Bringing big data to your school's analytics

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Higher Education Marketing

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Bringing Big Data to your School’s Analytics
Slide 1
Bringing Big Data to
your School’s Analytics

Bringing Big Data to your School’s Analytics
Slide 2
1) Big Data
2) The Four Vs
3) Educational Datasets
4) Recent Technological Advancements
Overview
Source: Higher Education Marketing – Bringing Big Data to your School’s Analytics

Bringing Big Data to your School’s Analytics
Slide 3
 Every day, we create multiple terabytes of data.
 It comes from every dimension: Social media, email communication,
digital graphics, online transaction and many more.
 Like any other niche, the education system produces a
tremendous amount of data as well.
 For example: student grades, campus IDs, homework, class syllabi
campus activities etc. When this data is combined and analyzed
properly, it can reveal astonishing insights about user behavior
patterns.
1. Big Data
Source: Higher Education Marketing – Bringing Big Data to your School’s Analytics

Bringing Big Data to your School’s Analytics
Slide 4
 Big Data is a term to describe a mammoth amount of
structured, semi structured and unstructured data-set.
 Big data spans over 4 dimensions: Volume, Velocity,
Variety and Veracity.
 Volume: Volume refers the amount of data that schools are trying to
analyze to improve decision-making and define scope for further
initiatives.
 Velocity: The speed at which new data is being created, captured
and analyzed by the school, in our current digitized world, data is
continually being created at a speed that is almost impossible for
traditional database systems to capture and process.
2. The Four Vs
Source: Higher Education Marketing – Bringing Big Data to your School’s Analytics

Bringing Big Data to your School’s Analytics
Slide 5
 Variety: Variety is about managing the complexity of multiple data
sources i.e. structured, semi-structured and unstructured. With the
continuous innovation and development of technologies and social
collaboration, data is being created in countless forms e.g. text,
sensor, graphics, log, web, etc. In our traditional database
management systems, the data that captured and processed, is
generally well structured and ordered.
 Veracity: It refers the degree of continuity and reliability of data. In
order for the data to be useful it has to be clean and reliable. For
example: data collected from Facebook posts and student activities
board may offer clues about user sentiments and the reliability of
such data is very much questionable.
2. The Four Vs
Source: Higher Education Marketing – Bringing Big Data to your School’s Analytics

Bringing Big Data to your School’s Analytics
Slide 6
 Schools and universities gather an astonishingly big amount
of data from various sources and activities.
 For the sake of simplicity, we divide educational data into three major
categories.
 Identity Data: The personal data of the end user.
 Interaction Data: Interaction data includes engagement
metrics and dimensions.
 Content Data: It includes content related data. Schools and
universities generate and process a really big amount of
content every year.
3. Educational Datasets
Source: Higher Education Marketing – Bringing Big Data to your School’s Analytics

Bringing Big Data to your School’s Analytics
Slide 7
 Recent advancement in technologies and data science
make it quite feasible to capture and process all these
different varieties of data. Here are a few key ways it can
affect your school:
 Determine student sentiments and behavior towards school.
 Identify the student’s core skills.
 Determine student performance and interaction with class and
course.
 Determine the effectiveness of classes and courses.
 Determine school reputation.
 To compete in a global economy, it is increasingly essential
that organizations need a comprehensive understanding of
market and user behavioral patterns.
4. Recent Technological Advancements
Source: Higher Education Marketing – Bringing Big Data to your School’s Analytics

Bringing Big Data to your School’s Analytics
Slide 8
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