Big Data vs Insights. 5 observations from Oracle Open World 15 San Francisco
So, I am here in San Francisco, mostly to shop and enjoy this amazing city while my husband attends OOW15.
Let me set the scene by saying that I am an insights professional, having spent the past 20+ years running focus groups in Australia and elsewhere, and of course over the past 5 years doing more and more qual, quant and mobile research online. I am both a firm believer in traditional market research methods and an early adopter of newer and better ways of obtaining insight.
The nature of these studies varies, but the ultimate aim is to guide business decision making, to inform strategy, to grow brands.
Intrigued by the predominance of sky high posters around the city claiming that Oracle have Big Data solutions, and seeing the opening key note where the CEO told the 60,000 IT delegates that they can now deliver actionable insights I have started to talk to various propeller heads to find out what exactly they are doing with big data. Here is what I've learned so far:
- Most of these guys have never spoken to someone like me. They have only a superficial understanding of how market research is done currently.
- They are just as keen to understand how we arrive at business solutions and actionable insights as we should be keen to talk to them. We don't quite speak the same language, but there has been a high level of enthusiasm around understanding how to merge what we know.
- The solutions on offer (if the sales pitch is to be believed) are mind blowing. There tools available now, or in the very near future which finally allow companies to truly make sense of the vast amounts of data they capture through sales, digital campaigns, loyalty cards, social media. It brings it into one 'data reservoir' and gives data scientists some very cool tools to play with it.
- It is expensive, and I really hope it's not coming from the research budget. I saw an example which predicted to the cent the precise number of ads which need to run on different channels, at different times, and in which order, and on which channels, to different demographic or user segments to reach a precise sales target. The argument of avoiding ad over or underspend is very compelling.
- But as always it is only as good as the person who is driving the machine. It is hard to see how these predictions can be accurate without the involvement of researchers with a solid understanding of consumer behaviour.
We in the research industry should be working more closely with IT teams instead of being defensive and dismissive of the potential of big data. IT + Insights professionals: better together?
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