Big data analytics is a work in progress in pharma

There’s good news and bad news in a study commissioned by ZS Associates, the sales and marketing consulting firm that works closely with leading pharma companies. When it comes to deploying the latest technologies in data analytics, including cloud-based Big Data platforms, the pharma industry is ranked as one of the most advanced industry sectors, across a spectrum including financial services, travel and consumer goods (that’s the good news). However, although the industry is making substantial investments, the payoff in identifiable returns or business process improvements is only just beginning to appear.

The study, Broken links: Why analytics investments have yet to pay off, was conducted for ZS by the Economist Intelligence Unit, and included nearly 450 senior executives across a variety of industries. The focus was on sales and marketing analytics (Big Data is also at play in clinical research and other endeavors). The study also included some measures of financial performance to distinguish leading from lagging firms. According to Dan Wetherill, associate principal in data analytics at ZS, data analytics includes the range of techniques—some of which have been extant for years in the pharma industry—to understand customer engagement with a company and its products. A new wave of analytical tools is arriving, however, based on extremely large data sets, and new sources like social media.

Overall, the top-performing industry sector in applying analytics is the retail industry (where, although not explicitly stated, e-commerce companies like Amazon reign). No. 2 is financial services and No. 3 is the pharma industry—well ahead of the industry that it is usually compared (unfavorably) to: consumer package goods.

Across all industries, 94% have invested in cloud-based big data capabilities, and a comparable number in their analytics function, but only 8% have fully integrated the two. “Most leading pharma companies have hired data scientists and others to work with big data, but the disconnect between those analytics capabilities and the business objectives to be met is fairly wide,” says Wetherill. He goes on to emphasize that the leading companies (as defined by their financial performance) put an emphasis on active collaboration between the business owners and the data scientists.

The study looks closely at what Wetherill calls the analytics “value chain,” starting with defining a problem to be addressed, building the infrastructure to analyze it, executing and then using results to improve business performance. The biggest problem areas (see figure) are in the initial definition and approach steps, and in taking action at the end of the chain; intermediate steps (identified as data integration/preparation, scoping and supplier selection, analysis execution and interpretation) are less difficult tasks.

“Overall, this is a journey, and this report is a snapshot of where companies are on a maturity curve,” he says. Another important finding is that companies are still choosing what parts of the analytics function to outsource, and which to retain; Wetherill says that while many of the technology platforms are of necessity obtained from outsourced providers, companies should treat their analytic capabilities as a strategic asset, and put an emphasis on “insourcing”—through better internal collaboration—the analytics for business value.

The full report is available here.

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