Should You Outsource Analytics?

  The debate over whether to outsource or offshore analytics remains widespread among executives today. Successful outsourcing or offshoring models provide an increasing number of pharma companies high-value, cost-effective analytics. But there are many challenges to overcome, including coordinating work across partners in dispersed locations, creating work accountability and protecting intellectual property. 

Ram Moorthy

  In this article, we share ideas and insights based on our combined 20 years' experience providing these services to some of the world’s largest pharmaceutical and biotechnology companies. We’ll use a case study that describes high points, though we are the first to admit the journey is never as smooth as explained here. Still, we hope it helps companies that seek better ways to deliver analytics.   Case study: new cost-effective analytics model for a mid-size pharmaceutical company For more than 15 years, a mid-size, international pharma company has engaged our firm to assist with business analytics. Typical reasons for outsourcing analytics included:   • A need for objective outside analysis • A lack of internal capacity, infrastructure/tools or skills to do the work • A need to meet seasonal/peak demand for specific analyses, and  • A desire to tap into the right innovation and expertise.    As a result of market-driven cost pressures and pending patent expirations for some key products, the newly appointed leader of commercial analytics at the pharmaceutical company faced a difficult assignment. The challenge was to implement - within nine months - a new model that delivered analytics at significantly lower cost without impacting the business. This meant supporting inline products and providing critical analytics for several impending product launches.

Dharmendra Sahay

  The vision was to create a lean internal team that would provide strategic insights to the sales force and brand teams, while outsourcing most of the detailed analytics to a cost-effective partner. Currently, the internal analytics team spent much time struggling to organize and analyze data and coordinate across outsourcing vendors. The team provided high levels of service to its internal business customers, who had come to expect quick responses for ad hoc requests. The challenge was to drive change within the internal analytics team and “make it stick” with internal business customers.   Bringing about change required the right execution:  

• Identify and fix inefficiencies and bottlenecks in how the internal analytics team functioned currently. Some early wins included eliminating duplicate data processing, holding IT partners more accountable for data quality and decreasing the number of vendors to reduce coordination overhead (and gain discounts in some cases).
   

• Get advice from peers in non-competing firms who had implemented similar initiatives. These peers provided insights about the challenges they faced, how to identify and execute solutions and how to choose the right partner(s).
   

• Work closely with internal business customers.This involved discussing and identifying needs that were business critical, deciding which services to rationalize and jointly planning how to deliver quarterly and yearly analytics. 
   

• Identify which functions to keep in-house and which to outsource. In this case, the internal analytics team wanted to keep strategic and value-add roles in-house. Tasks that involved data manipulation, modeling and analytics were considered good candidates to outsource. 
   

• Create alternative working models. In this case, the company worked with a consulting partner who used its experience in this area working with other companies (combined with input from selected internal team leads) to create three alternatives for delivering each key analytics function. These alternatives included different mixes of internal, onsite, onshore and offshore personnel. This step involved two key success factors: First, A consulting partner who had “been there done that” with other companies and knew the company and its way of working (that way, the partner could effectively customize options and make the appropriate recommendations). Second, the company needed input from select internal team leads to secure buy-in on who at the company would take ownership at the ground level and carry out the proposed working models. 
   

• Share the working model options and their benefits and costs with internal teams. This involved finding out the internal teams’ preference and comfort level with each model option. Teams for inline brands were comfortable with longer turnaround times and more planned analytics, while teams for launch brands wanted higher responsiveness at critical points around launch. It was important to ensure teams were willing to provide the budget to support the preferred working models.
   

• Conduct early pilots to test selected working models and identify areas of concern.  Some people in the company feared quality would suffer with offshoring analytics. But when offshore leads met with internal teams and shared their analytics expertise and knowledge of the company, this significantly mitigated concerns and allowed a larger migration to outsourced work. It was also important to conduct an early pilot program to look for organizational attrition and where the company could outsource. For example, rather than try to backfill an existing vacancy in the analytics team, the company outsourced as a way to test the outsourcing work model. This proof of concept with the pilot program, combined with meeting company needs and goals, allowed internal teams to be comfortable.

Through this process, the company put in place an integrated partnership model that provided customers appropriate service levels while achieving cost goals. The internal team retained responsibility to interface with business customers, generate strategic insights and ensure that analytics supported business needs. The bulk of data management, modeling and analytics work execution was outsourced to partners with strong service level agreements governing quality and turnaround times. Management publicly communicated its support for the new working models and its expectation for internal teams to support it as well.    Since stakeholders were aware of the trade-offs and bought in early, this made it easier for them to accept the new ways of working. It wasn’t always smooth sailing, but clearly defined working models and transparency of communication made the journey easier all around.   

About the Authors

Ram Moorthy

is a principal with

ZS Associates

in Los Angeles. He has more than 15 years of experience addressing strategic sales and marketing issues in the pharmaceutical, biotechnology and medical supply industries. He is a co-author of T

he Power of Sales

Analytics

.  

Dharmendra Sahay

is a managing principal with ZS in New York City. Over the last 15 years, he has worked extensively with pharmaceutical companies in building commercial analytics capabilities with a global delivery model. He is a co-author of The Power of Sales Analytics.                         ###