July 31, 2016

Terminology: Data scientists vs. data engineers

I learned some newish terms on my recent trip. They’re meant to solve the problem that “data scientists” used to be folks with remarkably broad skill sets, few of whom actually existed in ideal form. So instead now it is increasingly said that:

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July 19, 2016

Notes on vendor lock-in

Vendor lock-in is an important subject. Everybody knows that. But few of us realize just how complicated the subject is, nor how riddled it is with paradoxes. Truth be told, I wasn’t fully aware either. But when I set out to write this post, I found that it just kept growing longer.

1. The most basic form of lock-in is:

2. Enterprise vendor standardization is closely associated with lock-in. The core idea is that you have a mandate or strong bias toward having different apps run over the same platforms, because:

3. That last point is double-edged; you have more power over suppliers to whom you give more business, but they also have more power over you. The upshot is often an ELA (Enterprise License Agreement), which commonly works:

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July 19, 2016

Notes from a long trip, July 19, 2016

For starters:

A running list of recent posts is:

Subjects I’d like to add to that list include:

Read more

June 5, 2016

Challenges in anomaly management

As I observed yet again last week, much of analytics is concerned with anomaly detection, analysis and response. I don’t think anybody understands the full consequences of that fact,* but let’s start with some basics.

*me included

An anomaly, for our purposes, is a data point or more likely a data aggregate that is notably different from the trend or norm. If I may oversimplify, there are three kinds of anomalies:

Two major considerations are:

What I mean by the latter point is:

Anyhow, the Holy Grail* of anomaly management is a system that sends the right alerts to the right people, and never sends them wrong ones. And the quest seems about as hard as that for the Holy Grail, although this one uses more venture capital and fewer horses. Read more

May 30, 2016

Adversarial analytics and other topics

Five years ago, in a taxonomy of analytic business benefits, I wrote:

A large fraction of all analytic efforts ultimately serve one or more of three purposes:

  • Marketing
  • Problem and anomaly detection and diagnosis
  • Planning and optimization

That continues to be true today. Now let’s add a bit of spin.

1. A large fraction of analytics is adversarial. In particular: Read more

May 18, 2016

Surveillance data in ordinary law enforcement

One of the most important issues in privacy and surveillance is also one of the least-discussed — the use of new surveillance technologies in ordinary law enforcement. Reasons for this neglect surely include:

One major thread in the United States is: Read more

May 18, 2016

Governments vs. tech companies — it’s complicated

Numerous tussles fit the template:

As a general rule, what’s best for any kind of company is — pricing and so on aside — whatever is best or most pleasing for their customers or users. This would suggest that it is in tech companies’ best interest to favor privacy, but there are two important quasi-exceptions: Read more

May 18, 2016

Privacy and surveillance require our attention

This year, privacy and surveillance issues have been all over the news. The most important, in my opinion, deal with the tension among:

More precisely, I’d say that those are the most important in Western democracies. The biggest deal worldwide may be China’s movement towards an ever-more-Orwellian surveillance state.

The main examples on my mind — each covered in a companion post — are:

Legislators’ thinking about these issues, at least in the US, seems to be confused but relatively nonpartisan. Support for these assertions includes:

I do think we are in for a spate of law- and rule-making, especially in the US. Bounds on the possible outcomes likely include: Read more

May 18, 2016

I’m having issues with comment spam

My blogs are having a bad time with comment spam. While Akismet and other safeguards are intercepting almost all of the ~5000 attempted spam comments per day, the small fraction that get through are still a large absolute number to deal with.

There’s some danger I’ll need to restrict comments here to combat it. (At the moment they’ve been turned off almost entirely on Text Technologies, which may be awkward if I want to put a post up there rather than here.) If I do, I’ll say so in a separate post. I apologize in advance for any inconvenience.

February 15, 2016

Some checklists for making technical choices

Whenever somebody asks for my help on application technology strategy, I start by trying to ascertain three things. The absolute first is actually a prerequisite to almost any kind of useful conversation, which is to ascertain in general terms what the hell it is that we are talking about. :)

My second goal is to ascertain technology constraints. Three common types are:

That’s often a short and straightforward discussion, except in those awkward situations when all three of my bullet points above are applicable at once.

The third item is usually more interesting. I try to figure out what is to be accomplished. That’s usually not a simple matter, because the initial list of goals and requirements is almost never accurate. It’s actually more common that I have to tell somebody to be more ambitious than that I need to rein them in.

Commonly overlooked needs include:

And if you take one thing away from this post, then take this:

I guarantee it.

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