Data mining technology has advanced tremendously over the course of the past decade. We’ve seen amazing breakthroughs – not least of which is the sheer expansion in how these tools can be used. In fact, it seems like every day there’s a report about a new, powerful context wherein data mining can deliver impressive results.
Here are just a few examples of how data mining use is diversifying across a huge range of sectors. As these and countless other cases demonstrate, there are really no limits to how data mining can be utilized – so long as it is combined with the right supplemental solutions, including IMSL embeddable algorithms. Organization leaders should always be on the lookout for new ways to leverage this technology.
“Universities are using data mining to identify alumni who are the most likely to donate.”
Data mining for donors
One noteworthy example of this trend can be found in the realm of higher education. The New York Times reported that a growing number of colleges and universities are using data mining and social media to identify and target alumni who are the most likely to donate to their alma maters. These schools can hire data mining companies to collect and examine information freely provided via Facebook, Twitter, LinkedIn and other social media sites.
These social networks tend to provide much more robust and accurate information than traditional strategies available to colleges – namely, cold calling and unpersonalized email and letters. And it’s not just a question of data collection. As James Stofan, vice president for alumni relations at Tulane University, told the source, there’s also the issue of alumni engagement. With data mining, schools can effectively interact with their former students long after they graduate. This makes alumni much more likely to donate to their alma maters, and it’s only possible thanks to advances in data mining use.
Genetic data mining
Another, unrelated example of the expanding benefits of data mining can be found in the realm of medical research. Specifically, 23andMe and Genentech are working together to mine patients’ genetic data to learn more about Parkinson’s disease. In particular, the organizations are looking for proteins on the DNA level that may be responsible for causing the disease, The Free Lance-Star reported. Parkinson’s is a particularly mysterious and poorly understood condition, but applying data mining techniques to affected patients’ genetic code may reveal commonalities that can lead to targeted treatment.
“We’re hoping it gives a clue about Parkinson’s genetic Achilles’ heel,” said Tim Behrens, senior director of human genetics for Genentech, the source reported.
Data mining future
These two examples really have nothing to do with one another in terms of the information desired or raw data analyzed, and that’s the point: Data mining’s applicability is not limited by industry, organization size or data type. To take advantage of these resources, firms simply need a target, the willpower, the expertise – and the right tools.
That’s where IMSL embeddable algorithms enter the picture. Rogue Wave IMSL mathematical and statistical functions are designed for maximum flexibility, and are used by organizations in a huge range of industries. These resources represent a key tool for developing high-performance data mining and analytics solutions.
• Download the datasheet to view the full list of capabilities IMSL provides.
• Walk through a few technical examples and code of how to use JMSL Numerical Libraries in Hadoop MapReduce applications