Discover information – but what exactly? Connections, relationships, people and/or content – how should this be analyzed? How should we connect the dots, and how can information be assessed to meet university governance policies?
Throughout their campus life, students and researchers are asked to discover, investigate, and analyze data to provide input for their work. There is enough data to be discovered, but is this discovery intuitive and does it also meet governance policies?
Recently I read a guide published at EDUCAUSE that stated “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 – Explore
It is a challenge to find useful and trustful data, but even more difficult to analyze it and to connect the dots in an efficient way.
I would like to explore this from a researcher’s point of view.
Research groups and institutions rely on research grants and funds. The competition for funds has a major impact on research projects and their success.
Before a group can start a research project they usually look for the best fitting call. A call fits if it matches with the skillset of the researchers and the project facilities, and depends on whether the research proposal is accepted and sponsored. Therefore, the research group needs to search, discover and explore for relevant data from many sources, in different formats, structured and unstructured. The search should make use of flexible filters which allows some kind of a pre-assessment of the research call.
Research groups need a solution that allows the discovery for:
To assess a research call in relation to the key figures such as skillset, sponsors, research success it is not sufficient to discover and collect data. Data also needs to be analyzed. The research group needs to create analyses and predictive analytics in real-time, make use of AI capabilities to quickly assess research calls, and instant decisions..
The capability to simplify Big Data consolidation and embrace Visual Analytics is a prerequisite.
The consolidation of big data need to be simplified to help research groups to easily access and analyze their data sources and faster consolidate data from all internal and external sources. This means slicing and dicing big data faster. It is a need to easily gain meaningful insights into all data in various structured and unstructured formats, including texts. This would allow researchers to assess and evaluate the discovered and consolidated data. Through easy to understand visualization researchers could be enabled to grasp trends, pattern, and validate information. This gained knowledge could be shared and understood with colleagues and partners on any device.
As research is working international and across industries and fields, visual analytics is needed to help the researcher to visually discover the unknown connections between the objects. The researcher would need to visually navigate between connected objects and understand how they are related. If case objects have a geospatial reference like an address or geocoordinates, they should be shown on map.
Of course, researchers also need to explore how the researcher and research projects are related to each other over the time, how object relation has changed over the time
To see the wood for the trees, the researcher needs to:
To be on the safe side and meet institutional governance, research groups need:
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.
Throughout their campus life, students and researchers are asked to discover, investigate, and analyze data to provide input for their work. There is enough data to be discovered, but is this discovery intuitive and does it also meet governance policies?
Recently I read a guide published at EDUCAUSE that stated “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 – Explore
It is a challenge to find useful and trustful data, but even more difficult to analyze it and to connect the dots in an efficient way.
I would like to explore this from a researcher’s point of view.
Research groups and institutions rely on research grants and funds. The competition for funds has a major impact on research projects and their success.
Before a group can start a research project they usually look for the best fitting call. A call fits if it matches with the skillset of the researchers and the project facilities, and depends on whether the research proposal is accepted and sponsored. Therefore, the research group needs to search, discover and explore for relevant data from many sources, in different formats, structured and unstructured. The search should make use of flexible filters which allows some kind of a pre-assessment of the research call.
Research groups need a solution that allows the discovery for:
- Any type of entity like persons, objects, locations, etc.
- Relationships between these entities
- The chronological order of how entities became related
- Discover entities geospatially with any map layer
To assess a research call in relation to the key figures such as skillset, sponsors, research success it is not sufficient to discover and collect data. Data also needs to be analyzed. The research group needs to create analyses and predictive analytics in real-time, make use of AI capabilities to quickly assess research calls, and instant decisions..
The capability to simplify Big Data consolidation and embrace Visual Analytics is a prerequisite.
The consolidation of big data need to be simplified to help research groups to easily access and analyze their data sources and faster consolidate data from all internal and external sources. This means slicing and dicing big data faster. It is a need to easily gain meaningful insights into all data in various structured and unstructured formats, including texts. This would allow researchers to assess and evaluate the discovered and consolidated data. Through easy to understand visualization researchers could be enabled to grasp trends, pattern, and validate information. This gained knowledge could be shared and understood with colleagues and partners on any device.
As research is working international and across industries and fields, visual analytics is needed to help the researcher to visually discover the unknown connections between the objects. The researcher would need to visually navigate between connected objects and understand how they are related. If case objects have a geospatial reference like an address or geocoordinates, they should be shown on map.
Of course, researchers also need to explore how the researcher and research projects are related to each other over the time, how object relation has changed over the time
To see the wood for the trees, the researcher needs to:
- Run a research network analysis by providing relationships of data objects and visual cues to improve decision making
- Gain deeper insight by connecting data silos for a single point of truth
To be on the safe side and meet institutional governance, research groups need:
- Full control over the platform, the application and content as well as how the 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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