Here's a sample from my email inbox, which arrived yesterday.
I know how important your organization's big data strategy is. That's why I want to personally invite you to attend our webinar.
How does he know? Is he basing his knowledge on big data or extremely small data? I'm curious to know which.
And what is his idea of a personal invitation? Does he think that personalization is achieved by having his email software insert my first name into the first line? Gosh, how very customer-centric!
But at least the email arrived at a civilized time. Unlike the one that arrived as I was getting into bed the other night, from an eCRM system whose idea of personalization didn't extend to checking what time zone I was in. I guess one must be grateful for these small mercies.
When Complex Event Processing (CEP) emerged around ten years ago, one of the early applications was real-time risk management. In the financial sector, there was growing recognition for the need for real-time visibility - continuous calibration of positions – in order to keep pace with the emerging importance of algorithmic trading. This is now relatively well-established in banking and trading sectors; Chemitiganti argues that the insurance industry now faces similar requirements.
In 2008, Chris Martins, then Marketing Director for CEP firm Apama, suggested considering CEP as a prospective "dog whisperer" that can help manage the risk of the technology "dog" biting its master.
But "dog bites master" works in both directions. In the case of Eliot Spitzer, the dog that bit its master was the anti money-laundering software that he had used against others.
And in the case of algorithmic trading, it seems we can no longer be sure who is master - whether black swan events are the inevitable and emergent result of excessive complexity, or whether hostile agents are engaged in a black swan breeding programme. One of the first CEP insiders to raise this concern was John Bates, first as CTO at Apama and subsequently with Software AG. (He now works for a subsidiary of SAP.)
|from Dark Pools by Scott Patterson|
And in 2015, Bates wrote that "high-speed trading algorithms are an alluring target for cyber thieves".
So if technology is capable of both generating unexpected events and amplifying hostile attacks, are we being naive to imagine we use the same technology to protect ourselves?
Perhaps, but I believe there are some productive lines of development, as I've discussed previously on this blog and elsewhere.
1. Organizational intelligence
- not relying either on human intelligence alone or on artificial intelligence alone, but looking for establishing sociotechnical systems that allow people and algorithms to collaborate effectively.
2. Algorithmic biodiversity
- maintaining multiple algorithms, developed by different teams using different datasets, in order to detect additional weak signals and generate "second opinions".
John Bates, Algorithmic Terrorism
(Apama, 4 August 2010). To Catch an Algo Thief
(Huffington Post, 26 Feb 2015)
John Borland, The Technology That Toppled Eliot Spitzer
(MIT Technology Review, 19 March 2008) via Adam Shostack, Algorithms for the War on the Unexpected
(19 March 2008)
Vamsi Chemitiganti, Why the Insurance Industry Needs to Learn from Banking’s Risk Management Nightmares..
(10 September 2016)
Theo Hildyard, Pillar #6 of Market Surveillance 2.0: Known and unknown threats
(Trading Mesh, 2 April 2015)
Neil Johnson et al, Financial black swans driven by ultrafast machine ecology
(arXiv:1202.1448 [physics.soc-ph], 7 Feb 2012)
Chris Martins, CEP and Real-Time Risk – “The Dog Whisperer”
(Apama, 21 March 2008)
Scott Patterson, Dark Pools - The Rise of A. I. Trading Machines and the Looming Threat to Wall Street (Random House, 2013). See review by David Leinweber, Are Algorithmic Monsters Threatening The Global Financial System?
(Forbes, 11 July 2012)
Richard Veryard, Building Organizational Intelligence
The Shelf-Life of Algorithms
Where are Uber's real competitors? The obvious answer would be the traditional taxi operators in large cities. Taxi services are usually controlled by city authorities or other regulators, to ensure that the prices are fair, and that the drivers and the vehicles are safe. Taxi drivers in various cities have protested against Uber, arguing that it cheats regulation by using unlicensed drivers to undercut prices. However, regulators (such as the UK CMA) have sometimes decided that consumer interests are best promoted by allowing Uber to compete with established providers.
Uber is therefore selling itself three ways - not only to passengers and drivers but also to regulators. In a sense, this makes it a three-sided platform.
However, as discussed in my earlier posts, some commentators are dubious that Uber can ever be profitable in this competitive space, even with substantial deregulation in its favour. What Uber really wants (they argue) is to persuade city authorities to stop investing in public transport, to stop subsidizing buses and subsidize Uber transport instead. If other competing modes of transport are decommissioned, the Uber business model starts to look quite different - just another privatized yet publicly subsidized monopoly, supposedly independent but effectively underwritten by the government.
All you need to know about Uber
(BBC News, 9 July 2015) Uber says TfL cab proposals 'against public interest'
(BBC News, 2 October 2015)
Does Uber have an ally in the CMA?
(Maclay Murray & Spens, 12 October 2016)
Anne-Sylvaine Chassany, Uber: a route out of the French banlieues
(FT, 3 March 2016)
Dave Lee, Is Uber getting too vital to fail?
(BBC News, 10 December 2016)
(Nov 2016) Uber Mathematics 2
Aside from the discussion of Uber as a two-sided platform, addressed in my post on Uber Mathematics
(Nov 2016), there is also a discussion of Uber's overall growth strategy and profitability. @izakaminska has been writing a series of critical articles on FT Alphaville.
There are a few different issues that need to be teased apart here. Firstly, there is the fact that Uber is continually launching its service in more cities and countries. Nobody should expect the service in a new city to be instantly profitable. The total figures that Kaminska has obtained raise further questions - whether some cities are more profitable for Uber than others, whether there is a repeating pattern of investment returns as a city service moves from loss-making into profit. Like many companies in rapid growth phase, Uber has managed to convince its investors that they are funding growth into something that has good prospects of becoming profitable.
Profitability in Silicon Valley seems to be predicated on monopoly, as argued by Peter Thiel, leveraging network effects to establish barriers to entry. This is related to the concept of a retail destination
- establishing the illusion that there is only one place to go. Kaminska quotes an opinion by Piccioni and Kantorovich, to the effect that it wouldn't take much to set up a rival to Uber, but this opinion needs to be weighed against the fact that Uber has already seen off a number of competitors, including Sidecar. Sidecar was funded by Richard Branson, who asserted that he was not putting his money into a "winner-takes-all market". It now looks as if he was mistaken, as Om Malik (writing in the New Yorker) respectfully points out.
But is Uber economically sustainable even as a monopoly? Kaminska has raised a number of questions about the underlying business model, including the increasing need for capital investment which could erode margins further. Meanwhile, Uber will almost certainly leverage its cheapness and popularity with passengers to push for further deregulation. So the survival of this model may depend not only on a continual supply of innocent investors and innocent drivers, but also innocent politicians who fall for the deregulation agenda.
Philip Boxer, Managing over the Whole Governance Cycle
Izabella Kaminska, Why Uber’s capital costs will creep ever higher
(FT Alphaville, 3 June 2016). Myth-busting Uber's valuation
(FT Alphaville, 1 December 2016). The taxi unicorn’s new clothes
(FT Alphaville, 13 September 2016) FREE - REGISTRATION REQUIRED
Om Malik, In Silicon Valley Now, It’s Almost Always Winner Takes All
30 December 2015)
Brian Piccioni and Paul Kantorovich, On Unicorns, Disruption, And Cheap Rides
(BCA, 30 August 2016) BCA CLIENTS ONLY
Peter Sims, Why Peter Thiel is Dead Wrong About Monopolies
(Medium, 16 September 2014)
Peter Thiel, Competition Is for Losers
(Wall Street Journal, 12 September 2014)
Related Posts Uber Mathematics
(Nov 2016) Uber Mathematics 3
Two contrasting approaches to Brexit from architectural thought leaders.
Dan Onions offers an eleven-step decision plan based on his DASH method, showing the interrelated decisions to be taken on Brexit as a DASH output map.
|A decision plan for Brexit (Dan Onions)||
|A stakeholder map for Brexit (Dan Onions)|
Let me now contrast Dan's approach with Simon Wardley's. Simon had been making a general point about strategy and execution on Twitter.
Knowing Simon's views on Brexit, I asked whether he would apply the same principle to the UK Government's project to exit the European Union.
Simon's diagram revolves around purpose. OODA is a single loop, and the purpose is typically unproblematic. This reflects the UK government's perspective on Brexit, in which the purpose is assumed to be simply realising the Will of the People. The Prime Minister regards all interpretation, choice, decision and direction as falling under her control as leader. And according to the Prime Minister's doctrine, attempts by other stakeholders (such as Parliament or the Judiciary) to exert any governance over the process is tantamount to frustrating the Will of the People.
Whereas Dan's notion is explicitly pluralist - trying to negotiate something acceptable to a broad range of stakeholders with different concerns. He characterizes the challenge as complex and nebulous. Even this characterization would be regarded as subversive by orthodox Brexiteers. It is depressing to compare Dan's careful planning with Government insouciance.
Elsewhere, Simon has acknowledged that "acting upon your strategic choices (the why of movement) can also ultimately change your goal (the why of purpose)". Many years ago, I wrote something on what I called Third-Order Requirements Engineering, which suggested that changing the requirements goal led to a change in identity - if your beliefs and desires have changed, then in a sense you also have changed. This is a subtlety that is lost on most conventional stakeholder management approaches. It will be fascinating to see how the Brexit constituency (or for that matter the Trump constituency) evolves over time, especially as they discover the truth of George Bernard Shaw's remark.
"There are two tragedies in life. One is to lose your heart's desire. The other is to gain it."
Dan Onions, An 11 step Decision Plan for Brexit
(6 November 2016)
Richard Veryard, Third Order Requirements Engineering
Based on R.A. Veryard and J.E. Dobson, 'Third Order Requirements Engineering: Vision and Identity', in Proceedings of REFSQ 95, Second International Workshop on Requirements Engineering, (Jyvaskyla, Finland: June 12-13, 1995)
Simon Wardley, On Being Lost
Related Posts: VPEC-T and Pluralism
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