How modernizing your data infrastructure can lead to a smarter university

With modern technology, institutions can be re-imagined as smarter universities to offer a multitude of benefits beyond learning.
Micah Horner

For years, higher ed institutions have been collecting large amounts of data about their students, programs, and facilities. However, many have not been able to effectively use learner, academic or institutional data to improve resources, processes, and workflows.

But now, with modern technology, institutions can be re-imagined as smarter universities to offer a multitude of benefits beyond learning.

Achievable benefits

  • By analyzing their data, institutions can select the most innovative approaches to increase student engagement and success rates.
  • Analytics can help institutions determine ways to improve retention and graduation rates to improve revenue, such as receiving notifications when a student’s engagement is low to try to prevent the student from dropping out.
  • Using chatbots, educators can deliver personalized content to students to enhance teaching/learning.
  • With dashboards, alerts, and communication between faculty and students, institutions can improve academic advising by giving students information.
  • Data analytics can improve researchers’ access to information leading to better use of scientific literature and analysis.

Challenges to overcome

However, these benefits aren’t just there for the taking, and institutions and researchers face several challenges when it comes to effectively using their data:

  1. Storing and analyzing large volumes of existing and future data.
  1. Data such as documents, photos, audio recordings and videos is unstructured and cannot be stored in a database making it difficult to search and analyze.
  1. Disparate data is complicated to integrate.
  1. Gaining valuable insights quickly can be hard to achieve.
  1. Data validation from a variety of sources can pose a problem.
  1. Data governance is required to make the data more accurate and usable but can be complex with policies and technologies.
  1. Data security of advanced analytic tools – often in multiple locations – used for unstructured data and nonrelational databases can be difficult. Securing a large institution and adhering to data privacy laws and regulations can be challenging.

Cloud computing

Cloud computing presents a viable solution to unify all data into one platform to make it easier and faster to access, and less complicated to analyze valuable insights:

  • Implementation of cloud computing offers institutions unlimited scalability at a reduced cost. This means a better infrastructure with flexibility, access to data, ease of monitoring, and improving the quality of data processes.
  • Cloud computing gives institutions increased functional capabilities, such as better data analysis and access to machine learning algorithms that can improve decision-making.
  • Because the data is stored in one central location, institutions can access the data from anywhere on various platforms.
  • Using data analytics, institutions can create customized learning environments and better teaching methods for students and improve administrative processes to reduce potential dropout and failure rates.
  • For researchers, cloud computing can fundamentally change how they interact with data, devices, and each other. It offers them the benefits of being open, flexible, fast, cost-effective, scalable, efficient, and responsive.

The cloud computing infrastructure

The most appropriate cloud option for educational institutions to take advantage of these benefits is with data warehouses, data lakes, and data marts.

A data lake stores raw data throughout the institution even if there is no use for that data yet. In turn, the data warehouse stores all the data that has been prepared for research, reporting, and advanced analytics. This is often the preferred source to quickly report on and visualize organized data. The data mart provides views into a reduced set of data that a specific research unit or department can use.

With these three components, the infrastructure will look like this:

  • The data lake is the storage unit for the raw data in the data warehouse. From here, the data can be extracted for analytics and research.
  • The data warehouse models research data to support research and advanced analytics or machine learning.
  • Data marts are created from the data warehouse and organize the data into research views that can use the data for research and analytics by using built-in dashboards.

Final exam

By implementing this infrastructure, educational institutions ensure they have a robust solution that performs well and always makes data available for research, analytics, and artificial intelligence implementations. This infrastructure effectively ensures that institutions can meet the data retention requirements related to grant-funded research, is fully scalable, and can easily be adapted to accept new technologies. It supports ensuring that the data is secure and in compliance with legislation and regulations. To help remove complexities and extensive coding, data automation technology is available to build, manage, and migrate to data lakes, data warehouses, and data marts.

Micah Horner is the Product Marketing Manager at TimeXtender, a low-code, drag-and-drop, data estate builder that enables organizations to make better business decisions, while supporting compliance and governance of their data infrastructure. He is passionate about technology, storytelling, and strategic messaging.

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