Large Pharmaceutical Companies, Big Data Analytics, Artificial Intelligence and Core Competencies: A Brave New World

To date, and perhaps surprisingly, the idea of using data strategically remains somewhat novel in some organizations. However, the reality of "big data" and "big data analytics" (which is "the process of examining big data to uncover hidden patterns, unknown correlations, and other useful information that can be used to make better decisions") is becoming increasingly popular in business. Indeed, in the current competitive landscape, most businesses must use big data analytics (BDA) across all customer channels (mobile, Web, e-mail, and physical stores) throughout their supply chain to help them become more innovative.

This is the situation for large pharmaceutical companies (the firms often called "big pharma") in that many have been working to develop a core competence in BDA. (We define and discuss core competencies in this chapter.) There are several reasons they are doing this. In addition to the vast increases in the amounts of data that must be studied and interpreted for competitive purposes, "health care reform and the changing landscape of health care delivery" systems throughout the world are influencing these firms to think about developing BDA as a core competence.

AI can help analyze data on clinical trials, health records, genetic profiles, and preclinical studies. China has a goal to become the world leader in AI.

Many benefits can accrue to big pharma firms that develop BDA as a core competence. For example, having BDA as a core competence can help a firm quickly identify trial candidates and accelerate their recruitment, develop improved inclusion and exclusion criteria to use in clinical trials, and uncover unintended uses and indications for products. In terms of customer functionality, superior products can be provided at a faster pace as a foundation for helping patients live better and healthier lives.

In developing their BDA capabilities, many of the big pharma companies are investing in artificial intelligence (AI). AI provides the capability to analyze many different sets of information. For example, AI can help analyze data on clinical trials, health records, genetic profiles, and preclinical studies. AI can analyze and integrate these data to identify patterns in the data and suggest hypotheses about relationships. A new drug generally requires a decade of research and $2.6 billion of investment. And only about 5 percent of the drugs that enter experimental research make it to the market and are successful. Eventually, it is expected that the use of AI could reduce the early research development time from 4-6 years to 1 year, not only greatly reducing the time of development but also the costs.

As we discuss in this chapter, capabilities are the foundation for developing core competencies. There are several capabilities big pharma companies need for BDA to be a core competence. Supportive architecture, the proper mix of data scientists, and "technology that integrates and manages new types and sources of data flexibility and scalability while maintaining the highest standards of data governance, data quality, and data security" are examplesof capabilities that big pharma need if they wish to develop BDA as a core competence. Of course, using artificial intelligence provides strong support for the application of BDA.

Having a strong BDA competence could be critical for pharmaceutical firms in the future. Most Chinese pharmaceutical firms are medium-sized and sell generic drugs and therapeutic medicines, investing in R&D at only about 25% of the amount invested by big pharma in developed countries. However, China has a plan to develop large, competitive pharmaceutical firms by 2025. In 2017, for example, China's second largest class of investments was biopharma. Interestingly, the largest Chinese investment that year was in information systems, including AI. China has a goal to become the world leader in AI.

In recent years, big pharma has been earning mediocre returns of about 3 percent ROI, down from 10 percent a decade earlier. Thus, big pharma executives feel pressure especially with the initial costs of developing BDA and AI. Hopefully, they soon will be able to reduce their costs and experience higher rates of success in the development of new drugs. Until then, however, analysts are predicting record numbers of mergers and acquisitions in the pharmaceutical industry, with big pharma acquiring successful medium-sized pharmaceuticals and biotechnology firms.

1.How can data analytics help companies be competitive?Explain.

2.Using the Business Insights: Essentials database, identify 1 of the big pharma companies and look at its SWOT report.Name the company and list its strengths and weaknesses.

3.Who are the major pharmaceutical companies competing to develop a coronavirus vaccine.How can data analytics and AI help in that pursuit?