Big Data: Does size matter?

The Big Data Pandemic by Ken Roberts, CEO of Forethought is an interesting and thought provoking read. The article sets the stage by predicting that big data will explode at an alarming rate (projected growth in 2013 was $18.1 Billion USD) and completely negate the need for Small Data such as hypothesis, surveys and sampling. Anyone working in Small Data, on reading that, would have pricked up their ears and exclaimed “hang on a minute”, which is precisely the path that Robert’s then takes by offering the other side to the argument and concluding that the jury is still out as to whether Big Data will outperform Small Data.

Although there are many advocates for big data and its contribution to the marketing environment, issues with stability and integrity of data as well as basing decisions purely on clusters of behaviors without understand what is driving things has left opinions somewhat divided. Couple that with the inability of business in general to actually implement Big Data programs, (many can’t even get to grips with Small Data so how can they be expected to embrace Big Data) we are probably still a long way from this making a significant impact, or change to, business as usual approaches.

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As many of you know, I’m a huge advocate of Big Data. I’m totally fascinated by what information you can gain by using it and I think it can provide valuable insights in to identifying “what” is happening.

At the Bureau of Meteorology we use Big Data and Computer Models to provide weather forecasts. The Big Data generated by our observation networks coupled with the predictive analysis data from computer modeling can tell us with a high degree of certainty exactly what is happening, when it is happening and where it is happening. What it doesn’t necessarily do though is tell us “why” it is happening. For that we default back to our forecasters and observers on the ground to analyze what the models are outputting to help explain the “why”. In simple terms what I am talking about here is quantitative and qualitative data.

In the retail environment it is equally as complex. Big Data may well be able to provide valuable information on what people are buying and where they are buying it from, future trends etc. but is it really able to accurately predict how people feel about what they purchase, the decision making process they went through, whether they were happy with their purchase and would do it again? In an article by Chris Anderson in 2008 he argues that the why people do something or how they feel about it isn’t really that important as long as they do it, he is certainly more convinced with that than I am.

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And that was my key take away from the Robert’s article. Big Data can be a great resource of qualitative data, but would you really want to leave it all in the hands of computer models and artificial intelligence and then base your most important business decisions on just that?

To obtain a really good understanding, I think it’s still important to qualify those results by understanding the “why” and for that, you need some human intervention. As Robert’s notes “perhaps it is not about big data versus small data but rather, big data and small data combining to produce synergistic insight.

My money is on Roberts. What do you think?

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