Walgreens is looking to make its pharmacy operations as customer-focused as possible, with data and analytics playing critical roles. The pharmacy-store chain sees a business imperative to personalize interactions with customers and anticipate their needs: "Fast beats slow," said Gowri Selka, head of data analytics and corporate technology at parent company Walgreens Boots Alliance Inc.
To help put itself on the fast side of that divide, Walgreens is using a Hadoop-based big data architecture to do predictive data analytics as part of patient assessments at its in-store health clinics. The analytics process is aimed at improving patient care and identifying potential health risks before they become issues, according to Selka, who spoke at DataWorks Summit 2017 in San Jose, Calif.
Walgreens isn't alone. Predictive analytics is becoming a mainstream application as organizations increasingly look to forecast customer behavior, market trends, effective medical treatments and more.
There's good reason for that. Jen Underwood, founder of research firm Impact Analytix, wrote in a May 2016 blog post that predictive analytics "is likely to be a far more strategic investment" for companies than basic business intelligence and reporting is, with bigger payoffs possible if it's done right.
And the predictions that result "don't have to be macroscopic to be consequential," Forrester Research analyst Mike Gualtieri noted in a March 2017 post. What a particular customer is likely to buy next, which piece of manufacturing equipment might break down -- the business value gained from pinpointed predictive data analytics efforts can add up to significant amounts, he said.
But there are a lot of challenges to contend with in creating and managing a predictive analytics program. This handbook looks at both the benefits such initiatives can provide and the issues involved in making them work.