Managing the Exponential Growth of Life Sciences Data

The volume of data in life sciences continues to grow rapidly, contributing to the doubling of medical literature every three years. Clinical trials for digital pills like Otsuka’s Abilify MyCite now generate more data in just one trial than all of the company’s previous trials combined. Dr. William Carson, president & CEO of Otsuka Pharmaceuticals, has dubbed this the “data tsunami.” Life sciences companies increasingly rely on artificial intelligence (AI) to help make sense of this vast amount of data for new commercial opportunities.

But in order to realize the full potential of AI, companies first need to have the right data foundation in place. Pfizer is already taking important steps. “AI is showing great promise in delivering insights that help customers get to the next best action and drive more intelligent customer engagement,” says Randy Zagorin, director of digital solutions and emerging technologies at Pfizer. “In the old world, commercial teams would send message one, two, three, and four to doctors – in that order. Now, we’re looking at doctors’ response history and what emails similar doctors open most often, and they’re predicting–for each particular doctor–the best sequence to send emails with the greatest chance of being opened. We’ve definitely seen this have a positive impact and improve the effectiveness of email communications.”

With the spotlight on the industry to commercialize novel therapeutics, here are some important considerations for managing data in a next-generation data warehouse.

Data organized for AI consumption
Life sciences companies handle large numbers of data sources, including claims data, CRM, content, prescription, formulary, sales, and more. All of this data needs to be regularly cleaned and organized in a standard way for AI to make sense of it. In the past, an industry-specific data model with standard integrations was not available, forcing companies to rely on custom solutions. “Custom data warehouses are inherently inflexible, so it can take weeks to get answers to important questions every time a new data source is added or systems change,” says Dan Utzinger, vice president and CIO at Intra-Cellular Therapies, and former VP of IT at Eisai. What the industry requires is a next-generation warehouse with a standard data model to uniformly connect and organize all important data sources.

Standard integrations ensure information flow
The commercial data warehouse needs to stay in sync with other systems and manage not only a growing volume of data but a variety of sources as well. But data sources and structures constantly change, causing disconnected information flows between systems. A life sciences-specific data warehouse solves the integration issues inherent with custom data warehouses. It offers seamless integration with downstream systems, like CRM and content management, to ensure that customer activity and content usage information automatically sync into the data warehouse. Standard data integrations can give the industry greater flexibility to adjust to data changes as often and as fast as they occur, helping them keep up with evolving business requirements. “Companies no longer have to worry about manually synching data. They have the opportunity to focus on refining their data warehouse for AI tools to efficiently access the right data,” says Andy Fuchs, Sr. Director of Commercial Strategy at Veeva.

What a next-generation data warehouse looks like
Life sciences organizations must be confident that their data consistently flows from various data sources into the warehouse and is automatically organized the right way. While custom data warehouses have been unable to manage life sciences information, a next-generation commercial data warehouse is different:

• It is tailored to the unique needs of life sciences, including a standard industry data model.
• Its adaptive design will automatically synchronize data between sources and the warehouse.
• The cloud will enable petabytes-scale storage and infinite scalability to ensure outstanding performance even as data sets grow exponentially.

An industry-specific, next-generation commercial data warehouse like Veeva Nitro enables companies to stay current with the rapid growth of scientific and medical data, and drive better intelligence across the organization. A number of life sciences companies around the world are already implementing and using it to to deliver business insights faster. To learn more, watch Veeva CEO and founder Peter Gassner discuss how the life sciences industry can better address its data management challenges.