The Science of Communicating & Marketing Your Business

To recap: Part 1 dealt with defined noisy data, part 2 extended the concept into creating standards for new web technologies, part 3 considered where metrics, standards and noisy data might take us if we're not careful and part 4 dealt with what happens to noisy data under the current analytics paradigm. We left off wondering what the real question of analytics is... 

"What question do you want answered?" is a question which scalability hates and is why commercial analytics vendors have consultants and such on staff. There are going to be holes in any analysis regardless of how complete a package is (including ours and we're working on that) and it's the consultant's job to fill those holes. These holes are most often not easily filled by commercial vendors because the hole is a hole due to the question's answer hiding in the noisy data.

Looking in holes and listening to noise is often relegated to exploratory analytics and exploratory analytics are usually performed once. After that first exploratory analysis both vendors and clients want results oriented analysis. This once and never again policy is good for commercial vendors because they want clients to focus on what vendors know how to analyze. I'm not disparaging analytics vendors by writing that. To quote Barbara Johnson in The Critical Difference, "When we read a text once...we can see in it only what we have already learned to see before." I'll also point out that NextStage often asks clients for their web analytics reports if that information is germane to what the client has hired NextStage to do.

Noisy data is going to challenge a lot of what's out there because noisy data has historically been discarded as junk. It is neither recognized as useful nor thought of as necessary information (like the DNA example I used in Not So Social Networks.

Keith Jarrett wrote
"The treasure has always been there
It is not hidden
But is only where certain people would look
At all
Thus it remains a secret to the rest..." (Treasure Island)
This need to look where others aren't looking is how noisy data got its start in so many sciences and why multi-disciplinary approaches to problem solving are gaining favor in so many fields; training in a given discipline teaches one to look through the lens of that discipline and that means one can only see what that discipline has trained you to see. It is a Maslow's Hammerish trap, when all you have is a hammer, everything looks like a nail.

When all I have is a hammer, everything looks like my thumb but that's for another arc.
More to follow...

Links for this arc:

• Articles on noisy data affecting analysis:
  • The Isomap Algorithm and Topological Stability
  • Reconstructing Past Climate from Noisy Data
  • Losing Variability in the Noise
• The Critical Difference: Essays in the Contemporary Rhetoric of Reading
• Design and Purpose links:
  • Semphonics Functionalism Paper
  • IMediaConnectin Columns
  • Chapter 7, "Experience versus Expectation", Reading Virtual Minds
• Posts on new web technologies:
  • RIA
  • Rich Media
  • Social Tools
• Posts mentioning WAA:
  • The Long Tail, Part 1
  • The Long Tail, Part 2
  • The Reluctant Blogger
  • Nothing New Under the Sun (Ajax)...
  • Nothing New Under the Sun (Privacy)...
• The Noisy Data Arc:
  • Standards and Noisy Data, Part 1
  • Standards and Noisy Data, Part 2
  • Not So Social Networks
  • Standards and Noisy Data, Part 3
  • Standards and Noisy Data, Part 4
• Web Analytics Association Links:
  • WAA
  • Web Analytics Wednesday
• Web Analytics Vendors mentioned in this arc:
  • Coremetrics
  • Unica