This document discusses several topics related to data science innovations including: 1. The democratization of data science and big data through more accessible tools and platforms. 2. How companies are using algorithms to reimagine business processes and gain customer insights. 3. Emerging trends in natural language processing, systems of insight, and using alternative data sources. 4. The potential for natural language generation to automatically present narratives and insights from data. 5. How these techniques can help organizations move from traditional analytics to more cost effective systems of insight.

1. Data Science Innovations
#bigdata&artificialintelligence, #thinkdifferentlyaboutdata
6 June 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
Democratisation of data science (AI & tools)
Democratisation of big data
Gartner & Forrester Trends
 Natural Language Generation
 Natural Language Processing
 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 & Music
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.

8. • The data collected in a single day take nearly two million years to playback on an MP3 player
• Generates enough raw data to fill 15 million 64GB iPods every day
• The central computer has processing power of about one hundred million PCs
• Uses enough optical fiber linking up all the radio telescopes to wrap twice around the Earth
• The dishes when fully operational will produce 10 times the global internet traffic as of 2013
• The supercomputer will perform 1018 operations per second - equivalent to the number of stars in
three million Milky Way galaxies - in order to process all the data produced.
• Sensitivity to detect an airport radar on a planet 50 light years away.
• Thousands of antennas with a combined collecting area of 1,000,000 square meters - 1 sqkm)
• Previous mapping of Centaurus A galaxy took a team 12,000 hours of observations and several
years - SKA ETA 5 minutes !
To the scientists involved, however, the SKA is no testbed, it’s a transformative instrument which,
according to Luijten, will lead to “fundamental discoveries of how life and planets and matter all came
into existence. As a scientist, this is a once in a lifetime opportunity.”
Sources: http://bit.ly/amazin-facts & http://bit.ly/astro-ska
Galileo
Square Kilometer Array Construction
(SKA1 - 2018-23; SKA2 - 2023-30)
Centaurus A

9. The following BigQuery query (note that the wildcard on "TAX_WEAPONS_SUICIDE_" catches suicide vests, suicide bombers, suicide bombings, suicide
jackets, and so on):
SELECT DATE, DocumentIdentifier, SourceCommonName, V2Themes, V2Locations, V2Tone, SharingImage, TranslationInfo FROM [gdeltv2.gkg] where
(V2Themes like '%TAX_TERROR_GROUP_ISLAMIC_STATE%' or V2Themes like '%TAX_TERROR_GROUP_ISIL%' or V2Themes like
'%TAX_TERROR_GROUP_ISIS%' or V2Themes like '%TAX_TERROR_GROUP_DAASH%') and (V2Themes like '%TERROR%TERROR%' or V2Themes like
'%SUICIDE_ATTACK%' or V2Themes like '%TAX_WEAPONS_SUICIDE_%')
The GDELT Project pushes the boundaries of “big data,” weighing in at over a quarter-billion rows with 59 fields for each record,
spanning the geography of the entire planet, and covering a time horizon of more than 35 years. The GDELT Project is the largest
open-access database on human society in existence. Its archives contain nearly 400M latitude/longitude geographic coordinates
spanning over 12,900 days, making it one of the largest open-access spatio-temporal datasets as well.
GDELT + BigQuery = Query The Planet

10. 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

13. The
“Massive"
Skills Gap
US data only & includes job title of Marketing Manager
Source: Investing in America’s data science and analytics talent, PWC, April 2017
“There is a MASSIVE shortage of marketers that are skilled in the art of
data analysis…number of marketers with analytics skills is DECREASING
as the job levels increase toward CxO. This discrepancy between the
demand and supply is the most in all of the experiment, at over 10x for
every level…Google Analytics has 1.5x more demand than supply (only
7% of marketers have it)
Source: Marketing Skills 2017: Are You Qualified to Be Hired? [Updated], Ryan
Mccready, Sept 08, 2016, last viewed 18 November 2017
<https://venngage.com/blog/marketing-skills-2016/>

14. 14© 2017 FORRESTER. REPRODUCTION PROHIBITED.
Source: Forrester Research eCommerce Trends And Outlook For Asia Pacific Trends In Australia, China, India, Japan, South Korea From The Forrester Data: Online Retail
Forecast, 2016 To 2021 (Asia Pacific) June 28, 2017
Asia Pacific contains both the largest and the fastest-growing
eCommerce markets (China and India, respectively). Today,
total online retail revenues in just five markets in Asia Pacific
— China, Japan, South Korea, India, and Australia — surpass
the combined figure for online retail in the US and all of
Western Europe.
In these markets, total online retail revenues will jump from
$862 billion in 2016 to $1.4 trillion in 2021.
E-Commerce Outlook 2016-2021

15. Online tenure leads to more spending per customer
High engagement leads to more orders, more
categories purchased, and more spend
https://www.quillengage.com

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

17. https://nodexl.codeplex.com/

18. 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

19. 19
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
I. Natural Language Processing Leads to New Areas of Discovery

20. http://www.analyzewords.com
20

21. (Berger and Packard 2018)
Are Atypical Things More Popular?
Psychological Science
Every business would love to know the minds of its
competitors, and what they are likely to do next.
Strategy analysts have thus far used simple tools…But
new research at Wharton has shown how natural
language processing techniques could be used to
parse tomes of unstructured data such as text buried in
conference calls or annual reports to more accurately
anticipate competitor strategies. The research opens
new pathways to measure and test assumptions firms
make in their competitive strategies, and to “visualize
how firms are positioned with respect to each other,
and then map that on to performance consequences
(Menon and Choi 2018)
“What You Say Your Strategy Is and Why It Matters: Natural
Language Processing of Unstructured Texts,”
II. Natural Language Processing Leads to New Areas of Discovery
Music Strategy

22. Language on Twitter Tracks Rates of Coronary Heart
Disease, Psychological Science, January 2015
22
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.

23. 2017 Hype Cycle for Data Science and Machine Learning,
29 July, http://www.gartner.com/document/3772081
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 ( Gartner 2015)
“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.”

24. Systems of Insight (Forrester 2015)
 Automated pattern extraction
 Outlier detection
 Correlation
 Time series
 Analytics integration with process, app or IoT

25. 25
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

26. 26© 2017 FORRESTER. REPRODUCTION PROHIBITED.
Forrester Research, 2016

27. 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

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

31. Data Science Resources
University of Helsinki :
Online AI Course
https://www.elementsofai.com/
http://brookfieldinstitute.ca/wp-
content/uploads/2016/06/Talented
MrRobot.pdf
https://industry.gov.au/Innovation-and-
Science-Australia/Documents/Australia-2030-
Prosperity-through-Innovation-Full-Report.pdf

32. Deep Learning Libraries, Platforms, APIs and Hardware

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

34. 34
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