“Big Data Analytics is the new Oil – We need to extract it, refine it, distribute it and monetize it” – David Buckingham
“Disintermediation” is the defining feature of the digital age. Innovative technologies are making the middlemen obsolete. I still remember my father opening his encyclopedia when I asked him a question that stumped him.
I don’t think my children would ask me anything. In fact, they will quote all the information from the likes of Wikipedia, Answers.com, ehow, and so on. Wikipedia was a pioneer in disintermediating the encyclopedia.What can Big Data Analytics disintermediate? Imagine a lab technician looking at a biopsy in his/ her Petri dish and identifying whether the biopsy is malignant or not.
Now, imagine an algorithm that will have an access to data of all the biopsies done in the past 15 years. Based on the existing data in the new age of machine learning, the algorithm will be able to predict whether the cells cultures are malignant or not.
Though we are not currently in a state wherein we can disintermediate the lab technician, but we are getting there slowly. On the other hand, it will be difficult to completely eliminate the human factor in a sensitive industry such as healthcare and research.
Fact-based marketing activities support marketing and public relation decisions based on data. Understanding the psyche of the customers can help companies get the best out of their marketing campaigns.
As a technology consultant, I have seen data speaking to me and telling me some patterns that a marketing manager would have missed. There is a potential threat to the traditional marketing agencies being disintermediated and being replaced by data scientists who can give managers the numbers to support their strategic decisions.
Are we prepared to disintermediate yet? Mike Potts Chief Data Officer of the Havas Media Group,believes that “Clients are underprepared to take advantage of their data, as its most siloed and there’s a huge challenge on putting that data together and to share it” The main challenge that the proponents of big data analytics are facing is that if data is not handled properly, things can go really bad.
There is a famous theory “Garbage in – garbage out” – So bigger the garbage in, bigger would be the repercussions. For example, if Zappos were driven by margins, they’d abandon their generous returns policy. Just because data is objective, it doesn’t mean that it guides you to the right decision.
Take for example the failure of a leading food retailer to capitalize on its digital marketing strategy. Just because it’s precise, it doesn’t follow that it’s valuable. Till the time a human brain can be replicated, it will be difficult to disintermediate human intervention altogether while taking strategic decisions.
It is essential for enterprises to ride the wave of big data. However, we need to be careful of how to use that data in a more effective manner to be relevant in the future.
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