4 Benefits of Data Lakehouses for Higher Education
Fast rising in popularity, the newest data collection and management technology for higher education is the data lakehouse.
A data lakehouse is the future of data management in that it combines all the benefits of a data warehouse with the flexible storage of data lakes to hold both structured and unstructured data while creating completely new capabilities not previously possible in either.
Even more importantly, data lakehouses have begun to aid higher education data users to simplify data management, quickly work with data, and obtain meaningful insights from data now unified from previously siloed systems.
In this article, we’ll dive into four benefits of data lakehouses in higher education.
1. Adaptable Data Collection
Since data lakehouses are a combination of a data warehouse and a data lake, they are able to store both structured and unstructured data.
This structured data that higher education data users are used to storing in a data warehouse is highly specific and stored in its predefined format. Commonly extracted from key operational systems, such as the student information system, learning management system, and others.
Unstructured data refers to the rest of an institution's data, including text files, pdf files, photos, video files, comments, and more. Unstructured data is a collection of many varied data types stored in their native formats.
Since data lakehouses can work with both structured and unstructured data, higher education institutions can benefit from working with unstructured data while only needing one technology stack and, as a result, create just one complete repository of data - the ultimate source of truth.
This means that higher education institutions don’t need to maintain two separate data systems such as a data warehouse and data lake to access and store all of their data.
Rather, they can more easily access all of their data from a single, unified source.
Being able to access all of your institution's data in a single spot means that faculty and IT can quickly find what they need and begin to work with their data much faster than when using both a data warehouse and data lake to store and structure data.
2. Reduction of Redundancy
The data lakehouse’s single all-purpose storage platform allows for a reduction of redundancy when working with data by leveraging data virtualization to ensure that the institution’s data can be used as needed without creating numerous, duplicative copies of source data that quickly become stale or outdated and lead to arguments over which data is “more right”.
With a data lakehouse, institutions can quickly create virtual instances for traditional functions, like “Development”, “Test”, and “Production” or even provide constituents with dedicated instances for their own needs. Imagine quickly providing an “IT” instance, an “institutional research” instance, or a “third party” instance that are presented as dedicated environments but are completely virtual and immediately reflective of the master source.
3. Artificial Intelligence and Machine Learning
Data in the warehouse generally feeds business intelligence (BI) analytics, while data in the lake is used for data science – which could include artificial intelligence (AI) such as machine learning, neural networking, and even storage for future, as-of-yet undefined use cases.
As a combination of both a data warehouse and data lake, the data lakehouse combines the BI analytics of a data warehouse with the AI machine learning of a data lake in one seamless system.
This creates a single system allowing data users to ask traditional questions and provide expected reporting while also offering the ability to leverage advanced data science tools to build models and obtain predictions that improve operations and support student success.
4. Cost Efficiency
Being able to store and manage all unstructured and structured data in one easily accessible solution also makes data lakehouses extremely cost efficient and reliable.
Without requiring a hybrid of multiple data warehouses and data lakes to be able to store and manage all of their data, higher education institutions are able to spend less on the single solution of a data lakehouse.
This means that data users won't have to waste time and money keeping up to date with multiple, isolated data storage systems containing siloed and redundant data that simply cannot seamlessly interoperate.
Basically empowering users to engage more without wasting time acquiring, staging, and preparing data for use.
Invoke Learning Solutions
At the forefront of advanced data lakehouses, Invoke Learning is determined to bring these next-generation technologies to the mission of higher education.
With our InvokeClarity™ solution, Invoke Learning brings colleges and universities the most advanced data lakehouse designed specifically for the needs of higher education.
InvokeClarity™ delivers flexible deployment, pre-built data collectors, and automatically enriched data.
From a modern data architecture jumpstart to a fully managed solution, InvokeClarity™ is designed to support the needs and capabilities of higher education institutions, no matter the size.
InvokeClarity™ is able to bring data together quickly with pre-built data connectors across most common higher education systems, which makes it possible to add a new system in just minutes!
InvokeClarity™ allows for deepened analytics by leveraging enriched data from the Bureau of Labor Statistics (BLS), the National Institute of Health (NIH), US Census Bureau, and more.
Check out Invoke Learning to learn more about InvokeClarity™ and their other next-generation solutions aimed at advancing higher education.