LonData III: the MoshiMonsters paradox

Are you familiar with MoshiMonsters?  It is an online pet type game/junior social network developed by MindCandy.  If not, then you probably don’t have kids aged between 6 and 12…

At LonData III we were lucky enough to have a presentation from Toby Moore, CTO of MoshiMonsters, who took us through the world of data that the game generates, and how MindCandy got to where they are.

Toby took us through their aim moving from no data, through big data, right data, predictive data, and eventually strategic data.

At the beginning of their story they had lots of data, but no ETL, no reporting, and no analysis.  They realised they had to move forwards, and put in place a technology stack of:

  • MS SQL Server as an ETL platform
  • Hadoop for data storage
  • MS SQL for analysis/reporting

This still didn’t resolve their problems, and so they are moving to QlikView to give users direct access to their data.

So this is a Big Data play, right? Lots of data? Hadoop?  It must be!

Is this Big Data, or just big data?

There are lots of things that are great about this story – and let me be clear that none of my comments in any way take away from the amazing success of MoshiMonsters…

I like:

  • The fact that data is so important to them
  • The willingness to give end users direct access to data

But I think it fails to be Big Data because

  • They don’t try to experiment using the data
  • They don’t do predictive analysis (although they use six-sigma statistical approaches to identify issues)
  • There is very limited analysis

Data kills Creativity? Really?

In fact the most worrying issue was a CP Snow like divide: on the one side Creativity.  On the other Data.

This came up several times in the presentation – they would never burden their creative staff with data. They don’t think that segmenting their customers, or analysing their behaviour is the way to go. They don’t test out alternative strategies on the website.

Partly this is because they are extremely sensitive to the nature of their customers (young children) who aren’t the same as the people paying the bills (adults). They say they try to avoid pressuring their customers out of the freemium and into the paying segments*.

I’ve got to say, I really don’t believe this divide to be true. Yes, an anally retentive approach to analysis might kill creativity, but anyone that anal probably doesn’t understand the limits of their analysis. Analysis leaves many, many grey areas.  And on the other hand creativity cannot work in a vacuum.

I came away somewhat disturbed by their approach, whilst still being in admiration of their success and drive. I don’t believe that Big Data approaches can be separated from creativity!

The conclusion:

  1. Is Hadoop necessary for Big Data? Possibly, but it isn’t sufficient.
  2. Is volume necessary for Big Data? Not on an absolute scale, although it helps.
  3. Is attitude necessary for Big Data? Yes, absolutely!
  4. Is it creative? Hell Yes!




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