When I go to the bookstore or a university library, I pick out a stack of books in my areas of interest, and try to scan through them enough to justify taking them off the shelf. I was supposed to finish a particular book, but that didn’t happen. Instead, I spent some time looking through the following at a high level:
- EMC^2 (the author), Data Science and Big Data Analytics,
- Lea Verou, CSS Secrets, and
- Adam Morgan et.al, A Beautiful Constraint.
In Data Science …, I came across a very clear diagram of how power (or significance) gets narrow and taller as sample size increases. Consider each sample to be a unit of time. That leads us to the idea that power arrives over time. These statistics don’t depend on the data. They are about the framing of the underlying studies. The data might change the means and the standard deviations. If the means are narrowly separated, you’re going to need a larger sample size to get the distributions to be narrow enough to be clearly separated, which is the point of the power statistic. Their arrival and departures will change the logic of the various hypotheses. You could under this paradigm see the disruptions of Richard Foster’s Innovation, a book Christensen referenced in his Inventor’s Dilemma before Christensen took an inside-out view of disruption, a view of the scientist/engineer-free innovation, as the arrival of the steeper slopes of the price-performance curve intersections and the departures of same.
As an aside, This week in a twitter linked blog post by a never to be named product manager, I came across the weakest definition of our “all the rage” disruptive innovation, as being akin to a classroom disruption, so far has our vocabulary fallen. No. No. But, it is a buzzword after all. Louder with the buzz please. “I can’t hear you.”
There was also a graph of Centroids (Clusters) that turn out to look like a factor analysis in the sense of steep and long to ever flatter and shorter spans.
There was also a discussion of trees. A branching node in the middle of the tree was called an internal node. I typically divide a tree into its branch nodes and it’s leaf leave nodes. I didn’t read it closely, so the distinction is lost on me.
This book is not an easy elementary statistics book. I will buy it and take a slow read through it.
In CSS Secrets, there were a lot of things new to me. I did some CSS back in the day, so sprinting through this was interesting. Yes, you can do that now. What? Align text on any path, use embedded SVG. The real shocker was tied to Bezier curves and animation. Various curves in a cubic-Bezier curve showed how to “Ease In;” “Ease In and Out,” which looks like the S-curve of price-performance fame; “Ease Out”; and the familiar “Linear.” The names of the curves could be framed as talking about business results. There were more curves, but there are only a limited number of cubic-Bezier curves. Higher-order curves were not discussed. A cubic-Bezier curve has two end points, and two control points. In the animation sense, the curve feeds values to the animated object. The cubic-Bezier curve is not capable of driving, by itself, full-fledged character animation, but it’s a beginning. We, the computer industry, are easing out of Moore’s law as we speak.
In A Beautiful Constraint, we are looking at a biz book, in the self-help sense. It describes the mindset, method, and motivation for overcoming constraints on one’s performance. We start out as victims. We have to overcome path dependence. We do that with propelling questions and what the author calls Can-If questions. With a Can-If question we are asking about the “How,” sort of the developer’s how, rather than the requirements elicitor’s what. Breaking the path dependency has us asking ourselves or our team about believing it’s possible, knowing where to start, and knowing how much do we want to do it.
An interesting statement was that Moore’s law is actually a path dependence. Intel’s people didn’t let the law break. They always found a way to preserve the “law.” But, Moore’s law was really a sigmoid curve. It flattens at the top. The investment to break the constraint requires much more investment and delivers almost no return, so Intel’s people easing out of it. They like Microsoft will have to find another discontinuous innovation to ride. The cloud is not such a thing. In fact, the cloud is old and there won’t be a near monopolist in that category. It’s not the next discontinuous innovation. It is really the disappearance, the phobic and non-adopter phases–the phases at the convergence at the end of the category. The device space is that the laggard, yes laggard, but it is still 10x bigger than pre-merger late mainstreet. The normal of Moore’s technology adoption lifecycle is really a sum of a bunch of normals, which leave us unable to see the reality of the category that the discontinuous innovation gave rise to. The end is near.
Anyway, that was tonight’s reading/browsing/carousing. Enjoy.