Entries by Alan Patrick

Soem weeks ago I gave a talk about the "Dark Side of Open Data" at the Open data Institute, where I predicted that the major beneficiaries of government data were not going to be private citizens, taxpayers, or enthusiastic small startups, both large enterprises with deep pockets and less than altruistic service models. The slide I used noted that history tells us any potential goldmine will be mined, and the obvious business model would be:

- Triangulation of Open Data sources and other freely available data (Anonymisation is bunk)

- Buy other private (maybe "lost") data for triangulation (Anonymisation is bunk part II)

- Use Open Data, together with Big Computing to drive products for commercial purposes

As to who would do this, the question I asked I posed was "Which side are all the sharpest knives on?". No surprises than, that today I read in a McKinsey article on trends in Big Data that:

...there was a growing awareness, among participants, of the potential of tapping swelling reservoirs of external data—sometimes known as open data—and combining them with existing proprietary data to improve models and business outcomes. (See “What executives should know about open data.”) Hedge funds have been among the first to exploit a flood of newly accessible government data, correlating that information with stock-price movements to spot short-term investment opportunities.

Which immediately begs the question as, given the government is giving away the data, and the taxpayer funding it, should they be getting a better deal and not letting it go for $0.00?. I contend, in a world where companies such as Facebook valued at c $ 175 bn will pay $19bn for companies like Whatsapp primarily for their user data assets, that the answer is "no".

Another slide I put up was a rather perceptive comment by Jo Bates, of Manchester Metropolitan University, from 2012:

The current ‘transparency agenda’ [of the UK government, supported by prominent Open Data advocates] should be recognised as an initiative that also aims to enable the marketisation of public services, and this is something that is not readily apparent to the general observer.

Further, whilst democratic ends are claimed in the desire to enable ‘the public’ to hold ‘the state’ to account via these measures, there is an issue in utilising a dichotomy between the state and a notion of ‘the public’ which does not differentiate between citizens and commercial interests…”

That point is made loud and clear by the McKinsey news...and this is just the beginning. My last slide but one was about what I predict we will see for the next few years:

- The combination of enthusiasts who see no problems, and commercial interests who intend to make money from the data, will ensure data will get out without adequate protections or safeguards, at low cost

- The people who experience “the problems” (eg data hacked, sold, etc) will have little redress initially, but resistance will increase via social media channels

- There will be scandals, “lessons will be learned”, but little will be done…

- …until there is one scandal too many, too many "little people" will have been damaged, and the pressure to Do Something will be unavoidable.

- Finally there will be (over) regulation, an OfData will be formed, and it will all settle down to business as usual

So it is no great surprise that hedge funds are early entrants, nor that this week news emerged that 13 years of UK health data had already been sold under the radar to insurance companies.

However, there are signs of hope. Days after I gave my talk, the Health Secretary had to abandon plans to sell off health data after a vigorous public protest campaign (waged heavily by social media....) and days later decided they would not sell patient data to such customers. In fact what looks like an early day charter emerged, the Government promised to:

....provide "rock-solid" assurance to patients that confidential information will not be sold for commercial insurance purposes, the Department of Health said.

He is to put a raft of measures in place including a statutory requirement that any patient's opt-out will be respected and legislation that will prevent the Health and Social Care Information Centre (HSCIC) – the body which will control the data – from sharing personal information where there is "not a clear health or care benefit for people".

A Department of Health spokeswoman said this "puts beyond any doubt that the HSCIC cannot release identifiable, or potentially identifiable, patient data for commercial insurance or other purely commercial purposes."

The HSCIC will also be bound by laws to protect patient confidentiality when anonymised data is released. And any researchers who wish to access identifiable data must demonstrate "an ethical reason to do so".

Reading the comments to the report though, it is clear that all the shenanigans and the backlash that finally brought the Government to this point has significantly impacted any trust that this new recommendation will actually be followed - especially as they are going to try yet again to change the law, to be able to make data accessible in a few months time.

The other interesting event today was an abortion charity being heavily fined for being somewhat cavalier with peoples' data and giving it to a hacker. While its a pity its a charity, unless penalties for slack data care are pretty heavy there will be little incentive to look after peoples' data and it will be open season for hackers.

Yes, another one has found it has some Bitcoins missing:

A bitcoin bank has been forced to close after hackers stole 896 bitcoin, worth £365,000, in an attack on Sunday....

"On March 2nd 2014 Flexcoin was attacked and robbed of all coins in the hot wallet,” the statement read. “As Flexcoin does not have the resources, assets, or otherwise to come back from this loss, we are closing our doors immediately.”

Not all of the company’s assets were stolen. In line with best practices for running a bitcoin financial service, Flexcoin held some bitcoins in “cold storage”, keeping them on devices not connected to the internet. Those bitcoins are safe, but only users who explicitly requested their bitcoins be held in cold storage (and paid a 0.5% fee) benefit.

We told ya so....

In the 90's and 'Noughties I made many trips to San Francisco/ the Valley, and as the 90's dotcom bubble built up on I noticed two "non-stock" signals of its frothiness - house prices and occupation of the SoMa (South of Market) area by trendy bars and techie startups:

- House prices rose to the point that educated non techies couldn't afford them, so people like teachers were priced out. This is starting to happen again. (By the way, my "top of market" indicator was when teachers in SF/SV decided to sell and go and teach elsewhere/semi retire based on the huge house price gains)

- SOMA became littered with start up bumf, not old newspapers, and you were tripping over blue-bazered dotcommers instead of winos (SoMa to me has been the flood plain of economic exuberance in the SF/SV area, its use is a good marker of prevailing conditions) - and its filling up with DotCom 2-ers again.

So, another sign of the BubbleTime.

Of course, this time it Will Be Different....

Of course it will....

Incidentally, I recall going back in c 2003 after a 2 year absence and there was a house price tumble almost back to Palo Alto, plus SoMa was full of winos and old newspapers again....

The Tube, if it told the Truth - Kudos Buzzfeed

Every time you think that Twitter has become more silly than it was, something existential like the above emerges in your feed and you stay hooked. That is all you need to know about Twitter's ongoing value proposition.

(Actually.....I have a meeting in town today, I can either get there from Tourist Tat or Eric Pickles.....oh the choices)

I haven't heard much about Prediction Markets for a while, but here is a new one - predicting Innovation - Innovation Excellence:

Prediction markets were popularized in James Surowiecki’s 2004 book, The Wisdom of Crowds. They are systems which forecast the outcome of projects or events based on how willing individuals are to buy “stock” in them. People buy shares in the topics they think will succeed. Each topic or event then gets a value similar to a stock market price. These prices can be interpreted as predictions of the likelihood of the event.

Companies such as Motorola have used prediction markets to engage employees in selecting and predicting which new product ideas will turn into winners. According to Consensus Point who supply the Huunu Prediction Market package, Motorola have doubled the number of ideas pursued and significantly improved time to market. Other users of the Consensus Point platform include Best Buy and Healthstream.

What makes prediction markets better at engaging employees than conventional suggestion schemes is the element of gamification. Exago is an innovation consultancy which supplies a prediction market service with a strong games emphasis. Participants get points to spend on ideas and they get additional points for suggesting ideas. The values of their holdings increase if they back ideas which are turned into implemented innovations. The points can be redeemed for personal prizes or corporate rewards such as attendance at prestigious external conferences or training sessions.

Exago claims that employee participation in their schemes ranges from 20% to 70% which is well above average for large scale suggestion schemes.

Much was predicted for Prediction Markets a few years back, but they faded from view as results were not as stellar as, er, predicted (especially in the US elections), but hope always burns. The reason is typically that the preconditions for them to work are ignored, i.e. that all choices must be made by a heterogenous and fairly large number of people who are in no way influenced by one another or any common intrinsic factors.

If this can be pulled off in companies (or by companies crowdsourcing innovation) it will be a very interesting.

One to watch.

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)

The (non) regulatory annual Bitcoin crash


News today that Mt Gox may well have been turned over - Forbes:

On Monday, a day after CEO Mark Karpeles “resigned” from the Bitcoin Foundation board, the Mt. Gox site shut down citing “ the potential repercussions on MtGox’s operations and the market.”

Meanwhile a leaked document circulated by Bitcoin entreprenuer and blogger Ryan Selkis suggested a massive hack that robbed the site of 744,000 coins, or $380 million at Bitcoin’s current value; that’s 6% of all Bitcoin in circulation. The document is a “crisis strategy” PowerPoint presentation that appears to have been prepared by a third party trying to come up with ways to cover up Mt. Gox’s troubles.

This was always going to happen, as we've pointed out before, and there is no restitution - no one is insuring bitcoin holders against losses. And, just as predictably, post crash there will be regulation:

U.S. lawmakers say the Mt. Gox implosion is proof that the Bitcoin industry requires strong regulatory oversight.

“The disturbing news today from Japan is a reminder of the damage potentially ill equipped and unregulated financial actors can wreak on unsuspecting consumers,” said Sen. Tom Carper (D-Del) in a statement. Carper helped organizing hearings in November that sent Bitcoin’s value soaring. “My staff is working closely with relevant federal agencies to determine what lessons can be learned from this failure to help ensure this does not happen here in the United States.”

“As any industry matures it will face growing pains and there will be individuals who believe they can use the fog of uncertainty to cover up their follies,” said Carper.

It's probably going to happen again before that though, as Bitcoin's decentralisation and lack of oversight is both its strength and Achilles heel

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.

From the BBC, a report on a series of universities trying to build a system that can counter social media borne rumours, lies lies and gossip. The Pheme (maneda fter the Greek goddess of Gossip) is a collaboration between five universities — Sheffield, Warwick, King's College London, Saarland in Germany and MODUL University Vienna — and four companies: ATOS in Spain, iHub in Kenya, Ontotext in Bulgaria and swissinfo.ch, led by Kalina Bontcheva of from the University of Sheffield. Pheme will classify online rumours into four types:

- speculation
- controversy
- misinformation
- disinformation

Apparently different types of digital disngenuity leave their own type of digital footprints and can be recognized. The system will also look at the accounts spreading it and look for bots. Idea is then to search for information that is true from known sources and re-seed the stream of the original falsehood followed with "the truth". It will be ready late 2015 apparently.

The obvious flaw is if it can detect falsehoods, any half decent falsehood spreading system can detect it and re-seed the same trails. The other sad flaw is many people will rather believe a convenient lie than an uncomfortable truth. The war for the truth is about to be fought in the cyber-memespace to an unprecented degree - I wonder if there wll be a new subscience of memetics, called "phemetics".

Google Glasses, 2020 Vision

Google has published a series of "Don'ts" for Google Glass aficionadoes to prevent them being Glassholes - not because of the danger of nerds being disrepected, but because its bad for business - Don't #4:

...creepy or rude (aka, a “Glasshole”). Respect others and if they have questions about Glass don’t get snappy. Be polite and explain what Glass does and remember, a quick demo can go a long way. In places where cell phone cameras aren’t allowed, the same rules will apply to Glass. If you’re asked to turn your phone off, turn Glass off as well. Breaking the rules or being rude will not get businesses excited about Glass and will ruin it for other Explorers.

And Do #2:

Ask for permission. Standing alone in the corner of a room staring at people while recording them through Glass is not going to win you any friends (see Don’ts #4). The Glass camera function is no different from a cell phone so behave as you would with your phone and ask permission before taking photos or videos of others.

But even this still gives huge permission latitude. Most people would find it creepy if someone was sitting in their social situation with someone recording everything, even if there was not a formal "no camera/mike/recording" sign or no real permision required. As TechCrunch notes:

Google’s challenge is not building the Glass platform, but training the general public to welcome Glass wearers into society. Glass’s future rests largely on the public’s acceptance of the technology. If, like Bluetooth headsets, it’s deemed nerdy or, worse, if Glass is lumped in with the NSA privacy scandle, the technology will be an also-ran. A lot is riding on Google Glass Explorers.

The problem is many "Explorers" - being tech nerds - have the social intelligence of...well, tech nerds - the result is increasing unease with Glass, and the emergence of No Glasses Allowed and similar campaigns' hence the publication of the advice. They'd probably also be well advised to give them to more socially aware people rather than the sort of geeks who will pay $1,500 for the privilege of testing them.

But if you think this is creepy, just roll this forward a few generations of Moore's law when you can wear a much less obvious recording device, and it can access a wealth of Big Data Crunching and layer some form of augmented reality over your vision. Here's a scenario - you can walk into a room in 2020, scan all the faces, and do a search of all the data held against those people from a whole range of sources - on Google, from their social networks, from open data given away, and, for a price, from hacked data apps that give you that little bit more. The picture at the top of this post imagines that - imagine a cocktail party where you could see all the dirt on everyone after a guick glass and google.

Creepy? You bet. Impossible? You're fooling yourself - all those in the picture have happened or may soon do so:

- The infidelity App map: How iPhone can secretly keep track on love cheats (Daily Mail, 2011 – Researchers found that could get stored location data out of iPhones if they knew the phone numbers)
- “How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did” (Forbes, 2012, Target stores algorithms identify pregnant girl)
- “Gay? Conservative? High IQ? Your Facebook 'likes' can reveal traits” (NBC – 2013. University of Cambridge's Psychometrics Centre algorithms.)
- BNP member? Beware the Man in the Middle with a Mission! (Guardian, 2009 - BNP membership list appears on Wikileaks)

The other 2 cases are hypothetical, but could come from Government data already in the frame for being opened up (though I note today we have another 6 months grace for medical records).

Playing Charades at parties will never be the same again....