IBM has made news on the big data front twice this week, first with Wednesday's acquisition of Pittsburgh-based Vivisimo, and today with an announcement from SUNY Buffalo about multiple sclerosis research.

Vivisimo develops federated discovery and navigation software meant to enable organizations to access and analyze big data enterprise-wide. With some 2.5 quintillion bytes of data created every day, IBM says the deal – terms of which were not disclosed – will help accelerate its analytics initiatives, helping organizations such as healthcare providers, government agencies and telecommunications companies navigate and analyze the full variety, velocity and volume of structured and unstructured data.

"Navigating big data to uncover the right information is a key challenge for all industries," says Arvind Krishna, general manager, Information Management, IBM Software Group, in a statement. "The winners in the era of big data will be those who unlock their information assets to drive innovation, make real-time decisions, and gain actionable insights to be more competitive."

"As part of IBM, we can bring clients the quickest and most accurate access to information necessary to drive growth initiatives," added Vivisimo CEO John Kealey.

[See also: 6 keys to making better use of your data .]

On Thursday, IBM announced that researchers at the State University of New York (SUNY) at Buffalo are using its analytics technology to study more than 2,000 genetic and environmental factors that may contribute to multiple sclerosis (MS) symptoms.

The initiative finds scientists using IBM's technology to develop algorithms for large genomic datasets to uncover critical factors that speed up disease progression in MS patients. Insights gained from that research will eventually be shared with physicians to help them tailor individual treatments to slow brain injury, physical disability and cognitive impairments caused by MS.

Affecting approximately 400,000 people in the United States and some 2.1 million people worldwide, MS is a chronic neurological disease for which there is no cure. It is believed to be caused by a combination of genetic, environmental, infectious and autoimmune factors, making treatment difficult.

SUNY Buffalo researchers will explore clinical and patient data to find hidden trends among MS patients by looking at factors such as gender, geography, ethnicity, diet, exercise, sun exposure and living and working conditions, IBM officials say. All that data – including medical records, lab results, MRI scans and patient surveys – arrive in various formats and sizes, requiring researchers to spend days making it manageable before they can analyze it.

[See also: IBM acquires Initiate Systems, bolsters role in healthcare IT.] 

Shawn Dolley, vice president and GM of global healthcare and life science at IBM Big Data (he worked for Marlborough, Mass.-based Netezza before IBM's acquisition of that company in 2010), remembers when Harvard Medical School researchers told him not long ago that, when it came to solving problems with big data analytics, "We have four questions we can ask per month." Why four? "Well, there's four weekends per month," he recalls them saying. "That's how long it takes us to run our jobs."

Now, using an IBM Netezza analytics appliance, in conjunction with software from Revolution Analytics, researchers can analyze disparate data in a matter of minutes instead of days, regardless of what type or size it is, say IBM officials. The technology automatically consumes and analyzes the data, and makes the results available for further analysis, leaving researchers more time to analyze trends.

"Having worked with data scientists for more than 15 years, for every hypothesis and insight that a subject matter expert has, for every needle in a haystack, there are 10 other insights that are in there that data crunching can find," says Dolley.

"There's no hypothesis a priori," he adds, "but if you go in and let a system find something that's statistically significant, which is the SUNY approach, and then it tells you there's something there, it's like, 'Let's figure out why that is.' It's almost a computer-driven insight, rather than a human-driven insight. What's revolutionary about SUNY is that they're letting the big data drive the insights rather than using the big data to test hypotheses."

Since 2007, SUNY Buffalo researchers have been studying clinical and historical data from MS patients to identify genetic and environmental factors that contribute to the risk of developing the disease. These researchers are studying different age groups to see why the disease appears early in some children and why people who are diagnosed later in life tend to have a more aggressive course that affects their ability to walk. They are also looking at why MS is more common in northern latitudes and less common towards the equator, calling into question the role sunlight or lack thereof plays in the disease.

"The analytics thing that was appealing to them was that they weren't going into this saying, 'We have a very specific study, where we have a deep belief that urban allergens have to do with multiple sclerosis so we think a certain string of genes can be compared to something else," says Dolley. "Their approach was, 'Let's do something that has combinatorial explosion, let's put every phenotype variable we can, let's put the subject genes in there, and let's run every possible combination."

The hope is that insights gleaned from this big data analysis can be applied to diseases such as MS and significantly change the way patients receive treatment.

“Multiple Sclerosis is a debilitating and complex disease whose cause is unknown," says Murali Ramanathan, lead researcher at SUNY Buffalo, in a statement. "No two people share the exact same symptoms, and individual symptoms can worsen unexpectedly. Identifying common trends across massive amounts of MS data is a monumental task that is much like trying to shoot a speeding bullet out of the sky with another bullet.

"IBM analytics helps our researchers fine tune their aim and match the speed of analysis with the rate of data coming into our systemsm," he adds. "Our goal is to demystify why the disease progresses more rapidly in some patients and get those insights back to other researchers, so they can find new treatments.”