data science innovations : democratisation of data and data science
TRANSCRIPT
Areas for Conversation
Data Science
Data Science Innovation
Democratisation of big data
Gartner & Forrester Trends
Systems of Insight
BEST USE OF CLEVER DATA
The Works with Suresh Sood, Advanced Analytics
Institute, UTS & Professor James Pennebaker from
the University of Texas - The Deceit Algorithm
BEST INSIGHT The Works with Suresh Sood,
Advanced Analytics Institute, UTS & Professor
James Pennebaker from the University of Texas -
The Deceit Algorithm
www.datafication.com.au
Vignettes in the two-step arrival of the internet of
things and its reshaping of marketing management’s
service-dominant logicWoodside & 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
Statistics, Data Mining or Data Science ?
• Statistics– precise deterministic causal analysis over precisely collected data
• Data Mining– deterministic causal analysis over re-purposed data carefully sampled
• Data Science– trending/correlation analysis over existing data using bulk of population i.e. big data
– Extraction of actionable knowledge directly from data through a process of discovery, hypothesis, and hypothesis testing.
Adapted from: NIST Big Data taxonomy draft report :
(see http://bigdatawg.nist.gov /show_InputDoc.php)
Useful References Big Data • NIST Big Data interoperability Framework (NBDIF) V1.0 Final Version (September 2015)
Big Data Definitions: http://dx.doi.org/10.6028/NIST.SP.1500-1
Big Data Taxonomies: http://dx.doi.org/10.6028/NIST.SP.1500-2
Big Data Use Cases and Requirements: http://dx.doi.org/10.6028/NIST.SP.1500-3
Big Data Security and Privacy: http://dx.doi.org/10.6028/NIST.SP.1500-4
Big Data Architecture White Paper Survey: http://dx.doi.org/10.6028/NIST.SP.1500-5
Big Data Reference Architecture: http://dx.doi.org/10.6028/NIST.SP.1500-6
Big Data Standards Roadmap: http://dx.doi.org/10.6028/NIST.SP.1500-7
• Apache Spark 2.1.0 Documentation
Machine Learning Library (MLlib) Guide http://spark.apache.org/docs/latest/ml-guide.html
GraphX Programming Guide http://spark.apache.org/docs/latest/graphx-programming-guide.html
SparkR (R on Spark) http://spark.apache.org/docs/latest/sparkr.html#sparkdataframe
Spark SQL, DataFrames and Datasets Guide http://spark.apache.org/docs/latest/sql-programming-guide.html
Data Science Innovation
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
Variety of Data Types & Big Data Challenge 1. Astronomical 2. Documents 3. Earthquake4. Email5. Environmental sensors 6. Fingerprints7. Health (personal) Images8. Graph data (social network)9. Location10.Marine11.Particle accelerator 12.Satellite13.Scanned survey data 14.Sound15.Text16.Transactions17.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.
Internet of Things “trillion sensors”
Source: www.tsensorssummit.org
• 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
New Sources of Information (Big data) : Social Media + Internet of Things Innovations
7,919 40,204
2,003,254,102 51
Gridded Data Sources
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
Oil reserves shipment monitoring
Ras Tanura Najmah compound, Saudi Arabia
Source: http://www.skyboximaging.com/blog/monitoring-oil-reserves-from-space
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https://nodexl.codeplex.com/
Key Network Measures
• Degree Centrality • Betweenness Centrality• Closeness Centrality• Eigenvector Centrality
krackkite.##h (modified labels)
Connector(hub)
Diana’sClique
Broker
Boundary spanners
Contractor ? Vendor
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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
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The Newman Model of Deception (Pennebaker et al)Key word categories for deception mapping:
(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
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Language on Twitter Tracks Rates of Coronary Heart Disease, Psychological Science, January 2015
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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.
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/
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http://www.analyzewords.com
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
SStrategic Predictions for 2017 and Beyond, research note 14 October, http://www.gartner.com/document/3471568
2016 Hype Cycle for Business Intelligence and Analytics,
29 July, http://www.gartner.com/document/3388326
Gartner (2016)
“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
Smart Data Discovery Will Enable
New Class of Citizen Data Scientist
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Insights-driven businesses will generate $1.2 trillion in
2020
Forrester Research, 2016
27© 2016 Forrester Research, Inc. Reproduction Prohibited
Insights-driven businesses are faster than large companies
2015 2016 2017 2018 2019 2020$0
$250
$500
$750
$1,000
$1,250 Revenue (billions)
PublicStartup
Global GDP will grow only 3.5% annually.
27% CAGR
40% CAGR
Source: Forrester, Morningstar, PitchBook, and The Economist Intelligence Unit
Reports&
Analysis
Visualisation&
Interpretation
WriteData/Business
“Story” Insights
Led by Data Analyst or Scientist
SME owner, Machine Learning and Natural Language GenerationFusion of data science, business knowledge & creativity for
maximium ROI
Data Aggregation Operationalise
Detect & Extract
Patterns andRelationship
s
Generate Insights &
Story
ProcessApplication
IoT Data
Aggregation or
Data Set
Traditional Analytics: Slow & Expensive80% of time sifting through data
System of Insight (SoI)
SoI: Fast & Cost Effective80% of time in decision making with client
Actionable Insights
1. What now ?
2. So what ?
3. Now what ?
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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
Better customer experiences . . .. . . and half the inventory-
carrying costs of other online fashion retailers.
Forrester, 2016
Systems of Insight
Automated pattern extraction
Outlier detection
Correlation
Time series
Analytics integration with process, app or IoT
https://ubereats.com/melbourne/
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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
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
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
Next Step
Start using Systems of Insight and innovative data sources
Data Science Resources
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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