Category Archives: Analytics

Forbes post “5 Cool Ways Big Data Is Changing Lives”

A colleague sent me a link to a Forbes post “5 Cool Ways Big Data Is Changing Lives”, and I have to object to one of the entries in the post:  ” When Big Data Goes Bad”

The example referred to is from when Forbes writer Kashmir Hill reported on how Target figured out a teen girl was pregnant before her father did.

Here is my issue with Raj Sabhlok‘s inclusion of this as one of “5 Cool Ways Big Data Is Changing Lives”:

1)      It clearly is not a ‘cool way big data is changing lives’..  PERHAPS I could concede that it is changing lives, but, as implemented, I would not agree it is ‘cool’.  (I guess I might concede that the predictive power is cool to data analysis folks)

2)      And labeling the Target scenario as a case where Big Data [Went] Bad misleads the reader.  It has nothing to do with the data itself but has to do with the business, policies, and implementation details of how the results might be used.  And those types foibles have been around since the dawn of marketing, not as some new phenomenon that Big Data has caused.

Don’t blame the data, or even the techniques for analyzing the data, for things you bring upon yourself based on improper or poor usage / policies.

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“Actionable Analytics”

A colleague sent me a request for information about ‘actionable analytics’.  The request was from their government customer to find whitepapers, research, etc. on ‘actionable analytics’.

Hmm.

First, I asked “How did the question/request come about?”  Sometimes things go askew from the question to the request to the response. (and even further askew when trying to gather inputs from others).

Second, (and the reason I asked the first question) I think (my opinion and yet-to-be-refuted based on my cursory research) that ‘actionable analytics’ is a combination of marketing hype (e.g. Gartner) and poor-phrasing for an existing concept.

  • RE: hype.  Looking at Google trends, you see that this phrase is a recent phenomenon.  Here: http://www.google.com/trends/explore#q=actionable%20analytics&date=today%2012-m&cmpt=q is the last 12 months, and you notice there is a peak in Jan 2013.  Lo and behold, that coincided with Gartner’s publication (http://www.gartner.com/resId=2316120) on Jan 25th.  Gartner is clearly the ‘loudest voice’ in this discussion.  I am seeing if I can get ahold of that Gartner.
  • RE: Existing concepts.  Prior to Gartner’s published report, the phrase has been used to generally mean ‘analytics which can be used for taking action’.  But, as this blog post (http://www.clickz.com/clickz/column/2166558/actionable-analytics)  points out, the phrase is linguistically ‘flawed’ .  (Actionable meaning ‘able to bring a lawsuit’..)  Aside from that nit, the web analytics community (the author refers to) uses that to mean ‘something you can take action on’.. as in how to turn that information into $$.

For me, using that phrase (as ‘bad’ as it is) really refers more to a ‘best practice’ or mindset versus some concrete ‘thing’.  It use today (by the pundits, e.g. Gartner) really tries to help differentiate how the future analytics should be different than the ‘same old BI (analytics) from yesteryear’.

Gartner’s points are a bit more than that – not just something that enables the business to take action, but something which is approachable/digestible by ‘the masses’.  Their phrase ‘invisible’ analytics aims to point out that decision makers are rarely the back-room number crunchers building models – even in the model-heavy financial industries.  The key is to make the analytics/models easily accessible and understandable for the decision maker.

I applaud that idea.  Yet.. care needs to be taken.  It is easy to hide all of the complexity (and more importantly the assumptions) from the end users and we can end up with what I affectionately term ‘babies wielding chainsaws’.  A great example is the financial meltdown on Wall Street – the hidden risk was in the assumptions and details obscured in the models.  I don’t think the general populace has the ability to either understand or even know what to question about analytics/models.  Throw in nuances like ‘correlation versus causation’, and forget it – all will be lost.

Gartner cites that there needs to me increased agility around analytics, but I think that ends up being held back by the knowledge, understanding, and maturity of the decision makers of analytics and models.  In order to have the analytic cycle shorten and better decisions being made, I think the education of the decision makers is one of the most important aspects.

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