The Death of Dashboards, and the Promise of Personalized Analytics

Posted by ZS Editors on Mon, Jan 07, 2019

Multiple factors are colliding to encourage the need for personalized analytics. The ever-expanding explosion of data is forcing companies to consider more nimble, automated solutions that simplify the user experience. The need for competitive advantage in a crowded marketplace is inspiring companies to arm reps with sales tools that give them an edge. Reps are increasingly expecting the kind of user experience that they get from consumer applications. The list goes on.

Personalization conjures a future where reps are no longer bombarded with information and expected to draw their own insights. In this future, AI-driven insights come to them as alerts on their mobile devices, personalized to their needs and customers, and delivered at the best possible time. 

But this is pharma. Surely the spreadsheets and dashboards will persist, right?


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Making the Case for a New Analytics Consumption Model

Posted by ZS Editors on Thu, Nov 29, 2018

Industries like retail and technology are transitioning to an AI-driven, personalized approach to surfacing insights to end users, and they’re reaping the benefits. Life sciences companies have the same opportunity to capitalize on the runaway growth in data and rethink the way that analytics are consumed.  

ZS recently partnered with IDC to study how commercial life sciences teams are currently consuming data, and to determine their data and analytics pain points. The study revealed that sales and marketing professionals in life sciences want


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From BI to AI: Getting Started With Advanced Analytics

Posted by Niroop Singh on Thu, Nov 01, 2018

Pharmaceutical companies are making big investments in analytics ecosystems, but not without some disappointment in terms of ROI. Because investments are typically limited to people or technology in isolation, companies can’t deliver the kind of value that makes such programs worthwhile. At the same time, cost pressures mean that analytics and data management groups have to deliver more with less. Executives who sponsor such programs also need to be very clear on how to define the success of such initiatives. Creating hundreds of new reports does not equal success. To give your analysts the advanced tools that they need to truly succeed, you need the right combination of people, data processes and technology to get the most out of your advanced analytics investment.

What should my team look like? What kind of processes do I need to support an advanced analytics capability? What kind of technology? These are great questions to ask at the outset, and here are some answers:


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Five Critical Steps for Successfully Leveraging AI

Posted by Arun Shastri on Fri, Apr 27, 2018

Dharmendra Sahay co-wrote this blog post with Arun Shastri.

This post is the first in a two-part series.

In a few days, we’ll be presenting our thoughts on how to create impact with artificial intelligence at the 2018 PMSA Annual Conference. We’ll talk about how AI is being used in life sciences and how AI could be used, and we’ll bust some myths. We’ll also share detailed advice on how to start or expand an AI capability at your organization, which includes these five critical steps:


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