Foresight conversation
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This document discusses big data, the Internet of Things (IoT), analytics, and foresight. It focuses on natural language generation, systems of insight, and deep learning. Key points include that by 2020-2022, natural language generation will be used in smart data discovery platforms to automatically present narratives summarizing findings from data. Systems of insight will enable automated pattern extraction, outlier detection, correlation analysis, and integration of analytics with processes, applications, and IoT systems. The document provides references and examples relating to big data, data science, machine learning, and the use of algorithms.
![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
“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](https://image.slidesharecdn.com/foresightconversationv1-180220061857/85/Foresight-conversation-4-320.jpg)
![“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](https://image.slidesharecdn.com/foresightconversationv1-180220061857/85/Foresight-conversation-18-320.jpg)
Foresight conversation
- 1. Big Data, IoT, Analytics & Foresight: Natural Language Generation, Systems of Insight & Deep Learning February 2018 Suresh Sood, PhD @soody, suresh.sood@uts.edu.au linkedin.com/in/sureshsood
- 2. Areas for Conversation Foresight Data Science Innovation (s) – IoT & Big Data Democratisation of big data Gartner & Forrester Trends Natural Language Generation Systems of Insight Deep Learning
- 4. 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 “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
- 6. 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
- 8. 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)
- 9. 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 Note: Version 2 drafts available at https://bigdatawg.nist.gov/V2_output_docs.php Use Case V2.0 https://bigdatawg.nist.gov/usecases.php - Request for Public Comments (Deadline: September 21, 2017) • 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
- 10. 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 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
- 11. 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.
- 12. • 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
- 13. Oil reserves shipment monitoring Ras Tanura Najmah compound, Saudi Arabia Source: http://www.skyboximaging.com/blog/monitoring-oil-reserves-from-space
- 15. 15 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
- 16. 16 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
- 17. Language on Twitter Tracks Rates of Coronary Heart Disease, Psychological Science, January 2015 17 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.
- 18. “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
- 19. Systems of Insight Automated pattern extraction Outlier detection Correlation Time series Analytics integration with process, app or IoT https://ubereats.com/melbourne/
- 20. 20© 2017 FORRESTER. REPRODUCTION PROHIBITED. Forrester Research, 2016
- 21. 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
- 22. 22 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
- 23. Online tenure leads to more spending per customer High engagement leads to more orders, more categories purchased, and more spend https://www.quillengage.com
- 24. Better customer experiences . . . . . . and half the inventory-carrying costs of other online fashion retailers. Forrester, 2016
- 25. 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
- 26. 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
- 27. Deep Learning Libraries, Platforms, APIs and Hardware
- 28. Next Step Start using Data Science Innovations Systems of Insight and innovative data sources Natural Language Generation Deep Learning
- 30. 30 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