ZS and Top 10 Pharma Optimize Analytics Sourcing for Life Sciences

Michael Townsend

Situation Overview


In 2013, a top 10 pharma company enlisted ZS to design and run an analytics sourcing program for its commercial operations. The company had already worked successfully with ZS for several years on projects such as field sales force deployment and marketing analytics and had built out its analytics capabilities in multiple applications in preceding years.

At this point, the company had decided to explore consolidating analytics functions while keeping internal head count stable, including both generalists and analytics specialists. Based on a history of successfully delivering on analytics-related projects, ZS was chosen to lead the external consulting and sourcing of this critical initiative. Recently, IDC spoke with the company's Global Lead for Data and Analytics Strategy and Operations regarding the company's experience with ZS and analytics sourcing.

Leading up to 2014, the model of labor arbitrage had worked well for the pharma by increasing productivity while managing demand variation in IT, including many analytics roles. However, the company and ZS felt that digital innovation and role sharing could increase productivity and capability further compared with "throwing more bodies" at projects. Prior experience in analytics projects, mainly using local resources on an ad hoc basis, pointed to a lack of incentives for continuous improvement, as well as difficulty scaling up these projects. It was hoped that by consolidating the analytics functions, it would be possible to compartmentalize analysts where they could move between projects.

The company's goal was to move to a situation where approximately 80% of the analytics work was "standardized," with the remaining 20% bespoke. In this plan, innovations such as analytics dashboards would allow management of initiatives based on outcomes, which could be tracked in real time. Incentives were designed to produce desired outcomes, with dashboards presenting measured results such as "top 10 KPI" or "number of monthly insights." These incentives undergo continuous improvement to align with business objectives.

In the process of consolidating and sourcing these analytics functions, ZS and its customer are applying client-supplied analytics software, generating insights for important topics, such as:

  • How various factors drive medication adherence
  • Optimization of messages that result in prescription writing
  • Improved patient identification
  • Medication uptake in hospital environments
  • Customer (HCP) 360 data generation and analysis

The pharma's Global Lead for Data and Analytics Strategy and Operations found that the greatest short-term challenges in designing these analytics projects revolved around finding patterns in the data and in preserving process knowledge within the team as members moved between projects. Anticipating more rapid standardization of analytics projects, she was surprised that due to complexity, several of these initiatives retained some prior-state customization longer than expected. As these projects are standardized, these projects are being moved to a managed-services model (like dashboarding) with the assistance of the ZS team.

She described the analytics sourcing model as a prototype for future initiatives and a success for her company, with goals for future projects including rapid standardization of business processes using analytics sourcing with established analytics software platforms.

Advice For The Technology Buyer

Prioritize analytics projects for sourcing based on future demand and potential for conversion to managed-services models.

  • Enlist analytics sourcing vendors based on customer references, life science vertical expertise, and analytics knowledge.
  • Conserve in-house analytics resources for new initiatives and core capabilities while expanding sourced projects.
  • Define processes and expected outcomes prior to sourcing using in-house operators and data scientists.

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