Since Big Data is more of Strategy Issue than an IT issue and also bearing in mind that there are several sources of data (structured and unstructured) in a university setting. I need to know if there are Standard and principles that will guide the implementation of Big Data Analytics in a University Educational Management System.
Dear Okoro,
I think this is better known as Learning Analytics. There is an organisation in the UK called JISC who have done some work in this area to regularise processes.
Best Practices for Implementing Big Data and Data Sciences for Analytics:-
One aspect of digital transformation that organizations struggle to get right is the identifying, capturing, managing and analyzing of big data. Across all industries, organizations are keen to use this data and the work of data scientists to discover the insights that will drive strategic business decisions. CIOs today need analytics expertise as well as an understanding of the data sciences and algorithmic approaches that will provide data analytics to their companies
Begin with Knowledge
The first issue to overcome is defining what big data actually means. A common data language will foster the growth of the best ideas shared across diverse internal teams and trusted partners. Taking this first step will determine how an organization will harness the power of advanced analytics and benefit from big data
Pain Points of the EDW
What organizations are probably used to is a traditional enterprise data warehouse (EDW), which typically works with abstracted data that has been gathered into a separate database for specific analytics. EDW databases are based on stable data models. They ingest data from enterprise applications like CRM, ERP and financial systems. Various Extract, Transform, Load (ETL) processes update and maintain these databases incrementally, typically on hourly, weekly and monthly schedules. A typical EDW runs from hundreds of gigabytes to multiple terabytes.
Create the Data Platform That Works for You
Clarity is needed regarding the variety of data you’re working with – typically a combination of traditional structured data and relatively unstructured big datasets. Once you have sorted out what your data platform should look like, you will have a better understanding of how to manage and analyze the different types of data.
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