Guest lecture on Advanced Analytics Institute Program @ UTS May 2018

1. Data Science Innovations
#Thinkingdifferentlyaboutdata
4 May 2018
Suresh Sood, PhD
@soody,
linkedin.com/in/sureshsood
suresh.sood@uts.edu.au

2. Vignettes in the two-step arrival of the internet
of things and its reshaping of marketing
management’s service-dominant logic
Woodside & Sood
Journal of Marketing
Management Volume 33, 2017 -
Issue 1-2: The Internet of Things
(IoT) and Marketing: The State of
Play, Future Trends and the
Implications for Marketing

4. Areas for Conversation
Algorithms or data ?
Democratisation of big data
Democratisation of data science
Systems of Insight

5. Data Science Innovation
#Thinkingdifferentlyaboutdata
Data science innovation is something
an organization has not done before or
even something nobody anywhere has
done before. A data science innovation
focuses on discovering and using new
or untraditional data sources to solve
new problems.
Adapted from:
Franks, B. (2012) Taming the Big Data
Tidal Wave, p. 255, John Wiley & Son
Data Science Algorithms
Companies are reimagining Business
Processes with Algorithms and there
is “evidence of significant, even
exponential, business gains in
customer’s customer engagement,
cost & revenue performance”
Wilson, H., Alter A. and Shukla, P.
(2016), Companies Are Reimagining
Business Processes with Algorithms,
Harvard Business Review, February

6. Variety of Data Types & Big Data Challenge
1.Astronomical
2.Documents
3.Earthquake
4.Email
5.Environmental sensors
6.Fingerprints
7.Health (personal) Images
8.Graph data (social network)
9.Location
10.Marine
11.Particle accelerator
12.Satellite
13.Scanned survey data
14.Sound
15.Text
16.Transactions
17.Video Big Data consists of extensive datasets primarily in the characteristics
of volume, variety, velocity, and/or variability that require a scalable
architecture for efficient storage, manipulation, and analysis.
. Computational portability is the movement of the computation to the location of the data.

7. Drones
&
Mobile
Robots

8. The ANZ Heavy Traffic Index comprises
flows of vehicles weighing more than 3.5
tonnes (primarily trucks) on 11 selected
roads around NZ. It is contemporaneous
with GDP growth.
The ANZ Light Traffic Index is made up of
light or total traffic flows (primarily cars and
vans) on 10 selected roads around the
country. It gives a six month lead on GDP
growth in normal circumstances (but
cannot predict sudden adverse events such
as the Global Financial Crisis).
http://www.a http://www.anz.co.nz/about-us/economic-markets-research/truckometer/
ANZ TRUCKOMETER

11. Oil reserves shipment monitoring
Ras Tanura Najmah compound, Saudi Arabia
Source: http://www.skyboximaging.com/blog/monitoring-oil-reserves-from-space

12. https://nodexl.codeplex.com/

13. 13
Sherman and Young (2016), When Financial Reporting Still Falls
Short, Harvard Business Review, July-August
Sood (2015), Truth, Lies and Brand Trust The Deceit
Algorithm,
http://datafication.com.au/
New Analytical Tools Can Help

14. 14
Deception Algorithm
(1) Self words e.g. “I” and “me” – decrease when someone
distances themselves from content
(2) Exclusive words e.g. “but” and “or” decrease with fabricated
content owing to complexity of maintaining deception
(3) Negative emotion words e.g. “hate” increase in word usage
owing to shame or guilty feeling
(4) Motion verbs e.g. “go” or “move” increase as exclusive words
go down to keep the story on track

15. Language on Twitter Tracks Rates of Coronary Heart
Disease, Psychological Science, January 2015
15
The findings show that expressions of negative emotions such as anger, stress, and fatigue in the tweets from people in a given
county were associated with higher heart disease risk in that county.
On the other hand, expressions of positive emotions like excitement and optimism were associated with lower risk.
The results suggest that using Twitter as a window into a community’s collective mental state may provide a
useful tool in epidemiology…So predictions from Twitter can actually be more accurate than using a set of
traditional variables.

16. Gartner (2017)
Strategic Predictions for 2017 and Beyond, research note
14 October, http://www.gartner.com/document/3471568
 By 2020-22 :
 100 million consumers shop in augmented reality
 30% of web browsing sessions without a screen
 Algorithms positively alter behavior of over 1B
 Blockchain-based business worth $10B
 IoT will save consumers/businesses $1T a year
 40% of employees cut healthcare costs via fitness tracker
Smart Data Discovery Will Enable New Class of Citizen Data Scientist
“With the addition of NLG [Natural Language Generation], smart data discovery platforms automatically
present a written or spoken context-based narrative of findings in the data that, alongside the visualization,
inform the user about what is most important for them to act on in the data.”
Gartner, 29 June, 2015
2017 Hype Cycle for Data Science and Machine Learning,
29 July, http://www.gartner.com/document/3772081

17. Systems of Insight
 Automated pattern extraction
 Outlier detection
 Correlation
 Time series
 Analytics integration with process, app or IoT
https://ubereats.com/melbourne/

18. 18© 2017 FORRESTER. REPRODUCTION PROHIBITED.
Forrester Research, 2016

19. Reports
&
Analysis
Visualisation
&
Interpretation
Write
Data/Business
“Story”
Insights
Led by Data Analyst or
Scientist
SME owner or Corporate , Machine Learning and Natural Language Generation
Fusion of data science, business knowledge & creativity for maximium ROI
Data
Aggregation Operationalise
Detect &
Extract
Patterns and
Relationships
Generate
Insights &
Story
Process
Application
IoT
Data
Aggregation
or
Data Set
Traditional Analytics: Slow & Expensive
80% of time sifting through data
System of Insight (SoI)
SoI: Fast & Cost Effective
80% of time in decision making with client

20. 20
outlier-detection “allow detecting a significant fraction
of fraudulent cases…different in nature from historical
fraud…resulting in a novel fraud pattern”
Baesens, B., Vlasselaer, V., and Verbeke, W., 2015, Fraud Analytics Using Descriptive, Predictive,
and Social Network Techniques: A Guide to Data Science for Fraud Detection, Wiley

21. Systems of Insight
• Helps move away from “crisis levels” in talent
• Traditional 5 step analytics process reduced to 2 step from data to action
• Reimagine business processes through “machine engineering”
• Minimise messy data issues and data preparation time

22. Better customer experiences . . .
. . . and half the inventory-carrying costs
of other online fashion retailers.
Forrester, 2016

23. Next Step
Start using Data Science Innovations
Systems of Insight and innovative data sources
Natural Language Generation

24. 24
The future is impossible to predict.
However one thing is certain :
The company that can excite it’s customers dreams
Is out ahead in the race to business success
Selling Dreams, Gian Luigi Longinotti