The investment in data and analytics by life sciences organizations is not new and the advantages are continually touted. A recent article from McKinsey estimates predictive analytics and data visualization can optimize life sciences’ commercial spend and lift returns by as much as 10 to 25 percent. And yet, there is ample room for improvement in driving an integrated analytics culture and strategy.
Life sciences organizations are facing a number of challenges and in order to stay competitive, they need to increase their speed from analytics insights to action. A drug’s patent life is 20 years from the time of filing, but the effective time of market exclusivity has become shorter and shorter. There is an increased need to produce ongoing evidence of a drug’s effectiveness and differentiation to prove its value to patients.
Pharmaceutical companies working in the same therapeutic areas are seeking to treat the same patient populations. They all have the same access to the same data and knowledge from scientific and medical literature, and they all hire top talent. What then becomes the competitive differentiator? The companies that can take that data, uncover unique insights faster than their competition and act on them more quickly will hold the upper hand.
The value proposition for organizations is contingent upon creating value by converting data to insights through the use of advanced analytics, and then capturing that value by converting insights to action. Operationalizing insights to unleash their true value requires that business leaders understand and trust the analytics insights. The organizations that can build this understanding and trust the fastest will be the ones who realize the competitive advantage.
Understanding and trusting data-driven insights requires analytics literacy throughout the organization, i.e. a data and analytics culture.
A recent survey revealed that over 97 percent of executives said their organizations were investing in Big Data and AI initiatives in an effort to “become nimble, data-driven businesses” in order to improve the quality of decisions. And yet, over 60 percent reported that they had yet to forge a data-driven culture, and fewer than half reported that they compete on analytics. Almost unanimously, they reported that the critical challenge is culture, not technology. 
The survey highlights the need to invest in building an analytics culture and I will address how to develop one in a subsequent blog. But there are several things organizations can do now to successfully scale commercial analytics to drive a competitive advantage.
Conduct analytics collaboratively. Collaboration between various teams is crucial to success and it starts with getting everyone to speak the same language. Marketers and sales teams don’t speak the language of analytics, so suppliers of analytics, i.e. the analysts, must translate from an analytics language to a business language when communicating to the consumers of analytics, i.e. the sales and marketing teams, so they understand and trust the data-driven insights. By bringing the various teams together to define the business problem, develop a common goal, and explore the solution space, you can create a common language and build understanding and trust.
Raise analytics literacy. Once you move toward a more collaborative approach to analytics, you enable everyone to explore the same data from different perspectives. It’s increasingly important for commercial team members to develop the capabilities to conduct ongoing analysis of their data to sustain a competitive edge. To do this, put analytics tools into the hands of both technical and non-technical staff to drive better collaboration and commercial success. Look for an analytics platform that offers speed and flexibility and ease of use to enable sharing of insights. This collaborative exploration develops the analytics literacy of the consumers of analytics, enabling them to ask better questions, and promoting the discovery of novel insights.
Generate insights relevant to the business. To gain collective buy-in, focus on generating insights and results in areas that are meaningful and material to the business. Analytics can’t be a theoretical pursuit. Making analytics relevant and noticeable is the most effective way to get everyone in the organization excited and engaged.
Take both deductive and inductive approaches. Be receptive to exploring new explanations and theories through inductive reasoning, as well as testing existing theories and beliefs. Challenging the status quo through inductive reasoning opens the door to transformational discoveries.
Align assumptions to accelerate the process. When discussing difficult strategic problems, the assumptions upon which you base your analysis are critically important. Algorithms may be objective, but the assumptions built into them or the data used to train them are not. Developing the assumptions transparently and collaboratively –e.g., will current trends continue?; will the political environment remain stable? – will help uncover potential weak spots in the analysis and determine which assumptions are agreed to and supported by the sales and marketing teams, and can dramatically accelerate the speed to insights and action.
Optimize the present AND focus on the future. Data science teams in digital companies like LinkedIn and Air BnB don’t focus exclusively on current issues. They also focus on how analytics can inform future needs. To be effective, you need to develop a bifocal approach. Raising analytics literacy through collaborative analytics will enable the organization to handle day-to-day problems themselves through established reports, tools and databases, giving your advanced analytics team the capacity to analyze the weak signals to anticipate future opportunities.
Bridge the gap between RWD and the commercial teams. Commercial analytics groups are focused on generating insights into sales and marketing level questions, typically with prescription level data. Using RWD in the form of anonymized patient level data can generate unique insights impactful to the bottom line. Work with your RWE team to gain access to this robust data set.
To successfully build an integrated analytics culture and strategy takes work. It means building a collaborative culture that breaks down siloes so that there’s agreement on the key business problems to address. It means improving your company’s analytic literacy by all speaking the same language. It also means scaling your analytics capabilities by enabling all members of your commercial teams to analyze data. The result is an increase in the speed to value and the competitive edge we are all looking for.
 Get more from your pharma commercial spend using advanced analytics, by Rishi Bhandari, Brian Fox, Laura Moran and Ziv Yaar, McKinsey and Company, August 31, 2017.
 Big Data and AI Executive Survey 2019, NewVantage Partners, 2019