Big Data Analytics: Challenges and Opportunities

Poo Kuan Hoong, Ph.D

Faculty of Computing & Informatics, Multimedia University

25th May 2016

Data Science Institute

• Data Science Institute comprises of expertise across faculties such as Faculty of Computing and Informatics, Faculty of Engineering, Faculty of Management and Faculty of Information Science and Technology.

• Members conduct research in leading data science areas including stream mining, video analytics, machine learning, next generation data visualization and advanced data modelling.

• The institute also aims to support the industries and government of Malaysia in training data scientists.

• The Data Science Institute envisions itself to be a leading partner for solving complex real-world data problems.

3

http://www.forbes.com/sites/louiscolumbus/2015/03/22/56-of-enterprises-will-increase-their-investment-in-big-data-over-the-next-three-years/

Infographic by: QMee

What is Big Data?

• Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured.

• Big data may be as important to business – and society – as the Internet has become. Why? More data may lead to more accurate analyses.

So what are the challenges of Big Data?

“high-volume, -velocity and -variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making” -Gartner

Big Data Analytics

3 phases of analytics

Descriptive Analytics• the simplest class of analytics

• condense big data into smaller, more useful nuggets of information

• Example: Roger Ebert Movie Ratings with Rating Scale (Ordinal): 0*, 0.5*,…,3.5*,4* of 5501 Films (rogerebert.com, circa 3/2006)

Rating Frequency Relative Freq Cumulative Freq

0.0 56 1.02% 1.02%

0.5 76 1.39% 2.41%

1.0 284 5.18% 7.58%

1.5 309 5.63% 13.22%

2.0 907 16.53% 29.75%

2.5 568 10.35% 40.10%

3.0 1684 30.70% 70.80%

3.5 840 15.31% 86.11%

4.0 762 13.89% 100.00%

Ebert Movie Ratings

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

Rating

Pe

rce

nt

Note: Median (and Modal) Category is 3.0, LQ is 2.0, UQ is 3.5

Predictive Analytics

• Predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends.

• The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behaviour.

Prescriptive Analytics

• Prescriptive Analytics extends beyond predictive analytics by specifying both the actions necessary to achieve predicted outcomes, and the interrelated effects of each decision

Embrace Big Data across your business

Units Sold, Discounts, and Profit

before Tax

Revenue and Target by Region Departments HeadcountXT2000 Status List

Show Only Problems

Indicator

Preliminary Budget

Materials and Packaging Review

Book Advertising Slots

Fall Showcase Event Analysis

End User Survey

Technical Review Milestone

Status 2M

1.5M

1M

0.5M

0M

Dis

cou

nts

(M

illio

ns)

50K 60K 70K 80K 90K 100K 110

Product A

Product D Product C

Product F

Product G

0 5 10 15

Accounting

Administration

Customer Support

Finance

Human Resources

IT

Marketing

R&D

Sales

SalesImprove revenueperformance

HRMaximize employee engagement

MarketingBuild deeper customer relationships

FinanceImpact your company’sbottom line

0

5

10

15

0

5

10

15

(Th

ou

san

ds)

North South

Region: SouthTarget: 13450Highlighted: 4900

Revenue Target

Big Data Analytics Solution

Capture and

integrate data from multiple internal

and external sources

Derive insight

from data with rich, interactive dashboards

and reports using the tools you know

Put insight

into action to increase efficiency

and constituent satisfaction

Use cases: Customer churn analysis

To reduce churn, know each customer individually to identify

warning signs. With a data analytics solution, demographics

and history data can be reviewed and monitored, Based on

customer data, company can actively engage with customer

with more personalized service in order to mitigate any

damages and build customer loyalty.

Reduce churn

with proactive

customer

campaigns

Customers churn happens for a lot

of reasons, including quality, service,

or feature issues, or new offers from

competitors. Individual analysis can

help reduce each.

9%Rate of wireless

subscribers switching services in Europe

and USA, 2009

Based on huge amount of customer purchase data

collected and managed a Hadoop-based solution, it is

able to serve useful and compelling cross-sell and up-sell

recommendations to customers.

Recommender Systems

Increase cross-

and up-sell

recommendations

on each product

Retailers can use customer

purchase & rating information to

serve recommendations to current

customers, based on similarities

across many dimensions

~30%E-commerce revenues

are from product recommendations such as cross-sell or up-sell.

Through sensors and other machine-generated data,

companies can identify when a malfunction is likely to

occur. The company can then preemptively order

pats and make repairs in order to avoid downtime

and lost profits.

Predictive Support

Increase

customer

satisfaction by

maintaining a

good safety

record.

Safety is one of the important

aspects for any airline industry. A

good and prompt maintenance of

the airplanes will increase the

realibility.

Southwest analyze sensor data on their

planes to identify potential malfunction

and safety issues

Conclusion

• We are living in data intensive digital world

• Data can be obtained from all sources

• Big data is here to stay with significant uptakes in companies

• Enormous potential and growth expected

Contact: khpoo@mmu.edu.my