collective unconscious of the smarter planet

Andrew Curry is a forecaster at the Futures Company. Here he writes about how the shape of today’s technology will reshape business - through the lens of Carlota Perez. How will organizations maintain their “edge” (or shape) in an increasingly permeable, fluid world?

New business models emerge late on

Looking at these two diagrams together, one of the striking features of Carlota Perez’ technology surges model is that new forms of business organisation and new business models tend to emerge clearly only in the second half of the surge, and later rather than earlier, although some pioneers are seen before this. Codification of these emerging ideas as management practices isn’t seen until the final decades of the surge. Looking back, for example, Alfred Sloan pioneered some of the main features of the 20th century assembly line business at General Motors in the 1930s, after the crash, but the theory that supported his practices wasn’t developed until the 1960s by writers and academics such as Alfred Chandler and Peter Drucker. In other words, we are only now beginning to see the development of the innovative business models which will characterise the digital technology age.

One of the important aspects of any new technology platform is that the shape of businesses adapt to the shape of the technology, and not the other way around. The steam engine emphasised the concentration of production, and the assembly line the importance of throughput. The successful businesses of those times absorbed these structural lessons, and adapted themselves to fit.

A radical challenge to the idea of the organisation

The core ideas at the heart of the next phase of digital technology are about:

• Pervasiveness
• Unbundling
• Inter-connection
• Reconfigurability

These four characteristics will define the shape of business over the next decade and beyond. Different businesses, and different sectors, will respond differently to these factors, depending, for example, on their value networks, the present mix of data and tangible output, and their wider external constraints. Strategy is always distinctive; every organisation has to find its own route through to a strategic future which is right for it, which matches its capabilities against a landscape shaped by changing social values, by evolving technologies, and by infrastructure, systems, and regulation.

Nonetheless, in all sectors, these characteristics represent a radical challenge to the idea of the organisation, which is at heart defined by boundaries (if it has no boundaries, it ceases to be an organisation). The big question for any organisation, commercial, public, or non-profit, is how it maintains its edges in a world where technology renders so much fluid; relations with customers, suppliers, stakeholders, and employees all start to melt into bytes. Indeed, the distinctions between the organisation and its customers and its suppliers start to blur. These are big strategic shifts which represent a deep challenge to prevailing business structures.

Scott Smith is a forecaster whom I’ve been bumping into again and again over the past couple of weeks’ web wanderings. Here’s a piece he semi-recently posted in response to McKinsey’s “second economy” report. Very interesting.

As a part of ongoing scanning, among the many sources I read at least weekly are job ads (there’s a foresight joke in here somewhere, but I’ll leave it for now). To me, the changing nature of work as described in the evolution of required duties and experience levels are an interesting leading indicator, at least of perception. What do companies think they will need, in what quantity, and based on what skills?

Granted, I’m looking at certain sectors that are the apparent leading edge of services, manufacturing and media, but several big shifts seem to be taking place in the way roles are described, and what skills and experience are required by employers to fill them. Some examples:

• Required education levels are getting higher. A BA or BS is becoming the equivalent of finishing secondary school. Master’s degrees are sought after more and more. Leading to…
• A jump in demand for specialized education. A general master’s level degree doesn’t seem to suffice—it has to be specialized in a niche area. Urban planning, design research, astrobiology, early childhood education. 
• Transdisciplinary capabilities. It’s not enough to have had a breadth of experience, but one needs to be able to call on both sides of the brain, and all areas of practice, all of the time. Graphic design? Check. Interactive design? Check. Project management? Check. Marketing and communication? Check. Research and insights? Check. On and on, a single department or division, rolled into one person. I suspect this is a sort of risk averse trend whereby many potential needs are crammed into one job, requiring not so much as a “T-shaped” individual as a fantasy firefighter/ninja who will cover the inadequacies of others when faced with an increasingly complex and chaotic environment.
• Analytical capabilities. This is the one that jumps out more and more. Problem solving isn’t sufficient. Being able to assess and analyze “insights,” what industry generally calls half-digested data today, is de rigueur. Increasingly, jobs appear to come with a data set, or a petabyte of data, attached. Be prepared to receive a data dump, and capable of telling a coherent story from it. Which means…
• The ability to weave a narrative. One must be able to always tell a story, craft a use case, layer up a persona, or imagine a segmentation. With increasing levels of disruption around them, organizations of all kinds seem to need to tell themselves a story. Who are we? Who do we serve? What is our backstory? What are our customers’/competitors’ motivations? 

The availability of data seems to be driving these latter two elements. As a recent McKinsey Quarterly article called “The Second Economy,” posits, our global economy is in the midst of a sort of molting—the physical economy that grew from the Industrial Revolution has spawned a second economy that is largely invisible, and a product of IT, a data economy. “This vast global digital network that is sensing, ‘computing,’ and reacting appropriately—is starting to constitute a neural layer for the economy.” writes W. Brian Arthur in this piece. “The second economy constitutes a neural layer for the physical economy.” 

Arthur goes on to say that this second economy doesn’t require the jobs the first, physical economy did. In his view, the intelligence we build into the system—the algorithms—do a lot of the lifting individuals used to. A clerk isn’t required to draft a report, move a document along, and make a decision. If you apply for a credit card today, or try to book a one-way flight with cash, somewhere an algorithm is sniffing you, matching your behavior to a set pattern, and rendering judgement. Others are coming to a similar conclusion, with much handwringing about how technology is displacing jobs. 

But the anecdotal evidence of scanning the job postings suggest otherwise. Our data sets are growing larger as the “aspen root system,” as Arthur calls the sensing tendrils of this second economy, grows more extensive. We can collect more, so we have more not to simply analyze, but contextualize and convert to meaningful narrative. This need to see the patterns in the noise itself may be the catalyst for an even higher (or deeper) level economy—a third economy of sensemaking. Collecting and warehousing massive amounts of data is simply an exercise in hording if we can’t see, contextualize, and use the patterns in the noise. (This article on the growing genetic data glut is a great example.)

This evolution will requre even greater, and more meaningful, analytical ability from workers of the near future. If sensing and collection is ubiquitous, then our ability to be analytical polyglots must be as well. Arthur is correct in that fewer people will be required to push a button or shift a document, but we are in serious trouble if we don’t grow the capability to make sense of what we “know” at a pace relative to the speed with which we can collect. Algorithms can only take us so far, which we are seeing even now.

Bruce Sterling on the New Aesthetic… really great. It’s very long - and I’m only excerpting a more critical passage - but the whole thing is worth a read.

The New Aesthetic is a genuine aesthetic movement with a weak aesthetic metaphysics. It’s sticky with bogus lyricism.

I will hammer that iron nail a bit more, in case you aren’t getting it yet. Because this is the older generation’s crippling hangup with their alleged “thinking machines.” When computers first shoved their way into analog reality, they came surrounded by a host of poetic metaphors. Cybernetic devices were clearly much more than mere motors and engines, so they were anthropomorphized and described as having “thought,” “memory,” and nowadays “sight” and “hearing.” Those metaphors are deceptive. These are the mental chains of the old aesthetic, these are the iron bars of oppression we cannot see.

Modern creatives who want to work in good faith will have to fully disengage from the older generation’s mythos of phantoms, and masterfully grasp the genuine nature of their own creative tools and platforms. Otherwise, they will lack comprehension and command of what they are doing and creating, and they will remain reduced to the freak-show position of most twentieth century tech art. That’s what is at stake.

Computers don’t and can’t make sound aesthetic judgements. Robots lack cognition. They lack perception. They lack intelligence. They lack taste. They lack ethics. They just don’t have any. Tossing in more software and interactivity, so that they’re even jumpier and more apparently lively, that doesn’t help.

It’s not their fault. They are not moral actors and they are incapable of faults. It’s our fault for pretending otherwise, for fooling ourselves, for projecting our own qualities onto phenomena that we built, that are very interesting to us, but not at all like us. We can’t give them those qualities of ours, no matter how hard we try.

Pretending otherwise is like making Super Mario the best man at your wedding. No matter how much time you spend with dear old Super Mario, he is going to disappoint in that role you chose for him. You need to let Super Mario be super in the ways that Mario is actually more-or-less super. Those are plentiful. And getting more so. These are the parts that require attention, while the AI mythos must be let go.

The New Aesthetic dusts off the Turing Test in a new Super Mario robot-vision guise, but it can’t get away with that attention-compelling metaphysical maneuver. That’s why it does smell of rubbish, and why the things it assembles look like a dustheap, instead of a coherent creative program to transform the way people perceive their reality.

The New Aesthetic can’t even get away with the seemingly mild error of claiming they’re “metaphorically” the same– that a “render ghost”, for instance, is metaphorically about being a sensitive creative among the hordes in East London who suddenly realizes how many cameras the cops have. No. The British cops have boatloads of surveillance cams, heaps of ‘em. Better cams all the time. That cop network isn’t going to magically become an art connoisseur. The aesthetics of surveillance cams are not value-free. Because aesthetics are not value-free.

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A genuine New Aesthetic in CERN would ask for some aesthetic help there in CERN, in tackling one of the biggest problems in the history of aesthetics. Which is: why is some (but not all) mathematics “beautiful?”

The “beauty” of mathematics is a fact of creative life. The beauty of software code is also a fact of creative life. Math people and coders both know that those beauties are real, real like anvils. Yet that is a truly deep and wicked aesthetic problem. A modern aesthetic movement who could resolve that problem would have a grand achievement. Instead of merely collecting weird seashells on the vast Newtonian shore, they’d be able to state that they had carried out a huge land-reclamation project.

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Our hardware is changing our lives far more profoundly than anything that we ever did to ourselves intentionally. We should heed the obvious there, and get used to that situation. We should befriend one another, under that reality. We should try to see what that means.

Forget workload optimization for big data. Microsoft makes a case for using arrays of cheap, commodity x86 servers to run big data analytics.

Infrastructure vendors’ vision of big data rigs based on scale-out NAS won’t come to fruition, according to the Microsoft executive heading the company’s big data push for SQL Server 2012.

David Campbell, a Microsoft Technical Fellow in the Data & Storage Platform Group claims personal responsibility for Microsoft’s adoption of Hadoop as both an app on the Windows platform and Azure. In conversation with The Register he declared himself “Diametrically opposed” to big data rigs built on scale-out storage. To explain his reason he challenged your correspondent to “Do a census of big data implementations. Ask how many are built on off-the-shelf products and how many are built on scale-out storage.”

All the examples Campbell could list were built on commodity hardware. “The evidence is there if you look,” he said, asserting that the market has already decided that a RAIS – redundant array of independent servers – approach typified by the way containerised data centres operate is superior to the somewhat exotic hardware involved in scale-out NAS or dedicated analytical appliances.

“I used to think the big online players were outliers,” he added. Now he thinks they got the approach to big data right the first time.

Campbell is also cool on the role of the data scientist, analytical experts who blend hard-core data-crunching skills with an understanding of moving bits at scale and can then translate their efforts into business insights. Such workers are in very short supply, he says, and industry cannot assume that the crop currently taking up the first university courses in the discipline will reach the workplace in the next five years.

Microsoft has therefore tooled SQL Server 2012 so it can satisfy a data scientist’s darkest big data desires, then pass the results of their efforts to lesser folk in IT and around the business.

“People ask me if they need to hire a data scientist,” Campbell asked a Microsoft event in Sydney today. “I say that if they can connect their people to the output a data scientist creates, maybe not all the work needs to be done in-house.” To help things along, SQL Server 2012 therefore includes Power View, a new data visualisation too which makes it easy - with the help of Excel - to turn data into something the average executive can understand.

Campbell is also optimistic that, over time, more suit-wearing types will be happy to drive tools like Power View, as “milennials” fluent in Excel syntax enter the workforce and start to crunch their own data.