Predicting Pharma’s Data-driven Future

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Published in Sept/Oct 2015 issue of Pharmaceutical Commerce Magazine.

By Dharma Subramanian, Director Product Management, Data-driven Applications, Reltio

Excellence in the Pharamaceutical industry means firing on all cylinders when it comes to innovation, agility and compliance. Thanks to mergers and acquisitions, increased regulatory scrutiny, access constraints, financial pressures, and a more informed and engaged consumer, the industry is facing a multi-fold increase in complexity in support of day-to-day operations. What was once an inward focused, brand-centric industry is now evolving to a one in which emphasis is placed on an outside view.

For many, this requires a radical change in attitude and foundational capabilities. It requires a deeper understanding of the factors that influence patients, caregivers, health systems, and payers, as well as transforming from a brand-centric push-based strategy to one that’s based on empathic listening. That, in turn, means being able to integrate, view and make sense of an avalanche of data from both within and outside the enterprise, so that they can be data-driven across every facet of their business.

Investing in Patient-centricity

Pharma companies are adopting patient-centric business models as the market dynamics shift in favor of engaging consumers, and being able to demonstrate improvements to the quality of their lives. Transitioning from legacy business models means they have to overcome the inertia of the past and earn public trust. As one recent industry executive recently put it:

But this is easier said than done. These elements require a data-driven closed-loop from which Pharma companies can continuously learn and improve as they embark on this journey.

In addition to a clear vision, supportive leadership and change management methodologies, Pharma companies must look to a whole new set of capabilities to support the transformation. New channels, applications and data types are needed to engage with end consumers. The mechanism of interaction with patients, caregivers and support groups may vary depending on the kinds of health conditions involved, regional / geographical cultural nuances, and the socio-economic conditions of the target population.  For example, engaging patient and consumer groups in North African countries to combat Ebola requires a significantly different set of capabilities than engaging patient and support groups in the USA for juvenile diabetes management.

Once those mechanisms are established, companies need the ability to rapidly analyze data to narrow down on true needs of their target audience, to identify and develop strategic responses in the form of a product, service or an integrated solution, and to track and monitor the effectiveness of their offering. In other words, they have to be data-driven.

The Rise of Enterprise Data-driven Applications

Facebook and LinkedIn are examples of easy-to-use applications that support the exchange and interaction using all types of data. They are personalized and engaging, offering relevant insight and recommended actions to make the experience valuable, while encouraging the exchange and self-management of personal details. These data-driven applications provide corresponding value based on the role and goal of the user. LinkedIn for example offers suggested jobs to seekers, while recruiters get filtered list of target candidates. Marketing users can prospect for future customers, while sales teams can understand relationships, connections and job history for account planning and engagement. All of these capabilities are built on a foundation of data that is ever expanding, continuously revealing relationships, and adding new capabilities and types of device access at will.

Today, Pharma companies are starting to use enterprise-class data-driven applications to support their teams in their own R&D, sales, marketing and compliance areas. Pulling together information across a broad spectrum of data sources that include provider, payer, plans, products, patient, transactions, social media, third-party data and more. Like LinkedIn, pharma-specific data-driven applications can make recommendations based on insights. “You should connect with Dr. Jones, through Dr. Smith who sits on the same committee as him. You last met with Dr. Smith last week. Her email is dr@smith.com.” And like LinkedIn, who closes the loop and knows that you took the job that it recommended because you updated your LinkedIn profile, a pharma data-driven application can correlate the action you took back to each recommendation by reviewing transactions executed in external applications, or directly through activities within the data-driven application itself. Full historical tracking of every click and update to data supports how data is being used, and maintains a detailed audit of what has occurred for compliance purposes, or to revert back to a previous set of details if needed.

This is a far cry from legacy applications and systems that were designed to solve a specific business problem through a fixed process or workflow.  Bringing together data from all parts of the enterprise and organizing it in such a way that all teams can interact with and interrogate the data as needed, has been a major goal for over a decade. A discipline known as Master Data Management (MDM) promised a full 360 view of customers. This has had varying degrees of success due to technology limitations, costly implementation cycles, and ever-growing data types and volumes. To add insult to injury, these systems ended up creating silos of information that they once set out to unify. Most recently big data infrastructure and tools have been popular, promising the ability to store any and all data in a single location, then running analytics on demand to uncover insights. Both MDM and Big Data with analytics each solve just a piece of the puzzle.

Today’s modern data management platforms offer both master data discipline and big data scale and agility. Leveraging the power of graph technology similar to those used by Facebook and LinkedIn, as well as built-in analytics and machine learning. Using a seamlessly integrated data management foundation, pharma companies are starting to roll out a variety of data-driven applications to grow their business.

Data-driven KAM

Account-based selling and Key Account Management (KAM) are not new to Pharma industry. However, market consolidation, newer kinds of business entities such as Accountable Care Organizations (ACO), and the resulting shift in power and influence have required the industry to adopt new sales management approaches and initiatives. For most, the siloed data and capabilities provided by traditional CRM and ERP systems are not able to fully support the wide breadth of information that requires continuous capture and collaboration.

A top Pharma company recently tasked a separate go-to-market team to cultivate new kinds of relationships at the C-suite and senior executive level at major accounts. Known for their use of innovative new technologies, this top 10 pharma chose a data-driven application built on a modern data management foundation to converge and continuously keep data about individuals and organizations reliable. Letting the system uncover relationships and hierarchies across all sources of information. Account teams get one-click access to information from any device, and are able to collaborate and contribute updates to profiles based on their real-time interactions with customers in the field.

Relevant insight into hierarchies and relationships between accounts and individuals opens the doors to sophisticated and targeted account planning. Questions that once were a challenge can now be easily answered

• Who are my customers and their value to my organization?

• What commercial opportunities can be identified at the corporate, subsidiary and local level?

• What should be my plan of action to convert those opportunities into wins?

• Who are the key stakeholders and influencers that I should target?

All this is possible because a data-driven application can identify and resolve inaccuracies it uncovers, maintaining reliable data about healthcare providers, administrators, and C-suite stakeholders. New sources of information can be added at a moments notice, providing a framework that adapts to the needs of the business.

Data-driven KOL

The importance of Key Opinion Leaders (KOL), is increasing across the drug development and marketing value chain. Pharma companies have institutionalized formal programs to identify and manage Thought Leader relationships and engagements. Working with and compensating Thought Leaders governed by compliance requirements are subject to public disclosure through Open Payments (Sunshine Act). Inconsistent processes and standards used to evaluate and manage Thought Leaders can result in significant time and expense, suboptimal selection, and increased risk of non-compliance.

A public pharmaceutical company decided to revamp its Thought Leader management processes to standardize across both functional and therapeutic areas. The company also realized that their current set of Thought Leaders have been “grandfathered in” because of pre-existing relationships and lacked insight into recent contributions in the therapeutic areas of interest, and ranking relative to peers. Meanwhile, the scale and variety of external data sets such as publications, clinical trials, claims and affiliations, combined with operational data generated by internal systems was inherently multidimensional and complex.

They partnered with a trusted Thought Leader data vendor to help build a data-driven application bringing together all disparate data sets for the Brand, Medical and Clinical teams for a therapeutic area. By providing them with the access to the most current and up-to-date views of Thought Leaders’ expertise, publications, communities of practice, influence network, and history of industry engagements, they are able to improve the productivity and effectiveness of their programs, while decreasing administrative costs.

The data-driven application also allowed them to review and optimize their current Thought Leader set based on objective and quantifiable evaluation criteria instead of relying on tribal knowledge alone. Over time, the company intends to look to identify and leverage other indicators that will help them spot and cultivate relationships with the next generation of Thought Leaders.

Applications With Data Built-in

Legacy applications meant that you purchased software, and then started the process of entering or loading data into the solution. If needed you licensed third-party data from vendors based on volume, segmented demographics, and had IT teams load and update the information on a semi-regular cadence. Today’s data-driven applications come with built-in data-as-a-service. Third-party data providers are pre-populating data that is instantly accessible through the application by any business user. Think one-click purchase of an HCP record on demand, with the ability to search across all available vendors using any combination of selection criteria, and the ability to preview the information before agreeing to purchase. This is the new reality, where data-driven applications literally come with access to information at your fingertips. No more switching out of your CRM application to Google the address or photo of the HCP you’re about to visit, or verifying the route on Google maps to their office. Information in a data-driven application is always-on, always accessible and continuously made reliable.

With the right framework, any pharmaceutical company can start their data-driven journey. The key is to select a particular business need, so that success and time-to-value can be achieved quickly. Most data-driven technologies leveraged the cloud for fast startup, no impact upgrades, and reasonable ongoing licensing costs that even the smallest of pre-commercial startups can afford. For established enterprises with legacy investments, data-driven applications co-exist with CRM, ERP applications and data warehouses. A big bang is not required to see value, nor is it necessary to understand the complexities of Hadoop and other big data technologies, which seem to change and evolve daily.  

In addition to the examples previously listed, data-driven applications have been deployed in life sciences to support mergers and acquisitions, CIA compliance enforcement and reporting, product and competitive intelligence, plan and payer management, and more.

It is important for companies to begin incorporating patient data into their thinking, while continuing to evolve key account management as a discipline. Companies would like to accurately predict their own future, and being data-driven will help them do just that.