Wednesday, March 26. 2014
King Digital stock price fall on IPO day courtesy Yahoo Finance
It was no great surprise to us that the makers of Candy Crush, King Digital Entertainment Plc , had a less than illustrious IPO. As we wrote in February, it was high time to run for the IPO gate before it closed on them, and they have. Good luck to them, they now have $500m in the bank now, a useful cushion against the slings and arrows of outrageous future misfortune. The c £8.5bn valuation (now c $7bn) will not be quite so easy to live with, I suspect.
But nothing changes our analysis since February, this stock is a still very high risk punt, and sold at Bubbletime prices to Bubble-minded investors to boot.
Tuesday, March 25. 2014
Who needs Google Glass with these goggles...
Facebook has bought a tiny Virtual Reality company with a near-virtual product for $2bn (mainly stock) - Grauniad.
Kudos to Oculus, it started as a kickstarter project 2 years ago and has taken some serious funding. And actually, its likely that goggles or glasses of some sort will be the view-screen of choice at some point. This is clearly where Facebook see it going, as Mark Zuckerberg notes:
After games, we're going to make Oculus a platform for many other experiences. Imagine enjoying a court side seat at a game, studying in a classroom of students and teachers all over the world or consulting with a doctor face-to-face -- just by putting on goggles in your home.
This does point to a rather interesting tussle between Google Glasses and Facebook Goggles for nerdiest eyewear, and it also points to a new tussle for video screenware device-as-portal. But this is very early days for a virtual product for virtual reality, to go for for $2bn. Still, its all virtual money and it all works out in the Bubbletime.
Can't wait for the iGlass now.....you just know its coming.
Wednesday, March 12. 2014
Everybody loved Archie & Veronica before 1993
Today is the 25th "birthday of the Web", although strictly speaking that was the first proposal submitted, a memetic sperm if you like. The actual thing only came out in any recognisable form about 4 years later in 1991. For me it arrived 5 years after it's birth.
It was summer 1994, the day I downloaded the newly released NCSA Mosaic software on Windows (It was already out on UNIX, but I didn't have a UNIX tin anymore). Suddenly, all that stuff I'd been doing before was easy (FTP, Gopher et al). The thing about Mosaic was it was easy to use, put graphics where you actually wanted them and - most importantly - didn't crash! ("Browsing" had been around in buggy pieces of software - Cello anyone - before that, but Mosaic lit the spark - and sealed CompuServe and AOL's doom).
The rest, as they say, is history.
Anyway, with a sad (but quick) wave goodbye to Archie and Veronica, and grabbing my trusty basic guide to HTML, I started writing my first website.
Today of course you can knock out a whole web experience in the time it took to write one page then, but what the hell, that was web hacking early 90's style.
(Archie and Veronica were pre-Web search engines for FTP and Gopher respectively)
Monday, March 10. 2014
Kudos Gangstersout blog
Two pieces of news in quick succession - Friday, drones are cleared for use commecially in the US* - Pando Daily:
And then today: news in that two major US legal practices, LeClairRyan and McKenna Long, have set up Drone case chasing groups. - Washington Post:
What a marvellous world......still, as the picture above shows, the hunting season could be prolonged all year
*Update: The FAA have appealed, which means the drones don't fly until its settled, and there will be lots of lawyers droning on about drones
Friday, March 7. 2014
Some 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, but 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:
As to who would do this, the question I posed was "Which side are all the sharpest knives on?". No surprises then, 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.
The issue is that there is major asymmetry between those that stand to gain (a few corporation s and companies) and those that stand to lose (citizens who have their data appropriated and misused with no recompense). That point is made loud and clear by the McKinsey news...and this is just the beginning, I'd predict. 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 exact problems it will cause, will ensure data will get out without adequate protections or safeguards, at low cost (to the buyers)
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 for a pittance (to be fair, it was sold for modelling purposes, but the fact remains no one had agreed their data should be sold).
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, as 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.
Reading the comments to that report though, it is clear that all the shenanigans and the backlash that finally brought the Government to this point has significantly reduced 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.
Wednesday, March 5. 2014
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....
We told ya so....
Monday, March 3. 2014
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)
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....
Friday, February 28. 2014
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)
Wednesday, February 26. 2014
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.
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.
Tuesday, February 25. 2014
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:
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)
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