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Data Analytics and Artificial Intelligence in the era of Digital Transformation

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As companies begin their digital transformation journey, data analytics and artificial intelligence can play a key role in it being a success.

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Data Analytics and Artificial Intelligence in the era of Digital Transformation

  1. 1. Data Analytics and Artificial Intelligence in the era of Digital Transformation
  2. 2. WE ARE LIVING IN A DIFFERENT ERA Web First à Mobile First à AI First
  3. 3. Google TPU
  4. 4. AI history à Perceptron 1958 F. Rosenblatt, “Perceptron” model, neuronal networks 1943 W. McCulloch, W. Pitts, “Neuron” as logical element OR function XOR function 1969 M. Minsky, S. Papert, triggers first AI winter feed forward
  5. 5. AI history à AI winter 1958 F. Rosenblatt, Perzeptron model, neuronal networks 1987-1993 the second AI winter, desktop computer, LISP machines expensive 1943 W. McCulloch, W. Pitts, neuron as logical element 1980 Boom expert systems, Q&A using logical rules, Prolog 1969 M. Minsky, S. Papert, trigger first AI winter 1993-2001 Moore’s law, Deep blue chess- playing, Standford DARPA challenge
  6. 6. AI history Accuracy Scale (data size, model size) other approaches neural networks 1990s https://www.scribd.com/document/355752799/Jeff-Dean-s-Lecture-for-YC-AI
  7. 7. More Data + Bigger Models + More Computation Accuracy Scale (data size, model size) other approaches neural networks Now https://www.scribd.com/document/355752799/Jeff-Dean-s-Lecture-for-YC-AI more compute
  8. 8. Human Intelligence Artificial Intelligence Average Intelligent Sub human Par human High human Super humanPerformance AI comparison with human performance Borderline performs better than all humans
  9. 9. 10 AI beats human in games - 2016 Komodo beasts H. Nakamura in 2016AlphaGo beats L. Sedols in 2016 Go 4:1 Chess 2:1
  10. 10. Image Classification- 2016 Human Performance AI Performance https://arxiv.org/pdf/1602.07261.pdf 95% 97% The ability to understand the content of an image by using machine learning
  11. 11. Breast Cancer Diagnoses - 2017 Pathologist Performance AI Performance https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html 73% 92% Doctors often use additional tests to find or diagnose breast cancer The pathologist ended up spending 30 hours on this task on 130 slides A closeup of a lymph node biopsy.
  12. 12. Face Recognition - 2016 Human Performance AI Performance https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf https://arxiv.org/pdf/1603.01249v2.pdf 97,5% 97,7% The ability of a computer to scan, store, and recognize human faces for use in identifying people
  13. 13. Speech recognition - 2016 Methodologies and technologies that enables the recognition and translation of spoken language into text by computers https://arxiv.org/pdf/1610.05256v1.pdf Human Performance AI Performance 41,3% 57,9%
  14. 14. Traditional Programming Machine Learning
  15. 15. 16 Machine Learning Problem Types
  16. 16. More Data + Bigger Models + More Computation = Better Results in Machine Learning
  17. 17. Millions of “trip” events each day globally 400+ billion viewing- related events per day Five billion data points for Price Tip feature Movie recommendation Price optimization Routing and price optimization
  18. 18. Rethink the Status quo and embrace Data, AI and Technology
  19. 19. Who are you? Who do you know? What can you afford? Where are you? What have you purchased? What do you like? What content do you prefer? Why have you contacted us?
  20. 20. Marketing Tools Touchpoints Aftersales Capture Data across Digital Channels Each of these customer interactions produces data
  21. 21. What are the key challenges? q Data silos – Data spread across a number of silos q Data volumes / growth – High rate of data growth q New / unstructured data sources q Cost of data storage & processing
  22. 22. Breaking Down Data Silos Connect all your data tools, other sources, and gain a 360 degree view on your data Get actionable insights and serve them personal, relevant content along their journey Real-time processing and decision making One Data Platform Marketing Tools Touchpoints Historical Aftersales Data Analytics Machine Learning Data Apps
  23. 23. Fishing in the sea versus fishing in the lake Data Warehouse Data Lake Business Intellingence helps find answers to questions you know. Data Science helps you find the question itself. Any kind of data & schema-on-readStructured data & schema-on-write Parallel processing on big dataSQL-ish queries on database tables Extract, Transform, Load Extract, Load, Transform-on-the-fly Low cost on commodity hardwareExpensive for large data
  24. 24. thank youData Analytics Speed Data Complexity Analytics Complexity Data Size Accuracy / Precision
  25. 25. Where Analytics can help… + Predicting lifetime value + Churn estimation + Customer segmentation + Cross/Upselling + Recommendations + Demand forecasting + Market Basket Analysis + Sentiment analysis + Loyalty programs + Reactivation likelihood + Discount targeting + Call to action + Risk analysis + In store traffic patterns
  26. 26. Actionable data insights that businesses can use to... ü Better understand and better engage your customers ü Respond to the convergence of customer expectations ü Driver of brand perception 360 degree view of your customers Social Apps CRM Billing Channels Service Call Location Devices Network Ordering Customer 360
  27. 27. GDPR General Data Protection Regulation
  28. 28. GDPR General Data Protection Regulation + Regulation on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) + 25 MAY 2018: ALL THE CONCERNED COMPANIES MUST BE COMPLIANT 29 GDPR (General Data Protection Regulation) also knows about "sensitive personal data" which is defined as information revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade-union membership, and data concerning health or sex life. This information is supplied without liability and without any claim to comprehensiveness.
  29. 29. Processing any operation or set of operations which is performed on personal data or on sets of personal data, whether or not by automated means, such as collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction Personal Data Any information relating to an identified or identifiable natural person (name, identification number, location data, online identifier, one or more factors specific to physical, physiological, genetic, mental, economic, cultural or social identity of that natural person (Data Subject) It includes IP address; PD that has been pseudonymised – eg key-coded- can fall within the scope of the GDPR depending on how difficult it is to attribute the pseudonym to a particular individual Sensitive personal data Racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, genetic data, biometric data processed to uniquely identify an individual, data concerning health, or data concerning a natural person’s sex life or sexual orientation cannot be processed except if expressly authorized. Material Scope 30This information is supplied without liability and without any claim to comprehensiveness.
  30. 30. Examples of Personal Data + Name + Home Address + Photo + Date/Place of Birth + Age, Gender + Race/Ethnic origin + EMail Addresses + Phone numbers + Any form of ID-numbers assigned to individuals + Passport Number & National Identification Card Number 31 + Credit Card Number + Health Status + Criminal Record + (Vehicle) locations and history thereof + CCTV and video recordings + IP Addresses, Mobile Device IDs, MAC Addresses + Genetic data, Health status (including pregnancy), Biometric data + Religion / Philosophical beliefs + Sexual preferences This information is supplied without liability and without any claim to comprehensiveness.
  31. 31. How to start?
  32. 32. “Culture eats strategy for breakfast, technology for lunch, and products for dinner, and soon thereafter everything else too.” Peter Drucker
  33. 33. + Classification, Regression, Clustering, Collaborative Filtering, Anomaly Detection + Supervised/Unsupervised Reinforcement Learning, Deep Learning, CNN + Model Training, Evaluation, Testing, Simulation, Inference + Big Data Strategy, Consulting, Data Lab, Data Science as a Service + Data Collection, Cleaning, Analyzing, Modeling, Validation, Visualization + Business Case Validation, Prototyping, MVPs, Dashboards Data Science Machine Learning
  34. 34. + Architecture, DevOps, Cloud Building + App. Management Hadoop Ecosystem + Managed Infrastructure Services + Compute, Network, Storage, Firewall, Loadbalancer, DDoS, Protection + Continuous Integration and Deployment + Data Pipelines (Acquisition, Ingestion, Analytics, Visualization) + Distributed Data Architectures + Data Processing Backend + Hadoop Ecosystem + Test Automation and Testing Data Engineering Data Operations
  35. 35. Think Big Business Strategy Data Strategy Technology Strategy Agile Delivery Model Business Case Validation Prototypes, MVPs Data Exploration Data AcquisitionStart Small Value Proposition
  36. 36. thank you

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