startup one per se

Innovation-led “smart” growth has occurred mainly in countries with a big group of medium to large companies, and a small group of SMEs that is spun out from some of those large companies or from universities. These firms have benefited immensely from government funded research. Indeed, in my book I show how many firms in Silicon Valley have benefitted directly from early-stage funding by government, as well as the ability to build their products on top of government funded technologies.

The author points out that nearly every "entrepreneurial startup" in Silicon Valley today would not exist without huge US government funded projects that underpin it's technology, and direct low cost (aka non VC) early days investment - Apple is a case in point:

Every technology that makes the iPhone smart was government-funded (internet, GPS, touch-screen display, SIRI). Apple spends relatively little on R&D compared with other IT firms precisely because it uses existing technology. It applies its remarkable design skills to these technologies, effectively surfing on a government-funded wave. Apple, Compaq and Intel also all enjoyed the benefits of early-stage public funds (SBIC in the case of Apple, SBIR in the case of Compaq and Intel).

Thirdly the UK's state spend on innovation and pull through is small by competitive standards. Silicon Valley was largely built on the huge government backed spendiing, not the VC community - and it is probably still the real case:

Silicon Valley firms were initially not funded mainly by venture capital. It came in after the ball had got rolling thanks to funding by the Department of Defence, the Department of Health and, more recently, the Department of Energy. In fact, there is increasing evidence that many startups are told by venture-capital firms to go first to SBIR and then come back (Block and Keller, 2013).Venture-capital funds are not providing the kind of patient long-term finance needed for radical innovations. They are too focused on a profitable “exit”—usually through an IPO or a sale to a bigger company—within 3-5 years. But innovation often takes 15-20 years.

This sort of state pull through is what China is using too, and the numbers are measured in $ Trillions. In fact even in Europe, the UK underperforms hugely on this sort of state investment:

In Germany such links are created by well-funded Fraunhofer Institutes. In Britain these are being imitated through the Catapult centres, which in theory should be linked to Tech City-type projects, either through procurement policy or via learning. Currently there are no links between these. And whereas the Fraunhofer system has an annual research budget of €1.8 billion ($2.4 billion) and a network of 20,000 staff across 60 centres (in 2010), Britain’s Catapult centres were given just £200m to spend over 4 years. When the Tech-City gurus in Number 10 Downing Street criticise the Technology Strategy Board, which is in charge of the Catapult strategy, for not being more like Darpa, they ignore the very different size of TSB’s budget in comparison with Darpa—and even more the fact that the TSB does not have the market creating potential that Darpa does.

Leads to a fourth point, about the competence of No 10's "Tech City Gurus" and advisors - but that's for another post. To end though, the observation is if small beer is what the government is willing to put into the game, its better to spend it on tertiary education and R&D, where impacts are proven, rather than launch a million underfunded startups:

Research at the University of Cambridge (Hughes 2008) suggests that the British government spends (directly and indirectly) close to £8 billion ($13 billion) annually on SMEs—more than it spends on the police and close to the amount it spends on universities. Is this warranted? How do we know it would not be better to simply direct that money to teachers where there is plenty of evidence that quality education raises human capital and growth.

This post is just a summary, I recommend reading the article.

Interesting 2 paragraphs from Fred Wilson's blog, talking about "what's next":

But the roadmap has been clear for the past seven years (maybe longer). The next thing was mobile. Mobile is now the last thing. And all of these big tech companies are looking for the next thing to make sure they don’t miss it.. And they will pay real money (to you and me) for a call option on the next thing.

It isn’t clear if the next thing is virtual reality, the internet of things, drones, machine learning, or something else. Larry doesn’t know. Zuck doesn’t know. I don’t know. But the race is on to figure it out. Trillions of dollars of collective market capitalizations are on the line. So a couple billion here or there is chump change. Except for the people who collect that chump change for selling them an option on the next thing. It’s real money to us.

I'm intrigued by the idea of a call option, I think it could be executed better than via VC funding though, Fred - now that would be disruptive

But I think Fred's largely right that Mobile was the last Next Thing - though strictly speaking its not "Mobile" now per se, but PC level processing power meeting Moore's Law and shrinking in size and price so it can be easily portable, with a damn good UI (think iPaq then iPhone). These "Smart" phones and "tablets" killed good old Planet Mobile dead in about 3 years (Motorola, Nokia, Blackberry - where are they now? They were earth shaking giants a few short years ago!)

Anyway, where is the Next Big Thing to be found is the question Fred asks. The future is of course here, just unevenly spread, so the trick is to see what bits of the future are here, now - and actually are going somewhere. Ten things that have changed exponentially in the "networked technology" areas we follow, in the time we've been writing Broadstuff (est 2006) are:

- Robotics (including the flying type)
- Data generation, usage and cost of processing it (aka "Big Data" - but I think the mpact will be felt indirectly - see this video by Phili Evans of BCG
- Penetration and usage of very cheap personal comms....
- .....especially those that are "Things"...
- ....and used to scrape data about "marketing value units" (aka people)
- WiFi Connectivity is increasingly ubiquitous, and whenever a powerful new comms system does that, big changes follow
- Smaller and smaller machine tools (including the over-hyped "3D Printer" but also a whole host of CNC tools)
- Games have moved from PCs to every device, and some games aren't really games any more as the principles move into other areas
- All manners of virtual/augmented reality devices (if Google Glasses don't make them irredeemably uncool)
- "Open" (ie other people's involuntarily exposed) Data - but sadly this will, in my opinion, probably be massively abused and then clamped down again, but its a short term shooting star.

As you can see, these are hardly New New Things, just things that were already here in 2006 and even then clearly had high potential. What's interesting is that they were all already on very predictable development vectors in 2006, but no one looked at them as killer technologies in those days. That was because at that time, their rate of development was still mainly all theoretical, and not provably valuable. To compare, here are 10 other things that were also floating around in 2006/7 that I thought also could happen sooner and haven't yet, but still may as they are all Big Next Next Things potentially.

- Widespread revolt against privacy-invasive free business models (it's starting now though)
- Modular hardware (Fred may recall Bug Labs from 2007)
- Open & portable social networks.
- "New" Search technologies (of which many have been tried over the last few years, a New New search hope appears every 2 years or so)
- Low cost and easy organisation of unstructured data
- Self learning software sorting out my media consumption, inbox and schedule (preferably materialising as a cross-media avatar with Marlene Dietrich's voice)
- VRM (Consumer to Business commercial tools)
- Effective voice recognition (the acid test - can it understand the Scottish accent...)
- And on that note, a near-real time "Babelfish" online service (to translate Scottish to English....)
- Various self-learning Algorithms that could predict things like the stock market, drive cars, etc etc

These are all here today, unevenly distributed, and still chugging along - but at slower rates than the various laws of networking, learning, Moores et al would predict. Typically there is a something in them that is missing, obstinately sticking at current capability or economically unavailable, awaiting the "key" to their leap over the Chasm. But all it takes is a small shift (think iPaq vs iPhone again) and over they go.

All you have to do to build your own mind-boggling portfolio of New Next Things To Watch is read the various Gartner Hype Curves for the last 10 years, and you will see a slew of things on the hot S curve one year and disappearing 2-3 years later. They don't go away though, and are still evolving in the Darwinian mud of technology species, it's just that something hasn't yet quite worked out for them yet. And somewhere in that stew already, are the next 10 New New Things.

Impact of mathematical techniques on operations, by industry - McKinsey


McKinsey has discovered you can use Operations Research (or Decision Maths as it is known these days) mathematical techniques to analyse and optimise manufacturing operations - McKinsey Insights:

The application of larger data sets, faster computational power, and more advanced analytic techniques is spurring progress on a range of lean-management priorities. Sophisticated modeling can help to identify waste, for example, thus empowering workers and opening up new frontiers where lean problem solving can support continuous improvement. Powerful data-driven analytics also can help to solve previously unsolvable (and even unknown) problems that undermine efficiency in complex manufacturing environments: hidden bottlenecks, operational rigidities, and areas of excessive variability. Similarly, the power of data to support improvement efforts in related areas, such as quality and production planning, is growing as companies get better at storing, sharing, integrating, and understanding their data more quickly and easily.

Not only that, but you can apply Lean operating techniques in manufacturing companies too:

Nonetheless, to get the most from data-fueled lean production, companies have to adjust their traditional approach to kaizen (the philosophy of continuous improvement). In our experience, many find it useful to set up special data-optimization labs or cells within their existing operations units. This approach typically requires forming a small team of econometrics specialists, operations-research experts, and statisticians familiar with the appropriate tools. By connecting these analytics experts with their frontline colleagues, companies can begin to identify opportunities for improvement projects that will both increase performance and help operators learn to apply their lean problem-solving skills in new ways.

Amazing stuff....except its very, very old news. Monte Carlo simulations and capacity planning algorithms have been around for decades, a lot of it even pre-dates WW2. Value analysis started at 3M in the 1960's. Richard Schonberger wrote the groundbreaking Japanese Manufacturing Techniques in 1982 (I still have my copy) and he was merely Westernising something the Japanese had been doing for 2 decades by then. And then I saw this, which really made me smile wryly:

Similarly, a leading steel producer used advanced analytics to identify and capture margin-improvement opportunities worth more than $200 million a year across its production value chain. This result is noteworthy because the company already had a 15-year history of deploying lean approaches and had recently won an award for quality and process excellence. The steelmaker began with a Monte Carlo simulation, widely used in biology, computational physics, engineering, finance, and insurance to model ranges of possible outcomes and their probabilities

The wry smile was because I did much the same, in 1994-5, for a steelmaker, using some of these exact same techniques - while I was consulting at McKinsey to boot. I have the obligatory picture of big rolling mills from a grateful client, and the prize I won in the McKinsey internal "Practice Olympics" to prove it In fact I'd bet the McKinsey Quarterly in the 1970's, 80's and 90's will be full of analyses like this one. There truly is nothing new under the sun.

But with New Improved Big Data it can all be rebadged bright and new....except it doesn't work this way. There was a shedload of Big Data in the Old Days too (shop floor data capture techniques underpin most of the Internet of Things, and did you know some of the first broadband networks in the world went in at manufacturers in the 1980's). Manufacturing has always had a lot of data, and Big Manufacturers bought Big Iron to process Big Datasets then too (except it was called data with a small "d" then). The Monte Carlo methods, or N jobs on M machines Optimisation (for examples) are still the same algorithms they were in the 1930's and 50's.

And you know what - you just cannot simulate the minute operation laden details of a shop floor or logistics network reliably. No matter how big your dataset, or your computers, or your machine tool onboard intelligence, there is just too much variability. Which is why the Just In Time/Lean movement came about as the better approach - the aim was to simplify the problem, rather than hit it with huge algorithm models and simulations so complex no one fully understood what they were doing anymore (just ask the banks what happens going down that route) - the aim of JiT/Lean was to actually reduce the problem variability, to get back to Small Data if you like.

And you know what else - despite the analytical miracles I and many others performed in the day, despite the extraordinary efforts by managements and workers, so many of those steel mills (and clothing companies, and manufacturers of a million other widgets) moved East. There is only so much you can do against cheap labour, national subsidies and guaranteed government contracts.

And that brings me to something else in the story, which is what is really going on here I suspect - its not Big Data, its Big Economics:

Many [of these companies] are lean veterans: these companies cut their teeth during the 1990s (when sagging prices hit a range of basic-materials companies hard) and more recently doubled down in response to rising raw-materials prices. The benefits they’re enjoying—an extra two to three percentage points of margin, on top of earlier productivity gains (from conventional lean methods) that often reached 10 to 15 percent—suggest that more big data applications will be finding their way into the lean tool kits of large manufacturers.

Sure, its partly about raw material prices changing - when they are too high to buy or too low to sell you really have to be efficient at manufacturing. But when you are getting to this level of number crunching, after 20 years of Lean projects, in my experience it's because the endgame is appearing on the horizon, its a last of the summer wine story, the end of an S curve. Interestingly, it seems like all the McKinsey consultants and the project were in India, and Eastern labour costs are rising, as is oil for those long ship rides back to the European and US markets, so much so in in fact that there is an increasing trend in re-shoring, as production is coming back to the US and EU. Big picture, the low cost Eastern windfall is ending, and you have to start getting much smarter again about the actual manufacturing process. You can get benefits from doing it right with Big Iron and Big Algorithms, no doubt - but this sounds like back to the future....I suspect they are now using bigger and bigger number crunching to eke the last 20% of improvements from the various kaizen projects ongoing, trying to keep the factories in situ as the Big Economics shift yet again.

And you didn't need Big Data to tell you that....

(Hat tip to my colleagues at the Agile Elephant for the link)

News just in that Facebook has bought WhatsApp for $SillyMoney - $19.6bn - TechCrunch:

With 450 million monthly users and a million more signing up each day, WhatsApp was just too far ahead in the international mobile messaging race for Facebook to catch up, as you can see in the chart above we made last year. Facebook either had to surrender the linchpin to mobile social networking abroad, or pony up and acquire WhatsApp before it got any bigger. It chose the latter.

Facebook couldn't afford not to have it, if someone else had bought it that would have made a direct attack on Facebook's chosen strategic way out of its own declining user engagement, i.e. mobile applications and messaging. Facebook is absolutely determined not to be overtaken by the "next wave" Social Networks. But its own "new wave" systems were kludgy, so the price of not having your lunch eaten in 2016 is c $20bn in 2014. There is a bit of irony in that WhatsApp strongly do not believe in advertisng, their founder once saying that:

"There's nothing more personal to you than communicating with friends and family, and interrupting that with advertising is not the right solution,"

Clearly $19bn is a mind changing amount

What this really shows is that the days of Facebook's organic growth are over, and from now on in they are going to have to acquire revenue, and thats a very expensive way of doing it at their size if you are continually needing to buy the guys who will eat your lunch. You had to believe a lot to believe Facebook's valuation - that just got harder. But its the Bubbletime, so all will be good - for a while

Update 1 - According to El Reg, Whatsapp does indulge in datascraping of a user's address book, so that does make it an interesting prospect for added value datamining.

Update 2 - Azeem Azhar of PeerIndex has a smart bit on analysis - if WhatsApp had stayed independent it still would have destroyed Facebook's mobile story, which really outlines why Facebook had to act to stop itself being eaten for lunch:

- WhatsApp would continue to grow: It was rumoured to be growing at 1m downloads per day, so on an annualised basis close to doubling its user base over the next 12 months, and showing few signs of slowing down.
- It would (and still will) race past Facebook: WhatsApp has already grown faster than Facebook to date. The proposition is terrifically clear and simple. It's like SMS messaging but faster, easier, "free'r" and more fun. Fun Facebook and Twitter may be, there is a platonic simplicity about WhatsApp. There is very little new stuff to learn about its interaction paradigms. No question about what is public or private. Just get up and go. And WhatsApp had been able to stretch us into photo messaging.
- The network effects would take hold: If all (or most) of your friends are on WhatsApp, Viber or Kik or BBM are going to be very lonely places to be. The network effects would take deep root and except in markets where there is limited crossover of users (thing China), WhatsApp will dominate, the way eBay dominated auctions. The best network for a user is the one your friends are on.

Bertrand Duperrin's summary presentation


I've written up the 2 days of case studies from the Enterprise 2.0 Summit I attended last week in Paris, they are over here - Day 1 and Day 2 - on the Agile Elephant blog. There are a number of common threads emerging from the studies. Being lazy, I've copied Emanuele Quintarelli's list to start with, based on his study (as they concur with my analysis), I've added my thoughts in italics:

- The project is explicitly supported and sponsored by the top management (70% vs 34% for laggards). Long lasting processes, technology and process change should be somewhat mandated by the formal organization. Informal projects are easy to start but need formal acceptance to embed themselves in the enterprise

- Social collaboration doesn’t happen without a significant effort. While Enterprise 2.0 may appear as inspired by public social networks, your intranet or employee community isn’t Facebook. Successful projects have a dedicated and professional support (sponsor, community managers, other departments involved) in 91% of the cases (vs 51% of the others). Point was made again and again that the service has to be actively seeded in its early days and reach a critical mass to work

- The right budget should be there. Like any other project, social transformation requires meaningful investments. This means more than 500K Euros or more for 27% of the leaders versus 8% of remaining participants. Even more importantly, the available money should be spent more on the people and strategy dimension than on the technology. Informal systems are point systems, they won't deliver ful value unless they can integrated with other social systems and the other business systems.

- A hybrid roll-out strategy is by far the most effective approach. Top performers use a mixed top-down and bottom-up way process two times more than laggards (52% vs 27%). Senior management involvement should then be balanced with a key role attributed to end-users in shaping both the priorities and the chosen solutions. When uncertain, end-users come first and early-on in the design process. The concept of "chasm jumpers" was interesting, having a group of super users able to explain to the "early majority what the benefits for them are, as early adopters cannot usually explain this n terms later users can understand.

- Culture and business potential are no longer an issue. The strategic importance of social initiatives is proven by the ability of leading organizations in overcoming the “wrong culture” and “not a priority” objections. These two arguments still represent the primary reason of failure for all the others (40% vs 15% for the business priority and 27% vs 10% for the culture). The conclusion again and again was that the projects have to be seen to be solving business problems - and must be usable - for people to use them.

- Measuring participation and business outcomes is not optional. An amazingly high portion of companies aren’t yet looking at collaboration from a quantitative perspective given that 50% of our sample hasn’t adopted any metric. This is true only for 9% of the top performers and many of them look not just for the level of activity (like, views, comments, etc) but especially for the business outcomes (revenues, cost reduction, new products, etc). There is still a strong debate about measurement, some "old schoolers" believe it loses the human element, some pragmatic managers worry that it will put people off using the systems if they feel watched and others that what you measure gets done, but not other things you may want. I think its early days with this area and needs a gentle touch.

Other stand-out observations so far from the case studies:

- Any system needs time to embed/mature/settle in (words varied, but the concept was the same) before it becomes stable and self sustaining, you can't "make a baby in 1 month with 9 mothers" as it were.

- A communication plan is necessary to spread the word, business systems don't "go viral". Also, making onboarding as hassle free as possible and knocking out the "rocks in the road" of the systems as quickly as possible once they appear - and expect them to appear - the projecst that iterated rapidly as user behaviousr exosed flaws seemed to do better.

- Software cost matters - there seemed to be a general pattern of low risk, i.e. if the software already existed in the stack (MS Yammer or Lync, IBM Connexions) it tended to be used.

Over the 2 days I did sense a departure of what the case studies were showing vs. what some of the Social Business theorists were espousing, in general the case studies showed that pragmatism and evolutionary development (what Dachis' Dion Hinchliffe called "Sustainable Transformation") was the order of the day vs more revolutionary/dramatic transformational approaches.

Update - I have also put up Bertrand Duperrin's slides (at top) now that they have been posted up, it was a very good talk on the subject as well

Just had a chance to finally read Janet Parkinson's latest post on this subject - it was to me the stand-out concept of our recent Patchwork Elephant conference, but she has pushed her ideas further - in essence, she argues that “The future is here, it’s just not evenly distributed yet” for businesses becoming just another part of the social network:

Let’s take the social platform Airbnb to illustrate what we mean. Founded in 2008 by Brian Chesky and his roommate when they charged visitiors to sleep on their apartment floor, Airbnb has risen within 6 years to arrange 10m stays in 550 000 rooms in 34,000 cities and is likely to become the world’s largest hotelier within the next year.

As the collaborative economy grows there will no doubt be thousands of other markets – if not all markets eventually – which will become nodes in the social networks. Social networks will allow any capacity to find any demand. Functions like Airbnb, Uber and Lyft will become part of the infrastructure. Just as once we needed AOL to have a web experience now the internet is part of the infrastructure. So it will be with online markets with rating services. That means that any business may have no option but to eventually become just a node in this social mesh – in other words a social object.

In other words "the social network" has become a business market that has already allowed a new form of hotel business to rapidly become almost the biggest market for beds in just a few years. As Janet points out, this is just the beginning:

The [minimum] size of the firm in the case of a supplier to Airbnb is nothing more than your spare room and an internet connection. Ebay was a forerunner to this – but it’s becoming clearer that eventually all the world will become a social market. Any business which tries to limit transparency and remain opaque or is trying to create arbitrage where there is none will find it difficult to compete and maintain their strategic position.

And eventually this will just be a part of "the matrix". Janet also mentions Ronald Coase's theories about transaction costs in making this happen (ie the social network will match buyers and sellers at a fraction of the present costs), I hope thats because she's heard me bang on about them all the time - but the endgame she sees is actually more Nick Carr:

Over time, this mesh will become regulated – infrastructures always do. Electricity, water, telephony all ended up as part of the utility infrastructure and this will be no different. The main problem for the individual will be the sheer scale of the mesh – we will need tools to navigate it. Some tools will come from the infrastructure itself but we imagine that some tools will come from yourself. This ties in closely with the VRM concept of tools being created for individuals to manage and control their own data, allowing access only to those to whom they give permission. We could imagine us all owning our own smart systems with data controlled by ourselves – a bit like owning an electric appliance which you plug into the mesh – that could source the relevant data, barter the deal and present the options in order of importance, then automatically make all the necessary arrangements for you. The opportunity for profiteering in these transactions would be minimal – regulation would be complex.

I think that is still the optimistic outlook, I can imagine quite a few stones on the road - but history has shown other once hard-to-do things have just been absorbed into the infrastructure....eventually.