CIOTechOutlook >> Magazine >> December - 2015 issue

Big on Data, Small on Intelligence

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Headquartered in Maharashtra, Beehive Communications is an integrated communications solutions provider. A member of the Publicis Group, Beehive Communications provide solutions like Creative Advertising, Integrated Strategy Planning, Public Relations / BTL, Media, Research and Digital Solutions.

Much is discussed about the 3 revered Vs of big data – Variety, Velocity and Variety. But what else must marketers need to know to make better decisions?
Marketers now have access to enormous amounts of data but that does not mean it automatically leads to intelligence. The reasons for this are multitude; sometimes it’s just a dump of data, or lack of right tools and expertise to interpret the data to arrive at conclusions. Arguably, arriving at one big idea based on analysing multiple sets of data is no easy task. Here’s a few suggestion to make data work to your marketing advantage:

Don’t let data override common sense:
The sky above you sure looks grey on a rainy day, but that doesn’t mean that’s the color of sky elsewhere. Same goes for the data accrued from one segment of customer (online audience) or one region (metros). Drawing conclusions that leads to major marketing decisions based on isolated data sources is sure way for disaster.

Too much zooming in gives you bad picture:
I haven’t seen a single marketer who’s not excited about showing personalised ads, sending emails with precise first and last name, better accuracy in product recommendations and so on. In theory, the more data they have about a customer, more accurate targeting can be achieved. In practical, the missing element in action is context and social nature of humans. Algorithms can seldom answer questions about the intent behind a customer’s behaviour, let alone the motive behind a purchase.

The size of your rear view mirror decides the crystal ball’s accuracy:
Machine Learning, the heart of predictive analysis algorithms requires what is called as training data (rear view mirror) to predict future (crystal gazing). An average algorithm with deeper taring data is far better than an advanced algorithm with shallow training data.

True, but not really: Most big data provides marketers with too many variables, but little data per variable; this gives rise to questionable relationships between parameters and thus leads to false information. In sum, the needle just got stacked up in a bigger, complex haystack.

Avoid GIGO: “Garbage In, Garbage Out. Or rather more felicitously: The tree of nonsense is watered with error and from its branches swing the pumpkins of disaster.” - Nick Harkaway.
In lack of order, metrics and hierarchies within data sets, big data only adds to ambiguity and can give out nothing more than distorted outputs.

Business objectives, biases, and blaming technology: The definitive way to make data work for any organization is to clearly define business objectives. Sounds like cliché but there’s a hidden gem in this statement. Are you using data to advance your business- hear customer sentiments to improve service, understand need-gap to introduce more products, improve operational efficiency, etc., or is it a case where CXO has an opinion and just needs some right data to metamorphose gut feel to concrete evidence?

It is not always about data & technology and what can be done with it. A business decision with bias as a preamble can’t be fixed by any data, there are hundreds of evidences hidden in plain sight to improve brand experiences, but I guess you can’t wake up someone who’s pretending to sleep.

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