Big Data Analytics: Challenge or Opportunity?

<2013 BD Presentation>
Copyright 2013 National University of Singapore. All Rights Reserved. 1
Big Data Analytics:
Challenge or
Opportunity?
Dr Carol Anne Hargreaves
Institute of Systems Science
National University of Singapore
Email: carol.hargreaves@nus.edu.sg
August 2014

Copyright 2013 National University of Singapore. All Rights Reserved.2
Volume
Every day, 15 petabytes
of new information are
being generated. Data
volume are expected to
double every 2 years.
Velocity
Data is coming at us faster
and faster than ever before
Variety
80% of new data growth is
from non-relational and non
traditional data types like
emails, documents, RFID
feeds, multimedia etc
Data is Everywhere…What makes Big Data
Challenging?
Veracity
The messiness of the
data (hash Tags,
abbrev., typos,
colloquial speech
Knowlege

Copyright 2013 National University of Singapore. All Rights Reserved.
My smart phone tells me how many
steps I have taken, the calories I
have burnt each day as well as how
well I have slept each night, etc.
Many devices are now internet-
enabled so that they self-generate
and share data.
What is Big Data- Data is Everywhere…..
Companies and organisations are
also keeping a digital record of
everything that is going on: Just think
of all the data generated daily in our
financial systems, stock control
systems, ordering systems, sales
transaction systems

Data Engineering & Processing
(Big Data Technologies)
(Hadoop, Hbase, MongoDB, SQL)
Data
Driven
Decisions
Supports
Data Science/Business Analytics
Data mining, Text Mining, Decision Trees, Neural Networks, Bayesian Networks
Business
Knowledge
Application/Specific
Business Solutions
Recommendation Systems, Ad Placement Optimisation, Health Risk Systems
Business
Problem
What is Big Data?.....................…..

Business Monitoring
• Key Business Processes
• Business Intelligence ….Data Warehouses
Business Insights
• Detailed Customer Transaction Data
• External Unstructured Data (Internet, Emails)
• Real time Data Access (Event happens, Analyse & Act)
Predictive Analytics
• Score events most likely to happen
• Location Situation Awareness
• Recommendation Systems
Business Optimisation
• Optimised Pricing Systems
• Optimised Ad Serving
• Optimised targeted Marketing Systems
What are the Big Data Opportunities?
Big Data
Big Opportunity
Data Cost
Data Asset
Data
Monetisation
Smart Products
Business Insights
User Experience

What are the Big Data Opportunities?
Big Data is being used to streamline its delivery
routes and cut fuel costs. Vans are outfitted with
sensors that monitor vehicle speed, location and
more. By analyzing the GPS data as well as
information from fuel-efficiency sensors, UPS
cut its fuel consumption by 8.4 million gallons
and cut 85 million miles off its routes in 2011.
Using Big data analytics doctors are better able
to target and understand high risk CHF patients
by utilizing natural language processing to
extract key elements from unstructured History
and Physical, Discharge Summaries, and
Consult Notes.

Making the Most of Big Data….
Data Visualisation is the Key! A few charts
with great data visualization are worth 1,000
slides. The key: reports should be easy to
understand as well as recommend the next
actionable step for business leaders.
Data Insights Action Measure

For example, we found 30 out of 500,000
customers who were at high risk of leaving.
That's a small number, but the loss of those
customers would have meant a loss of about
$4 million dollars.
If the company had waited for a completed
data warehouse implementation, this insight
would have been missed.
Use an Agile Approach for your Big Data Journey

Many businesses see data as costs, risks,
and liabilities. Data is not seen as a source
of value, but of additional work.
What if nurses & doctors could remotely
monitor patients through real time
notifications based on pre-set thresholds
set by the doctor, based on the patient’s
condition.
Most healthcare professionals think data
innovation means more work for them, not
less, and takes away time from what they see
as their key priorities in providing quality care.

BIG DATA consumes large space that wouldn't fit in
the memory even with thousands of machines. And if
it did, communication between those machines will
block the network.
Big data does not simply require big storage – it
requires big smart storage.
The biggest challenge being faced as we
enter the world of big data - we need to
develop the people who will drive and
implement useful big data systems.
New storage systems must automatically
and non-disruptively migrate data from
one generation of a system to another to
effectively address long-term

THE BIGGEST CHALLENGE for the use of “big
data” is business knowledge, not technical. The
challenge is figuring out how business people,
both users, management and technical, will
actually use data in practice.
Different users imagine data in very
different ways. Understanding this
key fact about data helps to
understand why so-called “big data”
solutions are so difficult to
implement in practice.

Lack of Understanding - How to Apply
Analytics to improve Business
• Organisations want better ways to communicate complex
insights so they can quickly absorb the meaning of the data
and take action.
• Leading organisation’s are now using data visualization as
their first step in understanding their data and then process
simulation.
3 – 5 Day Business Analytics Courses
MTech in Enterprise Business Analytics

Copyright 2013 National University of Singapore. All Rights Reserved.
Dr Carol Anne Hargreaves
PhD (Statistics) University of South Africa. MBA University of Wales (Cardiff)
• Chief of Business Analytics Practice at ISS.
Responsible for Consulting, Research & Teaching.
Currently teaching – MTech (EBAC), NICF Customer Analytics, NICF Predictive Analytic
NICF Statistics Bootcamp using R & Tableau & NICF Statistics for Business (2011-present)
• Prior to joining ISS, worked at Cegedim Strategic Data (CSD)(2009-2010) in Australia as
a Quantitative Methods Manager. Provided primary and secondary market research
analytics for client's in (customer profiling, target market segmentation, brand positioning,
competition, etc and was responsible for all quantitative research projects.
• In 2009, worked as a Statistical Modelling Analyst at Foxtel. Responsible for the Churn, Response,
Acquisition Modelling.
• In 2005-2008, worked as an Advanced Analytics Statistician at Synovate Aztec in Australia.
Developed models that enhanced the effectiveness of sales and marketing initiatives for clients
• In 2002-2005, worked as a Biostatistician at the National Health Medical Research Council (NHMRC)
at the University of Sydney. Analyzed and reported on the status and results of various Cancer and
Cardiovascular Clinical Trials.Advised on when a study should be stopped.
• In 1989-2002, while in South Africa, lectured statistics at several universities including the University of
Durban - Westville and the University of Natal

Big Data Analytics: Challenge or Opportunity?

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