Life sciences companies and healthcare organizations can use it to develop and target new drugs, devices and services as well as to match patients with trials, the company said.
One of the industry-specific modifications that had to be made to SAP's existing Hana technology arose from the fact that Hana's natural language processing engine couldn't initially support medical terminology.
SAP built a new ontology model for medicine to make that work, Vandayar said, along with a common data model for clinical data and genomics.
"We believe this common data model is crucial," he explained. "If you don't get the data in the right format, it's hard to get any insights."
That problem will only get worse as the volumes of genomic and lifestyle data increase in the coming years thanks to falling sensor costs and the rise of the Internet of Things.
SAP provides full transparency into the data and gives users complete control over how it is used, processed and reported, it said.
The new SAP Medical Research Insights app, meanwhile, aims to help researchers integrate clinical, genomic and lifestyle data and then analyze it easily. They can slice and dice data, view it in timeline format and drill down to the level of a single patient, Vandayar said.
SAP's new technology has already been in testing for several months at numerous organizations, he added: "Some of the customers we spoke to claim that we do in a matter of minutes what used to take them several weeks or months."
Healthcare is one of the last industries to be digitized, largely because of regulatory issues and the particulars of the data, said Carlos Bustamante, a professor of genetics and biomedical data science at the Stanford School of Medicine, which has worked with SAP on genomic data analysis.
"Particularly in this country, electronic health records have basically been shoe-horned on top of billing -- they began essentially as a way to streamline the reimbursement process," explained Bustamante in an interview at the SAP event.
Yet the potential is enormous, he said.
"From Stanford's point of view, we think there are large questions to be answered at the intersection of complex data and analytics," Bustamante said, citing the example of population health in particular.
The challenges, however, may be equally considerable, including regulation, privacy, data security and societal implications.
"The digital revolution is a double-edged sword," Bustamante said. "Just because I can measure every aspect of your every muscle twitch, does that mean I should?"
If it's possible for a healthcare provider to predict a patient's stroke, it's almost a moral obligation for them to do so, he said, but the downside -- increasingly invasive monitoring that could lead toward a "nanny state" -- may also be significant.
"Where do we draw the line?" he said. "It's a question we all have to think about. I think we're only beginning to comprehend what the societal impacts are."
Sign up for MIS Asia eNewsletters.