October 10, 2016

Notes on anomaly management

Then felt I like some watcher of the skies
When a new planet swims into his ken

— John Keats, “On First Looking Into Chapman’s Homer”

1. In June I wrote about why anomaly management is hard. Well, not only is it hard to do; it’s hard to talk about as well. One reason, I think, is that it’s hard to define what an anomaly is. And that’s a structural problem, not just a semantic one — if something is well enough understood to be easily described, then how much of an anomaly is it after all?

Artificial intelligence is famously hard to define for similar reasons.

“Anomaly management” and similar terms are not yet in the software marketing mainstream, and may never be. But naming aside, the actual subject matter is important.

2. Anomaly analysis is clearly at the heart of several sectors, including:

Each of those areas features one or both of the frameworks:

So if you want to identify, understand, avert and/or remediate bad stuff, data anomalies are the first place to look.

3. The “insights” promised by many analytics vendors — especially those who sell to marketing departments — are also often heralded by anomalies. Already in the 1970s, Walmart observed that red clothing sold particularly well in Omaha, while orange flew off the shelves in Syracuse. And so, in large college towns, they stocked their stores to the gills with clothing in the colors of the local football team. They also noticed that fancy dresses for little girls sold especially well in Hispanic communities … specifically for girls at the age of First Communion.

4. The examples in the previous point may be characterized as noteworthy correlations that surely are reflecting actual causality. (The beer/diapers story would be another example, if only it were true.) Formally, the same is probably true of most actionable anomalies. So “anomalies” are fairly similar to — or at least overlap heavily with — “statistically surprising observations”.

And I do mean “statistically”. As per my Keats quote above, we have a classical model of sudden-shock discovery — an astronomer finding a new planet, a radar operator seeing a blip on a screen, etc. But Keats’ poem is 200 years old this month. In this century, there’s a lot more number-crunching involved.

Please note: It is certainly not the case that anomalies are necessarily found via statistical techniques. But however they’re actually found, they would at least in theory score as positives via various statistical tests.

5. There are quite a few steps to the anomaly-surfacing process, including but not limited to:

Hence many different kinds of vendor can have roles to play.

6. One vendor that has influenced my thinking about data anomalies is Nestlogic, an early-stage start-up with which I’m heavily involved. Here “heavily involved” includes:

Nestlogic’s claims include:

I find these claims persuasive enough to help Nestlogic with its marketing and fund-raising, and to cite them in my post here. Still, please understand that they are Nestlogic’s and David’s assertions, not my own.

Comments

2 Responses to “Notes on anomaly management”

  1. Analyzing the right data | DBMS 2 : DataBase Management System Services – Cloud Data Architect on April 14th, 2017 1:32 am

    […] In an example of the previous point, anomaly management technology can, in theory, help shortcut any type of analytics, in that it tries to identify what […]

  2. Monitoring | DBMS 2 : DataBase Management System Services on June 26th, 2017 12:36 am

    […] The idea is to check data for surprises as soon as it streams in, using your favorite techniques in anomaly management. Perhaps an anomaly will herald a problem in the data pipeline. Perhaps it will highlight genuinely […]

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