More companies across more industries are making artificial intelligence (AI) a priority. In fact, nearly half of the CIOs surveyed in Gartner’s 2018 CIO Agenda Survey plan to implement AI in the near future. There is significant potential for AI, especially in the life sciences industry. We are on the cusp of extraordinary AI breakthroughs, but there is a missing link: the right data foundation. Transforming the commercial data warehouse space is the key to pharmaceutical and biotech companies realizing the full potential of AI.
AI needs data to build intelligence and for that data to be useful, it must be organized in a standard way. For decades the industry has relied on building and maintaining its own custom-built data warehouses because of a lack of high-quality, packaged commercial data warehouse solutions. The challenge is custom-built solutions are not quickly adaptable to changing data structures and sources. This delays insights from reaching the business and leaves organizations ill-equipped to support AI.
A next-generation, packaged commercial data warehouse can give the industry the right commercial data foundation for AI and analytics. An industry-specific data model and standard integrations can unify a company’s most important data sources and help get business insights faster. With a next-generation data warehouse, organizations can keep their data current and better leverage AI tools to tap into the right data and drive intelligence across the organization.
Using AI to Get the Right Information to HCPs
AI is showing great promise in delivering insights that help customers get to the next best action and drive more intelligent customer engagement. Life sciences customers can use AI to predict when to get the right message to the right doctor at the best time. Pharma companies such as Pfizer are already using AI in their commercial operations and seeing early results.
“AI is showing great promise in delivering insights that help customers get to the next best action and drive more intelligent customer engagement”
“In the old world, commercial teams would send message one, two, three, and four to doctors – in that order,” said Randy Zagorin, director of digital solutions and emerging technologies at Pfizer. “Now, we're looking at doctors’ response history and what emails similar doctors open most often, and we're then 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.”
Early AI success demonstrates the opportunity moving forward to better understand more about individual physicians’ preferences and analyze different data sets in real-time. But that is only the beginning.
Tremendous Opportunities Ahead with the Right Data Foundation
For life sciences companies to leverage all that AI has to offer, the commercial data warehouse needs to better stay in sync with critical business systems and manage a growing volume and variety of data.
Standard data integrations help the business keep pace
Companies struggle to reconfigure their custom-built data warehouse to keep up with changing data structures and sources. The rate of change in transactional systems is too fast to manually maintain the data warehouse over time. As a result, there is a constant, growing chasm between the data warehouse and underlying systems like CRM. By the time changes in the data warehouse ever catchup, companies are usually starting to plan the rebuild of their next data warehouse.
“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,” said Dan Utzinger, vice president and CIO at Intra-Cellular Therapies, and former VP of IT at Eisai.
A packaged data warehouse solution built specifically for life sciences solves the constant manual construction of custom data warehouses. Seamless integration with CRM and content management systems, for example, would ensure customer activity data and information about a company’s content and its usage 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 and keep up with evolving business requirements. Companies would no longer have to worry about keeping their data manually in sync, and instead could focus on fine-tuning their data warehouse for AI tools to efficiently access the right data.
Handle larger volume and variety of data
IDC forecasts enterprise data to grow ten-fold by 2025. Recently at the 2018 Veeva Commercial & Medical Summit, Otsuka CEO Dr. William H. Carson describedthe phenomenon as a data tsunami.
“The current explosion of data is a double-edged sword,” said David Ehrlich, CEO of Aktana. “It can offer customer insights in ways that were never before possible, but it can also overwhelm and frustrate organizations that are not equipped to harness it.”
Perhaps more challenging than volume is dealing with the wide variety of data sources in life sciences such as CRM data, content data, prescription, sales, formulary, and claims data – and then keeping it all organized. Each life sciences company organizes its data differently in the data warehouse, making it challenging for packaged AI solutions to make sense of it.
A packaged, industry-specific next-generation data warehouse solution can provide a standard data model that brings together all of the industry’s most important data sources and organizes it in a way that makes it much more efficient to use AI and analyze the data. With the right foundation, life sciences companies will be equipped to apply AI to their data, identify meaningful patterns, and draw valuable learnings.
An AI Future Enabled by a Next-Generation Data Warehouse
A next-generation data warehouse provides the industry standard data model and data integrations that companies have long needed to take advantage of the full power of AI and uncover insights that were never before possible. The industry would be able to move away from manual, labor-intensive custom-built data warehouses and ensure that data is automatically flowing into the data warehouse and organized the right way.
Looking ahead, the commercial data warehouse will have the following characteristics:
Industry-specific–a ready-to-use solution that’s tailored to life sciences companies’ unique needs, including a standard data model for the industry.
Adaptive Design – automatically synchronize data between the data warehouse and data sources such as CRM and prescription, sales performance, formulary, and claims data.
Cloud-based – infinite scalability and petabytes-scale data storage ensure fast-query performance even on the largest data sets.
An industry-specific commercial data warehouse will empower life sciences companies to keep up with the fast pace of change and apply AI tools to comprehensive and current data for better insights. With a new approach, companies will finally have the right commercial data foundation for AI and analytics to deliver business insights faster.