Data Integration Highlighted as a Priority IT Need

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The health care industry is a growing job source for IT professionals. According to a recent report, “Leading Indicators in Life Sciences” from Life Science Insights, data integration has been highlighted as a key challenge facing its businesses and institutions. The life sciences involve drug discovery, clinical trials and pharmaceutical supply chain optimization, all of which are complex tasks that involve data from many sources often in completely different formats.

“Data integration is important in life sciences for two reasons,” said Dr. James Golden, vice president of research, Life Science Insights. “The first reason is all of the life sciences companies are merging and acquiring. When you do that you have huge data shops that have to be merged together. That’s a very difficult and important thing to do. It requires very special skills and some real knowledge of IT to put the stuff together. The second reason why data integration is so important in things like molecular biology, which is at the core of most of life science, you’ve got a lot of different kinds of disparate data. You’ve got sequencing data, gene expression data, protein data, and to really get down and understand biology and disease you have to put all this data together in a comprehensive model that allows all of your scientists to understand gene to protein, protein to tissue or pathway, pathway to disease, disease to patient. Data integration is the sine qua non (Latin – the number one goal) of putting all of this stuff together. Data integration is the heart and soul of the new biology.”


Golden said that job titles involving computational biology, bioinformatics, all manner of software development, running data servers and storage is a huge part of integration. “Anybody today who’s developing programming languages, who’s a programmer and works in almost any kind of IT, they’re going to be able to make a contribution to this data integration,” said Golden.


The need for technology capable of seamlessly integrating data and making it available to users across a wide range of functions is both a priority in terms of spending plans and a driver in opening up opportunities to improve operations and enable broad-based collaborative work practices. “Key data integration spending priorities in the life sciences include document management systems, middleware and software purchases as well as the development of browser-based data access systems,” said Judy Hanover, research analyst, Life Science Insights.


In the pharmaceutical and biotechnology sector, second-quarter results show spending is heavily weighted on software, which accounts for 41 percent of expenditures in 2004. Other spending priorities include data integration tools, databases and middleware as well as data storage with a focus on size, latency, bandwidth and management tools.


For the majority of respondents in the clinical trials area, clinical trial process redesign to support better integration is a priority, as 74 percent seek to save time and money while increasing research quality. Many of the process redesign projects center around electronic data capture (EDC) and related technology with 32 percent of respondents saying that more than three quarters of their trials have been automated using EDC.


In the drug discovery and development space, laboratories are aggressively adopting automation technologies with the capabilities to revolutionize research practices and add functionality to the process. Driving this automation are centralized LIMS implementations replacing multiple, disaggregated systems as well as electronic lab notebook (ELN) implementations. The survey shows adoption of ELN technology doubling in 2004. In early stage drug development, 32 percent of respondents expect to adopt the technology within the next 12 months.


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