big data miracle drug

We've pondered the implications of 'big data' in these pages on several occasions and come to at least one simple conclusion, namely that more (ie big) data does not necessarily mean 'better' and that the more data one has will often actually make it harder to find the required information to distill into insight.

With that in mind, find of the week is this article by Scott Brinkner entitled 'The Big Data Bubble'.

Scott says 'Now, I love data as much as the next techy-geeky-marketing-wonk-with-a-blog. But what strikes me about all this explosive data chatter — in no small part, driven by the peaking hype cycle of big data as a miracle drug — is how little recognition is being given to the operational implications of actually using data.'

And from an agency person perspective I almost feel that Scott has observed some of the big agency CEO 'predictions' for 2013 that have been appearing.

Whereas two years ago the key buzzwords* would have included 'social', then 'possibly 'mobile' the new phrase to drop in assessment of challenges or opportunities is clearly 'big data'.

Quite why this is an opportunity though is sadly absent from this commentary.

Scott has neatly encapsulated the chunking of this percieved 'opportunity' thus...

1. Analyze data — preferably big data.
2. ???
3. Profit.
Huzzah!

The pesky bit in the middle is the bit that, for the most part, few have yet to approach a grasping distance.

Fortunately Brinkler outlines a potential 1-2-3 approach neatly.

1. Big Data. Collect and organize data to extract information and insight. This is the part that big data has to offer. But some of the most valuable output from such data analytics will be mere hypotheses — interesting correlations of factors and behaviors

2. Big Testing. Take those hypotheses and be able to quickly and effectively test them to prove cause-and-effect: that those factors can indeed be leveraged to influence customer behavior.

3. Big Experience. Apply your targeted data and proven tests towards delivering better customer experiences, to many different customer segments.

Read the full article here.

*note: the hapless CEO's are clearly subject to intuitizzle heuristics at this stage and as we all know, all heuristics are equal, but 'availablity' is more equal than others.

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big data bollocks

A couple of things I've learned from speaking at ad industry events over the past few years are these.

The first thing is to try and make your 15-20 mins as entertaining as you can. A story to back up your points is more important than graphs and charts, and the stories will be the thing that audience members will take away, more often than not.

The second thing is to be mindful that most events will have a hashtag connected via which delegates will tweet the bits and pieces that resonate.

A controversial or otherwise interesting 'blanket' statement about this or that will often get tweeted so it's always a good idea to structure a few of your points to be tweetable.

Also I've found that it is unlikely you will have the whole crowd nodding in agreement with you and quite often there will be significant disagreement. Don't worry about that, trying to appeal to every point of view inevitably ends up in appealing to no-one.

To that last point, I've had a few bits of feedback from delegates at last weeks AIMIA Future of Digital bash.

In my final section I proposed that 2013 may be the year in which we see the bubble burst in the whole big data situation.

There was equal parts agreement and dismay among those present.

The thought was thus; the value of big data is vastly overrated.

This is not to say that there is no value but rather that the value is derived from the processing and analysis of said data and it's conversion into important information.

For those familiar with the DIKW model, that information requires further distillation in order to come out the other end as Wisdom.

In adland parlance we would call wisdom 'insight'.

The data in itself may indeed be the new 'oil' however it is crude oil at best.

The other thing is that more data does not necessarily mean better.

In fact the more data one has will often make it harder to find the patterns that become the required information to distill into insight.

Better means better.

Having dealt with many businesses over the years who cannot even make sense of their own opted-in customer database, more data is not going to help them in any shape or form.

The other point is that any data set needs human beings to interpret it.

And knowing how as humans we are subject to no small amount of foibles and biases is testament to the difficulty of this task.

To illustrate I quoted this oft repeated psychology experiment (to add, we have conducted one of these ourselves and achieved remarkably similar results to those experiments of a similar nature from academia).

We asked two groups of financial services employees to assess their likelyhood to approve a credit card application from a recent graduate.

The applicant had creditworthy history, and was gainfully employed with an above average salary etc.

However, with the first group we gave them one extra data point to consider.

The applicant had an outstanding student loan of circa $5000.

With the second group we gave two data points.

The applicant had an outstanding student loan of between $5000 and $12500.

This second group were given an extra option in there assessment process. Either approve or decline the application. Or await further information about the extent of the outstanding debt.

Not surprisingly the majority of group two asked for further information.

We then revealed that the debt was actually very close to the $5000 number.

In group one around 70% declined the application for credit.

In group two only around 30% declined.

This is despite both groups having nearly identical data in the end.

By firstly anchoring group two on the $12500 number, the $5000 debt didn't feel so bad.

The point being that humans have clear difficulty with making consistent assessments when faced with only two pieces of data.

Good data has long been the lifeblood of marketing (ask any direct marketer) but at this point in time perhaps we don't necessarily need more data but better data, and there's a criminal shortage in the advertising industry today of the actual human skills needed to interpret, distill and convert into insight.

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none of the above (miniskirts part 2)

Back in the day there was an old anarchist/situationist slogan that used to appear around election time that went something like..
'Don't vote - It only encourages them'.
Or the other one was 'Whoever You vote for, the government gets in'.

I'm not sure this story correlates exactly but I was intrigued by this nugget from Eric Horrow's blog 'Peer-reviewed by my neurons'.

As the dust settles on the US election, and collective America asks itself...

'Is everybody happy?
'No?'
'Good, it's a deal'

What about those 'voters' who actually wanted neither Obama or Romney in the Oval office?

What was the choice available to them?

Horrow argued that if you are one of those dissenters then thing to do was vote for Obama.

'...the goal of these voters [who want neither] should evolve into ensuring that neither Obama nor Romney is elected president in 2016. Here’s where the decision become clear. If Obama wins this year there’s almost no chance that Obama or Romney will win the 2016 election. But if Romney wins there’s better than a 50% chance that Obama or Romney will win the 2016 election.

For people who claim they don’t want either Obama or Romney to be president, a 2012 Romney victory is a disaster because it ensures that in 2016 one party’s nominee will be somebody they already disapprove of...[so, in 2016] an unknown scenario is better than helping pave the road for the favored nominee to be somebody you already know you disapprove of.'

So although Baz appeared to romp home, could it be that a big chunk of the electorate overcame their natural tendency for hyperbolic discounting and figured that the best way to get rid of two candidates that they didn't support was to vote in the encumbent in the knowledge that in four years time both candidates will be gone.

Suddenly that 3,000,000 margin in the popular vote doesn't look so impressive.

I'm slightly joking, and I'll take Baz over the other guy any day of the week but, y'know...

As our old friend Ebbe Skovdahl, clearly a big data skeptic before we even invented the term, noted back in '09...

'Statistics are just like mini-skirts, they give you good ideas but hide the most important thing.'

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