Small Cloudy Musings about Big Data

If we develop the topic of cloud and big data in a more general context, the first thing to understand is that this is not about any particular vendor. We’re talking open source here, the moment you remove lock-ins—see Flume, Hadoop, Hive, Impala, to name just the Apache stack as an example. It also is not about the pure amount of data that we can handle. It is not about Tera-, Peta- or Exabytes.

It likewise is not really about knowing where you’re shopping and what. Those are rather “mundane” applications.

It is really about generating decision relevant information from that data.

And it is then about having the possibilities to implement products based on these decisions at marginal costs. Consider 3D printing. Today, we can not only print toys. We can print houses. We can print Nylon stockings. We can even print the functional equivalent of kidneys. In the future, a very small number of experts will be required to convert the observed “templates” into mathematical models to serve to drive reproduction machines.

Since quite a while already, we can create about any gene code—only if the argument would arise that biological processes are too complicated. They are quite complicated. So are the “printers.” Three years ago, the Craig Venter Institute generated the first bacterium DNA, creating “the first species to have its parents be a computer” (see http://news.bbc.co.uk/2/hi/science/nature/8695992.stm).

Against this background, the current Cloud strategies that the big players are running are pale with regards to what actually is happening. Cloud is not about credit card transactions. It is not about flight data. It is not about where someone is or when he goes to the restroom (Netflix). Those are specific applications that allow some people today to collect some relatively low hanging fruits.

It is about aggregating all available information. It is about all you can know, historically as well as what is currently generated—or what do you think Google Glasses is really about? Today, we have the necessary resources to do that. And we do have the algorithms to make sense from unstructured data.

In terms of strategy theory, Cloud and Big Data hammer in the last nail to “Inside-Out’s” coffin. You reduce information cost to zero and at the same time create choice.

That’s going to have massive impacts in the future. Very likely, learning processes are going to change fundamentally. The way you learn and the speed with which you learn is going to dramatically change and does so already: About two hundred years ago, it was unlikely for the average person in the UK to come across as much information in his lifetime as you can read in one single edition, today, of the New York Times. What’s more, the production processes are going to be largely simplified. Software development processes suddenly can be applied to hardware if you can allow for mistakes—resulting in massively reduced release cycles and much higher innovation rate. You could think that the information age and the way it works is specific for, well, intangible goods. It no longer is.

Intellectually, it will depend much less on your IQ what you can know and understand and hence utilize. It will not depend on your genes when you want to do what or when you want to go to sleep. In a more general sense, the “IQ” is a very extensible concept (individually as well as socially). As an example, for quite a while, people thought that we’re just using like 10 % of our brains. That’s nonsense. We’re always using everything that’s made available to us. Whether we use it in a sensible way is a different matter altogether. And by that I don’t mean time-wasting activities like watching “Brits got Talent” (they surely have). I also refer to what our brain does in order to have available, in extreme situations, a massive reserve that allows us to “fight or flight.” Since these days, the number of lurking sabretooth tigers has decreased remarkably, that potential should not sit unused most of the time, and it needn’t be: We can understand those processes both from the point of view of psychology (making resources available using, e.g., Hypnosis, NLP, Meditation, and others) as well as chemically (which brings us to Coffee, Coca-Cola, Red Bull, and their successors). As one result, I am very convinced that we’ll still be there to see that sleep is going to be a very optional activity.

Now, what does that mean to strategy?

Like in the Coca-Cola examples, the bargaining power of the suppliers is massively reduced as they merely deliver very fundamental products that our “3D printers” can turn into anything we like.

A new entrant can be deterred only by either investment into knowing the “production formula” of the goods we want to “print”, e.g. secured by patents (a temporary factor only) but countered by “generics” (“open source”).

Substitutes are ubiquitous the moment you can add value in a different way. Just think about becoming a car rental company. If you can add value by providing for a different kind of service, the actual task (there: getting from a to b), can be substituted by different means (helicopters, boats rather than cars) or different levels of service (driver, luxury car, etc.).

Existing competitors over the same products or services are likely to operate all at the PPF, if we assume production processes are optimal and information freely available.

Buyers have a strong bargaining power (as we’re the suppliers of goods that anyone else with the same information can likewise produce).

With this little game of thoughts, let me get some “Tea, Earl Grey, hot” and go back more boring things.

Best regards,

Matthias

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