Data analysis is itself innocuous unless it drives some form of action. Internet companies have mastered this trade through computational advertising. The causal business effect of interventions such as displaying an ad in a webpage is quantified precisely by how much an advertiser has bid for having the ad displayed or clicked on.
The user’s feedback (in general through clicking or not) is then automatically sent back to a machine learning algorithm that learns how profitable that ad is (per customer). The loop is then closed. The system that determines the intervention allocation policy monitors the business outcomes of every intervention and from that updates the policy automatically so as to maximise the business value of future allocations.
What to say of existing bricks and mortar businesses in this regard? Josh Wills, director of data science at Cloudera, a leading big data solutions provider for enterprise, claims no one is doing this automated closed-loop revenue generation mechanism apart from the giant online properties.
Maybe he is right, maybe not. But even if there are others doing this, there is certainly a long way to go. Granted, there are existing data-driven policies for marketing, credit scoring, pricing and other activities in big service providers like banks, telecoms and insurance companies.
But even in the US such large corporations suffer with the operational issues of legacy systems, as well as cultural and technological silos that simply make it too hard to integrate data science and intervention policy in a closed loop across a variety of business areas.
So, what’s the solution? I don’t think there is any silver bullet. The best bet I would place is simply to follow what has worked for online businesses: work as fast as possible on acquiring the right culture, people and operations model. In the US and Europe, some large retailers and service providers have been moving fast.
Walmart has had for years a large team dedicated to data science to leverage the historical purchase data to better tailor offers to its customers. Retailer Target made headlines two years ago when New York Times reporter Charles Duhigg brought to the public’s attention the now famous incident of one of Target’s analytics models predicting a teenager’s pregnancy before her father did.
One of the world’s largest mobile carriers, Telefonica from Spain, has several years ago established a scientific research group in machine learning.
Although Australian companies are in general significantly behind, in the past two years a few large corporations have started to make moves on the people side by succeeding in hiring data scientists. A notable domestic event was Woolworths recently acquiring a 50% stake in data analytics company Quantium.
Whether such and other large retailers and service providers will go a step beyond by realising a cultural and operational shift is also required remains to be seen.
Tiberio Caetano is a principal researcher in NICTA’s machine learning group and an adjunct associate professor at the ANU.
This article was originally published at The Conversation. Read the original article.
