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Creating Defensible Data Analytics Moats for a Competitive Advantage


Karim Damji discusses how Artificial Intelligence (AI)-informed Systems of Intelligence and Virtual Assistants offer powerful solutions to the common struggles pharma companies face today.

Karim Damji

In ancient times, a castle was the home base of defense for the kingdom it ruled. The approach to the castle was not easily accessible. It was defended by one or more deep moats that deterred enemies and reinforced a stronghold capable of withstanding any number of competitive sieges.

Fast forward six or seven centuries, and the concept of defensible moats now also applies to corporations. As noted investor Warren Buffet said, “In business, I look for economic castles protected by unbreachable moats.”

Twenty-first century pharma and biotech companies have embraced this metaphor, employing technology, marketing and sales defenses to prevent competitive encroachment on their kingdoms. The most strategic players in the life science industry look to ensure continued success by creating as many unassailable, defensible moats around their products, offerings and intellectual property as possible to protect and grow market share.

Pharmaceutical organizations today face many common struggles – lack of unlimited funding, pressure to shorten new therapy time-to-market, tighter-than-ever regulations, and increasing numbers of competitors. Needless to say, pharma needs to create and leverage new barriers, or moats, to overcome these obstacles, stay relevant and ensure market share.

Artificial Intelligence (AI)-informed Systems of Intelligence and Virtual Assistants offer two powerful answers.

AI-Powered Systems of Intelligence

Cutting-edge data analytics such as Artificial Intelligence (AI) and Machine Learning (ML)-informed Systems of Intelligence offer indispensable solutions that provide pharma with strong defensive moats for protecting the castle, as well as offensive advantages that enable kingdom, or market share, expansion. To continue our medieval analogy, an effective data analytics partner who can deliver such solutions should be thought of as the armor-clad knight who fortifies the castle’s defenses and ensures that they hold.

In life science’s bygone days, Systems of Record and Systems of Engagement were pharma’s defensible moats. In today’s world, they are outdated and require reinforcements. Enter Systems of Intelligence. Systems of Intelligence build upon and integrate existing Systems of Records, such as Clinical Trial Management Systems (CTMS), Electronic Data Capture (EDC), Enterprise Resource Planning (ERP), and Customer Relationship Management (CRM), and also enhance Systems of Engagement that reach and touch employees and customers directly. Real-time analytics are applied to leverage these platforms in different and insightful ways and enable new business capabilities. But the unique differentiating factor, what makes the Systems of Intelligence moats deeper, wider and more defensible, is the application of AI. Layering AI - deep learning technology that leverages artificial neural networks to support self-learning by computers - onto Systems of Intelligence is truly a game changer.

Until recently, it was almost impossible for any global pharmaceutical company to have an enterprise-level view of their own data. With numerous and simultaneous clinical trials and other protocols, the logistical and practical challenges of accessing, aggregating, cleaning and interpreting an organization’s many and varied data silos was overwhelming, and leveraging this big data for any kind of business advantage seemed the stuff of fairy tales.

AI has changed that forever. Pharmaceutical and biotechnology companies now have access to cutting-edge, AI-enabled clinical analytics platforms that serve as powerful defensible moats by seamlessly integrating, curating, and animating data, delivering more actionable insights. Such Systems of Intelligence provide cleansed, aggregated operational and clinical data for more cost-effective management of clinical trials. Machine Learning-augmented analysis normalizes disparate sources of data from a variety of systems across an enterprise, such as CTMS, EDC, financial, etc., to create a persona-based, holistic view of clinical trials. Life Sciences companies can optimize clinical development programs to achieve faster execution, potentially shaving up to six months off a clinical trial timeline, and reduced costs, realizing possible operational savings in excess of $1.5M per trial.

AI-Powered Virtual Assistants

AI-powered virtual assistants that harness Natural Language Processing (NLP) and Natural Language Understanding (NLU) facilitate an unprecedented conversational experience (CX) with clinical trial data.  This next-level CX overcomes the planning, feasibility and conduct challenges historically associated with clinical processes and pervasive throughout the drug development continuum. Such virtual assistants improve the life sciences industry’s ability to deliver safe and effective therapies, and offer competitive advantages to the pharma or biotech organizations advanced enough to leverage this innovative technology.

AI-directed virtual assistants can solve many of the business challenges common to the drug developmental lifecycle, bringing increased efficiencies and cost savings to critical outcomes such as patient recruitment, protocol adherence, prediction of study success, continuous process improvement, timely and accurate analytics insight, patient data privacy, and the ability to leverage previously untapped sources of data. One mid-sized biopharmaceutical company focused on the discovery, development and commercialization of oncology products is currently deploying such a system to support clinical operations and optimize trial site selection. They realized a 50% improvement in Cycle-Time to Information (CTI), resulting in a dramatic decrease in time spent wrangling data.

Despite being separated by hundreds of years, the digital age and the medieval age are more alike than one might think. Castles and kingdoms still need to be protected from competitive encroachment, but the moats that defend them most effectively in the 21st century are built with next-generation, AI-informed technology instead of water, and the companies that employ this innovative technology to manage their enterprises and defend their castles clearly have the competitive advantage on the life sciences battlefield.


Karim Damji is Senior Vice President, Product Management & Marketing at Saama Technologies.


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