Corporate Evolution (Strata NY 2013)

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The impact of the information age on corporate evolution and how you might think about making your company smarter.

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  • Terrific presentation! Most of us are familiar with Metcalfe's Law (that the value of a network goes up by the square of the number of participants). Jim Stogdill's is the first I've seen on the better question: how do our linkages advance our *thinking*? The 'We are Here' slide (Slide 21) is the most thought-provoking single slide I've seen in years -- A wonderfully rich answer follows. Great stuff!
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  • Hi, this talk is about the information age and an it’s impact on corporate evolution. It may not be obvious to you, but the company you work for is growing a brain. And it’s probably your job is to make it smarter.
  • Let’s start with what might sound like an odd question. Can a company have an IQ? How smart is the company you work for? Have you ever considered the question? Do you have any idea how you might go about answering it?
  • During the industrial age this would have been simple. The answer would have been: “as smart as the person running the company.” But now?
  • Let’s establish some context before we dive into that question… The modern corporation was created during the industrial age to organize mass pools of labor into specialized tasks, …
  • … at facilities like the River Rouge plant in Dearborn Michigan.
  • While Industrial era corporations wereprimarily organizing physical labor, they needed to be effective information processors to accomplish it. The system of control they used was the bureaucracy they adaptedfrom Napoleon's government administration. Bureaucracy, literally, “desks” from the French, consisted of functional departments focused on specific tasks of the organization. They held and moved paper between them for a combination of record keeping and control.
  • More than just hierarchy, rules, and specialization though, bureaucracy was a systems architecture. It was the original technology stack that businesses used for passing messages, storing state, and processing data.
  • At that stage of its evolutionary development, the industrial age corporation was about equivalent in sophistication to the nematode. The nematode is an incredibly plentiful and diverse but very simple worm with a simple nervous system. (We are no where near lizard brain territory yet)
  • The typical nematode’s nervous system consists of only 302 neurons. In fact, its core pharyngeal nervous system (essentially its brain) contains only 20, yet it is able to effectively manage homeostasis, direct movement, detect information in the environment, create complex responses and even learn a little bit.
  • Of course the nematode isn’t “conscious” of any of this. These are dispositional responses; essentially deterministic reflexes encoded in that simple network. The worm has dispositions to move toward food for example.These dispositions aren’t unlike the rules and processes encoded in a corporate bureaucracy. They are adequate for managing responses to specific (and expected) stimuli (like a competitor’s pricing change) within expected ranges but not more.
  • In1954, Joe Glickauf of Arthur Anderson implemented a payroll system for General Electric on a UNIVAC 1. This is one of the first general purpose computers used to automate a traditionally paper-based business process in the U.S. (and the beginning of IT consulting). Systems like this were adopted rapidly throughout the 1950’s and thus began the corporate shift into the information age.
  • Of course, even after corporations began to rely on computers for their data processing tasks, business at this stage remained fundamentally bureaucratic. Which is to say still hierarchical, based on fixed rules, and specialized functions.
  • In fact, as we automated the existing bureaucracy, we usually just adapted previous paper based systems into computer code. Invoices and trades and accounts and inventories etc. all migrated into the machine. We emptied our filing cabinets into database tables.
  • Throughout those projects from the 1950’s and beyond we didn’t fundamentally evolve the worm’s nervous system, we mostly set about digitizing it in its existing form. Substituting digital automation, controls, and record keeping for paper. In fact, that’s mostly what we’ve all been doing for the last 55 years, wiring the worm and automating existing bureaucracy.Through most of this period, the corporation remained dispositional and reactive - it did become more responsive, efficient, and scalable.And all of this workserves as the departure point in the corporation’s evolutionary history to come. That digital foundation would become the substrate on which further evolutionary processes could occur.More lizard, less worm?
  • So, to summarize. (or, by digitizing it’s worm brain, he or she made a lizard brain)
  • Then we added networks to the mix.Right in the middle of this process, about 30 years ago or so, Leonard Kleinrock, Lawrence Roberts, Robert Kahn, and Vint Cerf invented the computer network that ultimately became the internet, and by the mid to late 1990’s these technologies began to have an profound impact on the corporation…
  • Now instead of just automating internal processes, we began to focus on integration with trading partners, etc.
  • Our little worm was beginning to sense, and in very rudimentary ways, interact with the other worms. It was learning to speak, and its lingua franca is xml.
  • And all of that network connectivity isn’t just changing the corporation’s external interactions. With the rise of new communication and collaboration mechanisms it is changing how we organize internally, if not intentionally, then in an ad hoc emergent way. Org charts are becoming less relevant as we organize organically in direct response to the work. Many corporations officially remain hierarchical, but they have lots of ad hoc connections that serve as an overlay network on top of it. An emergent overlay network on top of the existing hierarchy. When a network changes in response to conditions, it’s learning. And learning is the definition of intelligence.
  • And we *need* that increasing internal organizational complexity because a hierarchical organization can never be smarter than the people who run it, but our worm’s extended ecosystem is just too complex for that small group of managers to understand and react to.So we need internal structures, processes, and time cycles that are better able to cope.We need the corporation to be smarter than the people that run it. And in fact, I smarter than the sum of all of the people in it.
  • So, computing digitized the worm and the network age has allowed emergent network overlays to form on top of the traditional bureaucratic hierarchical organization. Our worm is evolving, maybe it even has a lizard brain now, but still it’s really just getting started.We are here at a conference about data and analytics. I think that large scale data analysis represents a third “epoch” of the information age and will have tremendous ramifications on the subsequent evolution of the corporation.
  • Let’s switch to the topic of intelligence. There are many definitions of “general intelligence” but it turns out that it is very highly correlated with working memory. Which is essentially the ability to capture sensory perceptions and hold them in an accessible state to reason against.Which reminds me of this…
  • … the humble web log. Originally nothing more than an ephemeral store of troubleshooting information, the big data era essentially began with the realization that they should be stored instead of discarded, and that they were rich with signal to analyze and reason with.
  • And it’s not just web logs anymore. Companies now have enhanced powers of sensory perception, memory at scale, and the growing ability to reason on the contents of that memory in near real time without human intervention.The modern corporation has sensory perception and working memory that it’s blind nematode predecessors could only dream about, if they were capable of dreaming.The addition of analytical algorithms embedded into its systems will let it reason in real time against the data in those perceptions, often without human intervention.
  • This is where the corporation makes the jump from a lizard brain to something qualitatively more evolved.A major evolutionary transition for animals was the formation of brain regions that mapped internal and external states. The corporation has been storing internal states as transactional data for a long time, sort of a form of digital kinesthesis. BI was the way the arms told the head what they were doing. But now, with all of those sensors in the world, all those new perceptions, it’s beginning to store approximations of the state of its environment, at scales and resolutions never contemplated before. Like a mammal brain.
  • Corporations, like brains will accrete map-based working memory and the kinds of intelligence they enable as layers on top of previous era dispositional capabilities.Our existing automation and transactional systems will still be there working in concert with this new “big data” layer of sensing, mapping, and reasoning.
  • So companies evolving and getting smarter, and memory is giving them important new capabilities. But can you influence the phenotype of your specific company? How would you think about this if there was such thing as a corporate IQ test and your bonus was based on increasing it every year?What measures of intelligence would you focus on and how would you address them? What if financial analysts who follow your company start to measure you, at least partially, this way?
  • So working memory is an important part of general intelligence, but it’s not the entire story. It’s worth looking at a more complex definition to see if it hints at other things we might be thinking about.IQ, or “g” for general intelligence is defined in many different ways. But this definition of IQ from the Cattell – Horn – Carroll theory is useful as a model for discussion. These are not all the concepts that make up the theory. I’ve left out things like visual and auditory processing because they make less sense in this context. But when thinking of a corporation perhaps other things would replace them.
  • Fluid intelligence (Gf) includes the broad ability to reason, form concepts, and solve problems using unfamiliar information or novel procedures.
  • Crystallized intelligence (Gc) includes the breadth and depth of a person's acquired knowledge, the ability to communicate one's knowledge, and the ability to reason using previously learned experiences or procedures.
  • Quantitative reasoning (Gq) is the ability to comprehend quantitative concepts and relationships and to manipulate numerical symbols.
  • Short-term memory (Gsm) is the ability to apprehend and hold information in immediate awareness, and then use it within a few seconds.Long-term storage and retrieval (Glr) is the ability to store information and fluently retrieve it later in the process of thinking.
  • Processing speed (Gs) is the ability to perform automatic cognitive tasks, particularly when measured under pressure to maintain focused attention.Decision/reaction time/speed (Gt)reflects the immediacy with which an individual can react to stimuli or a task (typically measured in seconds or fractions of seconds; it is not to be confused with Gs, which typically is measured in intervals of 2–3 minutes).
  • Looking at this it’s clear that some of these things remain the domain of humans while others are clearly best accomplished with information technology. This hints at the degree of human machine symbiosis we can expect as machines take on more autonomous roles in the corporation but humans retain importance in the areas where human intelligence excels. It is probably obvious that we are talking about the corporation as almost a pure information processing machine in the information age. This isn’t about coordinating labor anymore, it’s about coordinating the thought of humans with the “thought” of machines.
  • We’ve been arguing about the definition of human intelligence for over a century now and still don’t have complete agreement, so I doubt we’ll arrive at an agreeable measure of organizational intelligence overnight. But the component parts of human intelligence make a useful starting point for thinking about the emerging intelligence of the companies you work for.And maybe this suggests a different way of thinking about managing them. Companies really aren’t about organizing labor anymore, they are almost entirely about information processing. And in a complex rapidly changing world, the intelligent companies will win.I think it would be pretty cool if someday financial analysts were paying as much attention to your company’s components of IQ as they do to direct financial results.
  • Corporate Evolution (Strata NY 2013)

    1. 1. The Evolving Corporation Being smarter about getting smarter
    2. 2. IQYourCo ≈ ?
    3. 3. IQFord ≈ IQ
    4. 4. From: http://theformofmoney.blogharbor.com/blog/_archives/2009/3/24/4131366.html
    5. 5. http://www.autolife.umd.umich.edu/Race/R_Overview/Rouge_Plant.htm
    6. 6. Photo Jan Banning http://www.mymodernmet.com/profiles/blogs/bureaucracies-around-the-world
    7. 7. Image source: http://www.inra.fr/hyppz/DESSINS/8032041.gif
    8. 8. Image: http://acrobeles.wordpress.com/2010/06/21/pristionchus-pharynx-connectome/
    9. 9. http://www.osa-
    10. 10. Photo: http://www.accenture-blogpodium.nl/best-place-to-work/accentures-history-becoming-a-global-player/
    11. 11. Financ e HR Sales ERP Admin Planning Ops Inventory Image source http://dineshns.blogspot.com/2008/12/microsoft-aspnet-mvc-frame-work-and.html
    12. 12. Wiring the worm http://www.wormatlas.org/hermaphrodite/neuronalsupport/mainframe.htm
    13. 13. Bureaucracy is your is your company’s lizard brain. And your CIO digitized it. http://sarahgoshman.com/wp-content/uploads/2011/12/lizard-brain.jpg
    14. 14. http://www.lamag.com/featuredarticle.aspx?id=13526&page=2 http://www.let.leidenuniv.nl/history/ivh/chap2.htm http://www.velocityguide.com/internet-history/lawrence-roberts.html http://www.computerhistory.org/internet_history/internet_history_70s.html http://www.livinginternet.com/i/ii_kahn.htm
    15. 15. Image source: http://beyondrelational.com/blogs/viral/archive/2010/04/12/ssrs-generating-report-with-xml-datasource.aspx
    16. 16. Image Source: http://30.media.tumblr.com/tumblr_kx11ssrFCN1qb0ukuo1_400.jpg
    17. 17. It takes a network to run a network. Yaneer Bar-Yam, Dynamics of Complex Systems
    18. 18. Industrial Revolution Information Age Steam Electricity Computing Internetworking “Big Data” We are Here ?
    19. 19. http://www.slate.com/blogs/quora/2013/06/17/what_is_the_relationship_between_working_memory_and_intelligence.html
    20. 20. Image source: http://www.appliedi.net/blog/2010/02/10/track-website-visitors/
    21. 21. http://www.railroadforums.com/forum/showthread.php?10124-Montgomery-Tunnel-VA-(56K-Warning) http://www.siongboon.com/projects/2012-03-03_rfid/index.html http://images.fatcow.com/data-center-photos/AffordableHostingSm.jpg http://www.telegraph.co.uk/technology/apple/iphone/10298812/iPhone-launch-what-theexperts-are-saying.html http://sarahgoshman.com/wp-content/uploads/2011/12/lizard-brain.jpg
    22. 22. http://lifehacker.com/5941997/defrag-your-brain-with-a-spark-file
    23. 23. “The two spaces point to different ages in brain evolution, one in which dispositions sufficed to guide adequate behavior and another in which maps gave rise to images and to an upgrade of the quality of behavior. Today they are seamlessly integrated.” From Self Comes to Mind Constructing the Conscious Brain, Antonio Damasio
    24. 24. IQYourCo ≈
    25. 25. IQ ≈ f(Gf,Gc,…) Gf: fluid intelligence Gc: crystallized intelligence … Gq: Quantitative Reasoning Gsm/Glr: short term / long term memory Gs: Processing speed Gt: Decision speed
    26. 26. IQYourCo ≈ ? Gf: fluid intelligence Corporate DevOps / Lean Near real time analysis Collaborative information flows across hierarchy
    27. 27. IQYourCo ≈ ? Gc: crystallized intelligence: IBM Watson Wolfram Alpha (information + computation) PageRank Entity extraction (unstructured -> structured)
    28. 28. IQYourCo ≈ ? Gq: Quantitative Reasoning Monte Carlo, other large scale quantitative techniques Machine learning, classification
    29. 29. IQYourCo ≈ ? Gsm/Glr: short term / long term memory Comprehensive data storage / access architecture Both structured and unstructured data Internal / external API strategies
    30. 30. IQYourCo ≈ ? Gs: Processing speed Gt: Decision speed Human collaboration Embedded analytics Complex event processing Human / machine symbiotic workflows
    31. 31. IQ ≈ f(Gf,Gc,…) Gf: fluid intelligence (Mostly human?) Gc: crystallized intelligence (digital recall) … Gq: Quantitative Reasoning (machines) Gsm/Glr: short term / long term memory (machines) Gs: Processing speed (symbiotic) Gt: Decision speed (symbiotic)
    32. 32. IQcorp ≈ f(G?,G?,?, ?, ?) >
    33. 33. Thank You Jim Stogdill @jstogdill jstogdill@oreilly.com radar.oreilly.com

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