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The Age of Analytics and Education Sector Copy

SMR HR Group May 27, 2020
Speaker: Professor Dr. Mudiarasan Kuppusamy, Dean, Faculty of Business and Technology,
University of Cyberjaya

Professor Dr Mudiarasan Kuppusamy is currently the Dean for Faculty of Business and Technology of the University of Cyberjaya. Professor Mudiarasan has a PhD in Management from Western Sydney
University, Australia and specializes in the field of Information Systems.


Data and analytics capabilities have made a leap forward in recent years. The volume of available data has grown exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved. The convergence of these trends is fueling rapid technology advances and business disruptions and differentiate themselves, while others will find themselves increasingly at a disadvantage. Higher education institution are facing critical challenges ( fewer students, shifting demographics, increased competition and limited resources).

Four questions

  1. Descriptive Analytics (What happened?)
  2. Diagnostics Analytics (Why did it happen?)
  3. Predictive Analytics (What is going to happen?)
  4. Prescriptive Analytics (How can we sort it out?)

Key Points – The Age of Analytics

The six Vs of Big Data

  • Volume = The amount of data from myriad sources
  • Variety = The types of data (Structure, Semi- Structure & Un-structure)
  • Velocity = The speed at big data is generated.
  • Veracity = The degree to big data can be trusted.
  • Value = The business value of the data collected.
  • Variability = The ways in which the big data can be used and formatted.

Take Away

  • Embrace data culture & hygiene.
  • Transformation & growth can be done through analytics.
  • Keep your data source transparent.
  • Be accountable and connected with your clients (students).

Question and Answer

Q: What skills and competency needed by decision maker to manage and utilize data analytics?

A: Skills and competency is notion of data carry culture. Need to train everybody in the organization
to embrace to data sharing culture.

Q: How fast financial data analytics can be generated?

A: The speed of bringing data together and speed of the structuring the data to the understandable
format will assist to provide valuable result as each organization needed.