2270_Making_the_Case_for_a_New_Analytics_Consumption_Model_Blog_Image (1)-1Industries 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 more insights, that better analytics could lead to much better performance (respondents estimated a potential 35% improvement in business performance), and that users are burdened with more data sources than they can make sense of.

To learn more about making the case for a new analytics consumption model in life sciences organizations, we caught up with three of ZS’s analytics experts: Mahmood Majeed, a managing partner, principal and leader of the firm’s business technology area; Jérôme Chabrillat, an associate principal who specializes in analytics; and Elizabeth Benker, an associate director and leader on ZS’s user experience team.

Q: The great majority of respondents from the study believed that there was “untapped potential for technology to aid in better insights and analysis.” And the study suggests that the answer is embedding analytics into commercial operations. What do you think is the biggest driver behind this need for a new way to consume analytics?

Mahmood Majeed: You can see in this study some conflicted feelings about wanting more data vs. wanting more insights, and on the back end, you do want more data. But unless you know what to do with it, more data will lead to more headaches. Once you have a certain amount of data, the human mind can’t process it anymore.

I think this desire for more data indicates that commercial teams need better data and better insights to make informed and timely decisions. Companies that use data to drive business decisions are clearly gaining advantage over the ones that just use data to inform the business.

Elizabeth Benker: Too much data can be almost debilitating, so that’s why you need a machine to help you make sense of it. It’s about needing insights, and as information continues to grow, the more you need some intelligent solution to help you get those insights. We need to leverage both what humans are good at doing and what machines are good at doing. Machines detect the signal versus the noise, but they can’t interpret that signal without humans.

MM: It’s a paradigm shift. It’s no longer about amassing data so that you can do more analysis. It’s about uncovering novel insights to build a story so you can create impact right away.

Q: Let’s talk about the significance of having data in one place. Most of the respondents in the study report that they “usually” have their data in one place. Only 21% “always” have their data in one place. For those clients who usually or sometimes or never have their data in one place, what are they risking?

EB: We hear a lot of stories about how difficult it is to get data from multiple systems, especially if that data is from different teams or in different levels of granularity. Then it becomes really challenging. It sounds simple, but just having the data itself in one place enables you to bypass all of those challenges and skip straight to interpreting the data.


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MM: Historically, the value of having everything in one place is so that I can get the insights faster, cheaper, better. But given the variety and velocity of data, it’s impossible to maintain all data in one place all the time. “Embed it into my day-to-day decision-making and build my knowledge base, and apply some correlations to develop new types of insights.”

Jérôme Chabrillat: The meaning of “in one place” is changing. For 20 years, it’s been about literally getting things into one system on the back end, but it’s now a more user-centric meaning that focuses on reducing complexity for the end user.  

MM: In the future, AI models won’t even require data all in one place. They’ll just pull from different sources and aggregate data for the end user on the fly.

EB: Exactly. No one cares if the data is all over the place on the back end as long as the user gets it in one place on the front end.

Q: On average, respondents expect a 35% improvement in business outcomes from better data. But at the same time, 95% of respondents are satisfied with their existing tools. Why would they be satisfied with so much room for improvement?

JC: The tools are designed to help with specific tasks, and that’s working, but I think what this is telling us is that there’s an opportunity to do more, to go beyond just making calls or looking at performance. It’s not that the tools are broken. They’re working, but there’s an opportunity to get a lot more value out of them.

EB: From a tools perspective, when you ask people, “Are you satisfied with email?” Most people are like, “Well, yeah,” because there isn’t a predefined alternative that you’re suggesting instead. There’s the thirst for more information to fill the gaps that they know they have, versus the tools that they have, that they’re comfortable using. They’re not really reflecting on the experience of their tools because they don’t have anything else to compare them to.

MM: Pharma companies are getting into situations where clinical value propositions are no longer sufficient to stand out against the competition. They need analytics to help them differentiate in a crowded marketplace. They are looking for new and different types of data that aren’t provided, typically.

Q: The study demonstrates how different roles need different insights. Given the prevailing data consumption model, how is pharma struggling to provide the right data and insights for every role?

JC: Right now, large organizations are organized by functional group, with each owning their own data and capabilities, and servicing a specific internal customer. Getting from data to insight within these siloes is fairly easy, but when you need to cut across siloes, things get difficult. To solve for this, you need cross-functional governance that is empowered to break the silos. You need to consolidate across disparate tool sets, put a cross-functional team together with a common goal. The new pharma landscape requires commercial organizations to look across all of these functional siloes, but we aren’t set up for that yet.

MM: Overall, there’s an over-reliance on the organizational model to inform and manage the data organization and supporting needs. It’s more nuanced and individual than that. If you use the data to make a certain kind of decision, that doesn’t really have bearing on how another person might use that data in a completely different way.

EB: Unless you deliberately do the work to understand what different people in different roles need from the data, and how they’re using the data to make decisions, you’re not going to give them the right data or put it in the right format.

There’s an example from a pharma company that I’ve been working with recently. It’s a team that provides data to their field force, and they assumed that they were providing the field with everything they needed. At the headquarters level, they look at the national results and then they drill down into the data to filter outliers at the territory level. When we got the chance to talk to the field, we learned that the reps look at the problems in exactly the opposite way. The reps want to look at data from their territory first, understand what’s happening in their world and connect it with their on-the-ground understanding, and then they want to see how that compares with what’s happening at the national level. It’s a completely different way to use the same kind of data.

Q: What are the business implications of not adopting some kind of next-generation analytics consumption model?

MM: Think of it this way: Analytics is maturing from informing the business to driving the business. Now we are embedding analytics in people’s day-to-day work lives. For salespeople, they’re getting insights on a real-time basis so that they can be more effective with their customers. For marketing people, they’re able to see what’s happening and know how to enable and orchestrate salespeople.

When it comes to the consequences of not taking this approach to analytics, well, other companies and competitors are doing it, so life sciences organizations will be left behind if they don’t. It’s no longer just about having a better product. You need to have everything in place in the entire ecosystem. That’s what creates advantage.


Topics: Analytics, artificial intelligence, AI, data and analytics, analytics disruption, commercial analytics, artificial intelligence & pharma, cutting-edge analytics, advanced analytics, personalized analytics, analytics maturity, pharma commercial model