What’s the value in Big Data? The Push Shopping example (Big Data, Part 3)

By Clive Gold – Marketing CTO, EMC Corporation, Australia and New Zealand

Talking about a Telco and its data might not resonate with you so let me expand my example, before we discuss how to solve the problem.  Go back to Part 1, here, of this series, and consider how a retailer could turn this near shopaphobic into a future shopaholic? As discussed online is changing the shopping experience, more than just as a substitute for physical.

Building on the online shopping experience, I described a substitute for physical shopping, now think beyond that.  We know about online auctions, but I also use Catchoftheday and Scoopon (yes I have a frustrated wife that thinks we get boxes of useless gadgets, and yes… I’m in the process of re-doing my open water divers licence – it’s been years).  These are interesting as it’s an example of push selling, not new but much faster and group purchasing.  Now think beyond that, the offer I get from these systems is not tailored to me, so I ignore almost all of them! However if they were in line with what I was interested in today, well that’s another story.

Firstly a health warning, don’t panic! As you live you leave a digital trail. Ignore for now the privacy and security issues. What if all this data was in one place, (Isilon-read Part 2), here, then a system could analyse this and tell me stuff I didn’t already know.  Here is an interesting example: I purchase an iPad and I comment on Facebook that I hate the soft-keyboard. Imagine if you sold iPad cases and you had these two bits of information and you could see I purchase a lot of electronic gadgets; you send me an offer of an iPad case with a Bluetooth keyboard built in… and you have me.

The problem is we are talking about two big problems, lots of data and looking for connections I don’t know I’m looking for. Traditional databases just can’t hack this, not in size and not in structure, so enter Greenplum. Designed to do this work it not only scales, (performance) but does not require you to pre-define the structures, which then limits you to finding things that you know you are looking for.

The example I like to use is a case study where a major Telco in the USA, was analysing their mobile call data records. They found that when a person cancelled their service, within a short period of time, up to six of the top 10 people they used to call would also cancel (not surprising, they are the people they talk to).

So, finding the un-known, un-known’s is an interesting topic for another day. For now, being able to store and analyse BIG DATA has so much potential.

Perhaps now having this digital trail can enhance our lives!


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