February 25, 2019/Matthew Van Wingerden/Actionable Analytics, Artificial Intelligence

Across industries, innovators are finding ways to use data and analytics to be disruptive. This combination fuels the need for organizations to have advanced data capabilities in order to stay competitive. Many life sciences companies aim to meet this challenge by tapping the resources and expertise of their in-house data analytics teams. However, these teams can often be under-resourced while meeting a variety of other roadblocks.

Artificial intelligence (AI) and machine learning are quickly becoming omnipresent, but converting data into business value remains a challenge for life sciences companies. With the pressure to implement intelligent technologies into analytics tools, many companies expect ambitious results without a clear understanding of what type of impact this technology can have.

According to McKinsey, “most [organizations] have experimented with a handful of use cases, but lack a comprehensive view. Even fewer have considered how analytics can create new sources of revenue. Lacking an enterprise-wide view of opportunity, business leaders struggle to make a considered business case for analytics. They may also struggle to communicate why analytics matter—and that is essential to get the organization committed to change.”

A Complicated Role

The in-house analytics team sits in the midst of this rapidly expanding universe of data, intelligent technology, and competitive disruption. While this team may seem like a natural owner of the growing capabilities, the speed of growth makes role clarity ambiguous. Downsizing and outsourcing have disrupted the traditional way that analytics teams support the business.

In some organizations, the rapid growth of data has been enabled by the creation of “centers of excellence” or digital hub. This can create complexity and confusion for in-house analytics teams regarding internal ownership of innovation and analytical insights. In other cases, the rapid rise of advanced analytics and data science has shifted the balance of expertise to vendors who may have an advanced understanding of the potential of new technologies.

This shifting environment has created a complicated landscape for in-house analytics teams to navigate.

The Most Valuable Asset

In-house analytics teams have both the burden and the benefit of owning the responsibility of data, reporting, and investment assessment across their organizations. Due to this insight, these teams can be the most valuable asset when developing business strategy, especially when looking to implement AI and machine learning.

As investments have increased and capabilities have become more difficult to manage, the need for a cross-cutting analytics thought leader is critical. In-house analytics teams’ focus on analytics and data science enables them to understand what’s possible for their specific organization to set realistic expectations for all stakeholders involved. These teams can play two critical roles in business strategy:

  • Advising business on the potential strategic use of advanced analytics and artificial intelligence: With their insight and proximity to different parts of the business, in-house analytics teams are well positioned to advise sales, marketing, and market access teams on which investments in data and technology will be most impactful.
  • Managing the development, deployment, and refinement of advanced analytics and AI: As experts in this space, analytics teams can help manage bandwidth, keep timelines realistic, and play a key role in the decision of bringing on internal or external partners. These teams understand how analytics can disrupt current business models and how to scale and capitalize on new opportunities.

Clarity of Purpose

Before this can all become a reality, all stakeholders involved must first determine how AI translates into business impact for their company and provide clarity to the in-house analytics teams on what their role entails. Today, there are many unknowns when it comes to truly understanding how AI and machine learning can help manipulate data to bring value to an organization. The challenge for organizations is enabling actionable execution and refining their strategy.

There is no one-stop solution for life sciences organizations who intend to implement intelligent data analytics models. What’s clear is that the in-house analytics team is one of the most knowledgeable assets a company can use to provide valuable insight into their business strategy.

For more insight on the state of machine learning in life sciences commercial processes, get the perspectives of Veeva Systems SVP of Commercial Strategy Paul Shawah and Aktana CEO David Ehrlich.