In the last few years, IBM has acquired two companies to help it improve its offerings to the education industry in the predictive analytics space.
In 2007, IBM acquired Cognos to accelerate its information-on-demand business initiative, followed by the purchase in 2009 of SPSS.
IBM Cognos helps schools improve student performance, deliver on performance mandates, and improve financial performance, IBM says, by aggregating critical data and identifying trends.
For example, Cognos can help schools:
- Calculate curriculum costs, or identify good fundraising programs.
- Monitor student headcount and performance, program outcomes, school reputation, national agendas, and other key performance indicators.
- Share secure web-based information with all stakeholders.
- Manage endowments and recruitment through driver-based planning.
- Spot high- and low-performance schools or programs.
- Map enrollment to attendance and attendance to performance.
- Speed compliance reporting.
IBM Cognos is currently in use by more than 1,000 institutions of higher education and more than 530 K-12 school districts (representing more than 20,000 schools), IBM says. Additionally, 13 state departments of education and the federal Education Department use IBM Cognos.
IBM’s SPSS Modeler is another tool that schools have found of great benefit in predictive analytics. With Modeler, users can access a broad range of data, including data stored in operational databases and files, as well as unstructured data such as call center notes, eMail messages, Web 2.0 sources, and survey responses, which can be mined, modeled, and deployed via a simple desktop tool or via advanced client server architecture. This allows organizations to integrate predictive analytics into their everyday business processes, IBM says.
The data can be used to create predictive models in a way that doesn’t require programming, which means users can access information without waiting weeks for their IT department to respond to data requests.
SPSS Modeler provides deeper insights and more accurate predictions than simple analytics, because it uses all data assets to provide a complete view of a school district’s data, regardless of where these are stored.
For example, a community college in California uses Modeler to predict which students are less likely to return to school, helping faculty and administrators improve retention by providing appropriate counseling, financial aid packages, and curriculum offerings.
Partly as a result of these programs, the college ranks third among the state’s community colleges for the percentage of students successfully transferring to the University of California system.
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