Category Archives: Charts

Big Data Conversion Chart & Functional Relevance

A Petabyte is a lot of data, not a generic bite from your pets























My favorite quotes from this post are:

“Data has no inherent value. To be useful, data must flow to agents who will ultimately process, analyze, and synthesize it to produce information that drives decisions. The recent conversation in DoD has focused on what is referred to as the “big data problem,” that is, since we don’t know what’s important in the data being collected, everything must be saved. But this is much harder than it sounds.”

“Data is not important. It’s the information that can be gleaned from the data that matters. The old data paradigm emphasizes precision: save only what you consider to be relevant at the time the data is collected. This approach works only so long as you are dealing with a more or less static context where “relevance” can be readily established.”

Relevance is not an attribute. It’s a relationship, or a complex mapping that has sources, targets, and attribute values on the link. Consider this abstract function:

Relevance = F(source, content, context; me, my role, my situation, my company; the environment and set of competitors and space of potential actions).

Because of these considerations, you can see why relevance is elusive and non-comparable across markets, uses, and situations. THerefore aggregate sums and statistics on relevance are even more problematic.


Astronomy Needs “Forecast Transparency” for Observatories

There is an interesting visualization of forecast transparency and using atmospheric data here. Science is usually my inspiration for both calculating metrics and visualizing them for business. Sometimes though, the business data needs to be more summarized, and less accurate, more simplified, and have some of the statistics removed….

There is an interesting contrast when comparing this to the concept of Business forecast transparency. In business parlance, Forecast is future revenue and future earnings; not future weather. Transparency means that external people (usually analysts) can see how the metric (in this case Forecast) was derived. The formula and source data (or leading indicators). In the case of Astronomy, the transparency refers to the visibility through the atmosphere.