Guest lecture for Western Sydney University Students MBA 200852 Innovation, Creativity and Foresight unit

1. Guest Lecture
@soody
or
linkedin.com/in/sureshsood
24 August 2014
“Wake me up tomorrow has already happened !”
Big Data, Analytics, IOT and Foresight

2. Topic Areas
1. Quick History of Foresight
2. Social media and predictive capabilities
3. New and innovative information sources
4. Internet of Things
5. Systems of Insight

3. 100 Years of Foresight Writing

4. PESTEL
Analysis
Narrative &
Story
Scenarios
Trend Spotting/
Ethnography
Forecasting
Econometrics
Historical
Analysis
Patent Analysis &
Tracking

5. Global Strategic Trends out to 2045
UK Ministry of Defence, 15 July 2014
www.bit.ly/Global2045
https://www.gov.uk/government/publications/global-strategic-trends-out-to-2045

7. Twitter and Marketing Predictions
• Tweets is “found data” without asking questions
• More meaning than typical search engine query
•
• Large numbers of passive participants in natural settings
• Twitter can predict the stock market (Lisa Grossman, Wired, Oct 19 2010)
• Predict movie success in first few weekends of release
– “…it also raises an interesting new question for advertisers and marketing
executives. Can they change the demand for their film, product or service buy
directly influencing the rate at which people tweet about it? In other words,
can they change the future that tweeters predict?”
Tech Review, http://www.technologyreview.com/blog/arxiv/25000/
7

8. Detecting flu trends using search engine query data (intentionality)
8

9. https://www.recordedfuture.com/

10. Web is Loaded with Events
Silicon Valley executives head to
Vail, Colo. next week for the
annual Pacific Crest Technology
Leadership Forum
The carrier may select partners to set up
a new carrier as early as next month
“2010 is the year when Iran will kick out
Islam. Ya Ahura we will.”
“... Dr Sarkar says the new facility will
be operational by March 2014...”
Drought and malnutrition hinder next year’s
development plans in Yemen...
“...opposition organizers
plan to meet on Thursday
to protest...”
“Excited to see Mubarak
speak this weekend...”
“According to TechCrunch
China’s new 4G network will
be deployed by mid-2010”
“Strange new Russian
worm set to unleash
botnet on 4/1/2012...”
https://www.recordedfuture.com/

11. From Keywords to Timelines
Timeline
the
World/Web
“Record what the world knows about the future”
https://www.recordedfuture.com/

12. Recorded Future Architecture
70,000 Real-time Sources
3+ Billion Time-tagged Facts
100,000 future events/day
https://www.recordedfuture.com/

13. Data Types
• Astronomical
• Documents
• Earthquake
• Email
• Environmental sensors
• Fingerprints
• Health (personal) Images
• Location
• Marine
• Particle accelerator Satellite
• Scanned survey data Social media
• Sound
• Text
• Transactions
• Video

14. New Sources of Information (Big data) : Social Media + Internet of Things  Innovations
7,919 40,204
2,003,254,102 51
Gridded Data Sources

15. 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
http://www.anz.co.nz/commercial-institutional/economic-markets-research/truckometer/

16. Useful References Informing our Thinking
on Mobility and Movement
(Silva et al (2013) A comparison of Foursquare and Instagram to the study of city
dynamics and urban social behavior, Proceedings of the 2nd ACM SIGKDD
International Workshop on Urban Computing
Instagram and Foursquare datasets might be compatible in finding popular regions of
city
Chaoming Song, et al. (2010), Limits of Predictability in Human Mobility, Science
There is a potential 93% average predictability in user mobility, an exceptionally high
value rooted in the inherent regularity of human behavior. Yet it is not the 93%
predictability that we find the most surprising. Rather, it is the lack of variability in
predictability across the population.
Scellato et al. (2011), NextPlace: A Spatio-temporal Prediction Framework for
Pervasive Systems. Proceedings of the 9th International Conference on Pervasive
Computing (Pervasive'11)
Daily and weekly routines => Few significant places every day => Regularity in human
activities => Regularity leads to predictability

17. Domenico, A. Lima, Musolesi.M. (2012) Interdependence and Predictability of Human
Mobility and Social Interactions. Proceedings of the Nokia Mobile Data Challenge
Workshop.
we have shown that it is possible to exploit the correlation between movement data and
social interactions in order to improve the accuracy of forecasting of the future geographic
position of a user. In particular, mobility correlation, measured by means of mutual
information, and the presence of social ties can be used to improve movement forecasting
by exploiting mobility data of friends. Moreover, this correlation can be used as indicator of
potential existence of physical or distant social interactions and vice versa.
Sadilek, A and Krumm, J. (2012) Far Out: Predicting Long-Term Human Mobility
Where are you going to be 285 days from now at 2pm …we show that it is possible to
predict location of a wide variety of hundreds of subjects even years into the future and
with high accuracy.
Useful References Informing our Thinking
on Mobility and Movement

18. 18
Insights-driven businesses will
generate $1.2 trillion in 2020
Forrester Research, 2016

19. Reports
&
Analysis
Visualisation
&
Interpretation
Write
Data/Business
“Story”
Insights
Led by Data Analyst or Scientist
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. Demonstration of Natural
Language Generation of
Stories from data as well
as automated pattern
extraction from data

21. Actionable Insights
1. What now ?
2. So what ?
3. Now what ?

22. 22
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, https://hbr.org/2016/02/companies-are-reimagining-
business-processes-with-algorithms

23. In the News

24. Augmentation Steps for Advisors Using CA Kairos
(adapted from Davenport, 2016)

25. 25

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

27. Systems of Insight
• Automated pattern extraction
• Outlier detection
• Correlation
• Time series
• Analytics integration with process, app or IoT
https://ubereats.com/melbourne/
Forrester 2016

28. 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

29. 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
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30. Coca-Cola Amatil invests in data science
http://www.itnews.com.au/news/coca-cola-amatil-invests-in-data-science-431153
“CCA have a very good historical dataset - we collect
lots of data from a variety of sources including
manufacturing, trucks, fridges, and from customers…
The main target challenge … is to be more proactive.
[We] want to predict where the market wants to go and
the trend of the market in the future.”
Former CBA data scientist Siamak Tafavogh