The style curve

At a recent conference, a fellow attendee asked about best practices for the live tiles on Windows 8.

Live tiles are different from the standard icons in that they can show information and change over time. Windows 8 comes with a number of live tiles: the clock, news, Bing search, and entertainment are a few.

For the question of best practices, my take is that we're too early in what I call the "style curve" of Windows live tiles. The style curve is similar to the "hype curve", in which new technologies are born, receive some hype, then become disparaged as they fail to cure all of our ills, and finally become accepted as useful. See more about the hype curve on wikipedia.

The style curve applies to new technologies, and is similar to the hype curve in that a technology is created, given lots of attention, disparaged, and then accepted. Here are the phases:

Creation The technology is created and made available.

Experimentation People test out the new technology and test its limits.

Overuse People adopt the new technology and use it, but with poor judgement. They use it for too many things, or too many situations, or with too many combinations.

Avoidance People dislike the overuse (or the poor taste) and complain. Some actively avoid the new technology.

Best practices A few folks use the technology with good taste. They demonstrate that the technology can be used without offending people's sensibilities. The techniques they use are dubbed "best practices".

Acceptance The techniques of restrained use (the best practices) are adopted by most folks.

Previous technologies have followed this curve. Examples include typefaces and fonts in desktop publishing (and later word processing) and animated images in web pages.

Some readers will remember the early days of the web and some of the garish designs that were used. The memories of spinning icons and blinking text may still be painful. This was the "overuse" phase of the style cycle for web pages. Several shops banned outright the use of the blink tag -- the "avoidance" phase. Now people understand good design principles for web pages. (Which do not include the blink tag, thankfully.)

Desktop publishing, powered by Windows and laser printers, allowed people to use a multitude of typefaces and fonts in their documents. And use them they did. Today we have use a limited set of typefaces and fonts in any one document, and shops have style guides.

Coming back to live tiles, I think we are at the "experimentation" phase of the style cycle. We don't know the limits on live tiles and we don't know the best practices. We have to go through the "overuse" and "avoidance" phases before we can get to "best practices". In other words, the best practices are a matter of knowing what not to do. But we have to try everything to see what works and what doesn't work.

Be prepared for some ugly, garish, and annoying live tiles. But know that style will arrive in the future.

A Risk of Big Data: Armchair Statisticians

In the mid-1980s, laser printers became affordable, word processor software became more capable, and many people found that they were able to publish their own documents. They proceeded to do so. Some showed restraint in the use of fonts; others created documents that were garish.

In the mid-1990s, web pages became affordable, web page design software became more capable, and many people found that they were able to create their own web sites. They proceeded to do so. Some showed restraint in the use of fonts, colors, and the blink tag; others created web sites that were hideous.

In the mid-2010s, storage became cheap, data became collectable, analysis tools became capable, and I suspect many people will find that they are able to collect and analyze large quantities of data. I further predict that many will do so. Some will show restraint in their analyses; others will collect some (almost) random data and create results that are less than correct.

The biggest risk of Big Data may be the amateur. Professional statisticians understand the data, understand the methods used to analyze the data, and understand the limits of those analyses. Armchair statisticians know enough to analysis the data but not enough to criticize the analysis. This is a problem because it is easy to mis-interpret the results.

Typical errors are:

• Omitting relevant data (or including irrelevant data) due to incorrect "select" operations.
• Identifying correlation as causation. (In an economic downturn, the unemployment rate increases as does the payments for unemployment insurance. But the UI payments do not cause the UI rate; both are driven by the economy.)
• Identifying the reverse of a causal relationship (Umbrellas do not cause rain.)
• Improper summary operations (Such as calculating an average of a quantized value like processor speed. You most likely want either the median or the mode.)

It is easy to make these errors, which is why the professionals take such pains to evaluate their work. Note that none of these are obvious in the results.

When the cost of performing these analyses was high, only the professionals could play. The cost of such analyses is dropping, which means that amateurs can play. And their results will look (at first glance) just as pretty as the professionals.

In desktop publishing and web page design, it was easy to separate the professionals and the amateurs. The visual aspects of the finished product were obvious.

With big data, it is hard to separate the two. The visual aspects of the final product do not show the workmanship of the analysis. (They show the workmanship of the presentation tool.)

Be prepared for the coming flood of presentations. And be prepared to ask some hard questions about the data and the analyses. It is the only way you will be able to tell the wheat from the chaff.