During their entire campus life, students and researchers are asked to discover, investigate, and analyze data to inform their work. There is plenty of data to be discovered, but is this discovery intuitive and does it comply with university governance?
And what are they supposed to discover? Connections, relationships, people, or content? How should they analyze the data and connect the dots? How should they assess and meet universities’ governance requirements?
Recently I read on EDUCAUSE:
“Data are everywhere and higher education has more than its fair share. From student data to business data to research data, there are no shortages of data in higher education. However, making use of that data in ways that move the institution forward and allow the institution to meet its varied goals can be a challenge.”
Search, connect, and explore
It is enough of a challenge to find useful and trustworthy data, but it’s even more difficult to analyze it and connect the dots in an efficient way.
From the researcher’s point of view, research groups and institutions rely on research grants and funds. The competition for funds has a major impact on a research project’s success.
Before beginning a new research project, the research group must search for a call that best fits their skillset and project facilities. This is so that, if the research proposal is accepted and sponsored, the research will be successful. The research group must search, discover, and explore relevant data from many sources, in different formats, both structured and unstructured. The search should make use of flexible filters that allow a pre-assessment of the research call.
Research groups need a solution that enables them to discover:
- Any type of entity like persons, objects, locations, etc.
- Relationships between these entities
- The chronological order of how entities became related
- If relevant, geospatial elements with a map layer
To assess a research call in relation to skillset, sponsors, and research success, it is not enough to merely discover and collect data. Data also has to be analyzed. The research group must create analyses and predictive analytics in real-time, using AI capabilities to quickly assess research calls and make decisions on the fly.
Simplifying Big Data consolidation and embracing visual analytics are prerequisites
Consolidation of Big Data must be simplified to help research groups to more easily and quickly access and analyze their data sources and consolidate data from all internal and external sources. Simply, they must be able to slice and dice Big Data faster to gain meaningful insights from data in various structured and unstructured formats, including texts. This would allow researchers to assess and evaluate discovered and consolidated data. Through easy-to-understand visualization, researchers can grasp trends and patterns and validate information. This knowledge can be shared and understood with colleagues and partners on any device.
As research crosses nations, industries, and fields, visual analytics helps researchers to literally see connections between objects. A researcher could visually navigate between connected objects and understand how they are related. If objects have a geospatial reference, like an address or geocoordinates, they can be shown on map.
Researchers also need to explore how researchers and research projects are related to each other and how object relationships have changed over time.
To see the tree from the forest, researchers must:
- Run a research network analysis by providing relationships of data objects and visual queues to improve decision making
- Get deeper insight by connecting data silos for a single point of truth
To meet institutional governance requirements, research groups need:
- Full control over the platform, the application, and content, as well as how data is integrated and processed
Easy data consumption and intuitive analysis and visualization accelerates innovation and improves research. It is all about connecting a mass of useful data in an endless network to improve intelligent decision making.
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