Why Life Sciences R&D Teams Like Data Virtualization
Accelerating data integration key to business success
Jul. 5, 2011 05:15 AM
In the life sciences industry, the latest blockbuster drug or device can mean billions in revenue.
But developing these new offerings often takes ten or more years.
Moving ahead a year or two can be worth hundreds of millions.
The question is how?
Data, Data, Data
Data is the life blood of new product development. Research findings, clinical trial results, manufacturing process validation, and more are information-intensive activities where each new data point can result in a major shift in plans and timing.
To accelerate time to market in this data rich environment, life science R&D teams look to IT.
Unfortunately, IT is not always well equipped to meet this challenge. The problem is not a lack of data. The problem is a lack of data integration agility.
Most life sciences companies have made significant investments in their data. These investments have resulted in data silos and complexity which slow down their ability to respond to new information requests. To overcome these silos, life sciences companies are seeking new ways to integrate their new product development data.
How Data Virtualization Helps
Data virtualization has been adopted by the vast majority of pharmaceutical vendors and has recently seen increasing acceptance at medical device makers. Use of data virtualization to integrate product development data has been the primary use case. The reasons are several:
- Gaining Timely Insight – Up-to-the-minute data is critical throughout every stage in the new product cycle. Data virtualization provides query optimization algorithms and techniques that deliver timely information whenever needed.
- Seeing the Complete Picture – Multiple types of data from multiple sources must be combined to provide researchers, analysts, and managers with the full picture thata effective decision making requires. Data virtualization provides data federation that virtually integrates multiple data sources data in memory, without the cost and overhead of physical data consolidation in data warehouses.
- Controlling Data Proliferation – Identifying and understanding data assets distributed across a range of R&D repositories and locations requires significant manual effort. Data virtualization provides data discovery that saves time by automating entity and relationship identification and accelerating data modeling.
- Addressing Data Complexity – Incredible complexity challenges IT’s ability to leverage existing R&D data for new R&D questions. Data virtualization provides powerful data abstraction tools that simplify complex data, transforming it from native structures and syntax into easy-to-understand, reusable views and data services with common, business-friendly semantics.
- Improving Data Availability – With so many technologies, formats and standards, successfully surfacing R&D life cycle data consumes significant IT resources. Data virtualization supports numerous standards-based data access, caching and delivery options that allows IT to flexibly publish all the information that R&D users require.
- Providing Proper Data Controls – Data is a critical asset that must be governed, especially in life science R&D with its myriad compliance requirements. Data virtualization provides data governance that centralizes metadata management, ensures data security and improves data quality to meet these stringent control requirements.
- Environment of Non-Stop Change – Ever changing research results, clinical trial findings, and compliance requirements make frequent change inevitable. Data virtualization provides a loosely-coupled data virtualization layer, rapid development tools, automated impact analysis and extensible architecture to provide the information agility required to keep pace.
Pfizer Finds a Successful Formula
The R&D team at Pfizer was an early adopter of data virtualization with a number of positive business benefits.
Their successes have been recognized in a number of recent articles including:
Fortunately the path to successful data virtualization adoption is far shorter than the new drug or device development path. But as with R&D, integrating available data is the key.
Here are some great data sources to help you get started: