The increasing ability for companies to get transaction-level, detail-level data — clickstream data versus summary data — presents huge opportunity, says Boston College’s Sam Ransbotham.
The increasing ability for companies to get transaction-level, detail-level data–click stream data versus summary data–presents huge opportunity, says Boston College’s Sam Ransbotham. Big data gets all the press these days, but as important and perhaps even more important is detailed data.
Detailed detail gives companies “the opportunity to try to figure out the ways that, say, customers, differ,” he says. And that’s not just demographically, but in their behavior. “By observing detailed transactional level of their data, we can actually find much more interesting things than we can by lumping them into demographic groups.”
Ransbotham’s primary research interests are in security and risk, but that led him to analytics. “What really sparked my interest is how do you make sense of that much data, since detection system logs have data in huge numbers, the billions of records category. How do you spot trends and how do you figure out what’s going on in those trends?” His research also now encompasses what he calls “more positive areas” of customer service and customer reviews and how those things are being used in marketing.
In a conversation with David Kiron, executive editor of Innovation Hubs at MIT Sloan Management Review, Ransbotham explains why detailed data can tell companies not just why someone did something but why they didn’t do something else, how hospitals don’t seem to face heightened malpractice risks when they install electronic medical records and what companies should and should not be worried about when their customers fire off real-time comments on Twitter.
3 Comments On: Why Detailed Data Is As Important As Big Data
The speed of Analytics with detailed data seems a great opportunity. The competition however might trigger a rat race for client business details and what the customer feels is more a classified information or his own success formula. Such intrusion might as well be a concern which could delay the adaptation. Members in the Supply Chain however don’t seem to have an option to get offline from this trend.
Also the Managerial skills for such such integration seems to be a different flavour. The Human Capital Management needs to be completely different from the traditional objectives and deliverables.
There seems to be far more experimentation than clear cut deliverables in the model. It is like an organization with a small research wing and a large production facility. Suddenly the R&D wing gets larger and more profitable and the production unit gets reduced and S&M mode. We are seeing many organizations with the second happening already. and the first is almost getting mandated for sustenance and better future.
Great Article. Appreciate the insight and focus. Thanks a lot, Kiron and Sam, for the conversation.
When we look at analytics at Nucleus, we find that the detailed information is important to create a more predictive enterprise. Although social sentiment and other public big data sources are important, these are ultimately reactive data inputs based on pre-existing business conditions.
The most predictive and analytical businesses identify risk environments and scenarios that can lead to problems rather than simply accelerate what everyone else is already doing.
[...] Read Sam Ransbotham’s interview in MIT Sloan Management Review (April [...]