Guest lecture as part of Foresight postgraduate course at Western Sydney University.

1. Getting to the Edge of the Future:
Tools & Trends of Foresight to Nowcasting
Guest Lecture
Suresh Sood, PhD
@soody or linkedin.com/in/sureshsood
4 February 2019
bit.ly/WSU4-sight

2. 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
2

3. Gas Provider Towards 2025
• Leading provider of gas
• Retail
• Industrial (B2B)
Disruptive Business Model e.g. WeWork, Uber, AirBnB, Tinder, FitBit
• New Technology
New Way of Connecting with Customers

5. Topic Areas for Conversation
1. Quick History of Foresight
2. Nowcasting, Social media and Predictive Capabilities
3. Recorded Future Architecture
4. New and innovative information sources
5. Hype Cycles
6. Creating Your Own Marketing Intelligence Capability

6. 100 Years of Foresight Writing

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

8. Global Strategic Trends out to 2050
UK Ministry of Defence, 15 October 2018
https://www.gov.uk/government/publications/global-strategic-trends

9. Global Strategic Trends – The Future Starts Today

10. Nowcasting
Nowcasting techniques refer to short term forecasts (0 12 hours)
http://www.radar.mcgill.ca/science/activities/nowcasting-menu.html

11. 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/
11

12. Detecting flu trends using search engine query data (intentionality)
12

13. https://www.recordedfuture.com/

14. 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/

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

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

17. Useful References Informing our Thinking on Mobility and Movement
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
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.

18. 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

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

20. 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/

21. Innovation Trigger
(formerly called
Technology Trigger): The
Hype Cycle starts when a
breakthrough, public
demonstration, product
launch or other event
generates press and
industry interest in a
technology innovation.
Peak of Inflated Expectations: A wave of “buzz” builds and
the expectations for this innovation rise above the current
reality of its capabilities. In some cases, an investment bubble
forms, as happened with the web and social media.
Trough of Disillusionment: Inevitably, impatience for results
begins to replace the original excitement about potential value.
Problems with performance, slower-than-expected adoption or a
failure to deliver financial returns in the time anticipated all lead to
missed expectations, and disillusionment sets in.
Slope of
Enlightenment: Some
early adopters overcome
the initial hurdles, begin
to experience benefits
and recommit efforts to
move forward.
Organizations draw on
the experience of the
early adopters. Their
understanding grows
about where and how
the innovation can be
used to good effect and,
just as importantly,
Plateau of Productivity: With the real-world benefits
of the innovation demonstrated and accepted,
growing numbers of organizations feel comfortable
with the now greatly reduced levels of risk. A sharp
rise in adoption begins (resembling a hockey stick
when shown graphically), and penetration accelerates
rapidly as a result of productive and useful value.

22. AI with Everything using Salesforce Einstein Products
Adapted from: Gartner Webinar (2018), Use AI to Create Better B2B Customer Experience, IIona Hansen, https://www.gartner.com/webinar/3891408

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
24