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--- week06 [2021/07/19 06:55] – suhaw
+++ week06 [2021/07/22 06:29] (current) – [Ghorra Paul] suhaw
@@ Line 2: / Line 2: @@
+===== Thursday 22 July 2021 =====
+==== elearning - Introduction to Business Analytics ====
+==== John David Ariansen and Madecraft ====
+Data sources:
+  * Internal
+    * Sales
+      * Breakdown to: demographics, geographic, time, product category, average sale
+    * Cost
+      * Fixed
+      * Variable
+    * Marketing
+    * Psychometric
+  * External
+    * Market Reports
+      * Market players
+      * Size of market
+      * High-level data points
+    * Market Research
+      * Behavourial patterns
+      * Buyer personas
+Data governance: ensure quality of data assets
+Causes of data quality issues:
+  * Data collection: Manual, lack of index field
+  * System integration (different data format across systems)
+  * Missing data
+Tools/Applications:
+    * Sales: Square, Stripe, Clover
+    * Cost: Quickbooks
+    * Marketing: Google Analytics, Salesforce
+    * Psychometric: Google Forms, Qualtrics, Social Media data, focus groups
+Data Management tools:
+  * MS Excel
+  * Tableau
+  * PowerBI
+  * MS SQL Server
+==== Ghorra Paul ====
+BCG Report "Putting AI to Work", Sept 2017 https://www.bcg.com/publications/2017/technology-digital-strategy-putting-artificial-intelligence-work
+Shipping container routing: https://youtu.be/QQmZiQfeVNw
+Key takeaways:
+  * Gathering relevant data and preparing it for analysis is often the longest step of analysis
+  * Make it visible - Just visualising existing data can already unlock tremendous value
+  * Simulation: A digital twin can expedite learning with risk free experimentation
+  * ML: Input data determines strength of the algorithm - it cannot predict outcomes it hasn't seen
+  * 3 step framework - Decision variables, Objective, Constraint, help to frame optimisation problem
+===== Wednesday 21 July 2021 =====
+==== e-learning - Digital Enablers - AI, IoT, Cybersecurity, DDP, Cloud ====
+=== AI ===
+  * Symbolic Reasoning/Planned AI
+    * Abstract problems, but know steps
+  * Machine Learning
+    * Look for patterns
+  * Artificial neural networks
+== Approaches ==
+  * Match patterns
+  * Data vs reasoning
+  * Unsupervised learning
+    * Machines creates categories based on similarities it detects, may be different from human categories
+  * Deep learning
+  * Clustering
+  * Backpropagation
+    * Allows later feedback to go back and adjust earlier rules/algorithms, error corrections
+  * Regression
+=== IoT ===
+== Business View ==
+  * Software-defined product
+  * Hardware-defined product
+  * Network fabric
+  * External system interface
+== Software-defined product ==
+  * Cyber Model
+    * digital simulation of the product's functionalities
+    * represented by statistics
+  * Application
+    * Functionality
+    * Human interface
+    * Data interface
+  * IoT value modeling
+== Hardware-defined product ==
+  * Connected sensors and actuators
+    * sensor gathers data
+      * transducer
+    * actuators makes physical changes
+  * Embedded system
+    * gathers data
+    * packages data
+    * sends data (over the OT Network) to application
+    * may also process application, analytics, security
+  * Mist computing vs cloud computing
+== Network fabric ==
+  * OT (Operational technology) network
+  * IT network
+  * Backhaul
+  * Internet (public cloud)
+  * Product cloud (private)
+  * IoT protocol
+    * Payload: data from sensors
+    * Application protocol: provides info context to payload
+    * Network protocol: data info to move it along its path
+    * Media protocol: allows it to be sent over the air
+== External system interface ==
+  * Analytics
+  * Data services
+  * Business systems
+  * Other IoT products
+=== Cybersecurity ===
+  * Many different types of cybersecurity breaches
+    * Phishing
+    * Malware
+    * DDoS
+    * Brute force attack
+    * Physical breach
+  * Financial and reputation harm
+  * Cybersecurity spending growths 10x slower than number of attacks
+  * Getting the people part is critical
+    * People account for 72% of breaches
+=== DDP: Data & Digital Platform ===
+  * Company's Vision
+  * Identify concrete and profitable use cases to fund the long-term and incremental transformation
+  * Build data driven organisation to enable use cases
+  * NG architecture to migrate legacy business logic and data incrementally onto new digital and data platform
+  * Master key technologies such as datalake, fueled by legacy and new data sources
+  * Governance, Organisation, Training
+=== Cloud ===
+Shift to the cloud driven by changes in technology economics
+  * Changes are here to stay
+Different possibilities to leverage cloud services: choose wisely
+  * Infrastructure
+  * Platform
+  * Software
+  * Business Processes
+Potential to reduce TCO
+  * Don't forget Maintenance, upgrades, re-migrations
+Hyperscalability for the future
+  * Reduces upfront costs for uncertain initiatives
+  * Allows scalability for wildly successful ones
+  * Can scale back if the fad has passed
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   * Data
+  * Artificial Intelligence: handle uncertainty
+  * Internet of Things (IoT)
+  * Blockchain
+    * https://youtu.be/xfXzvoW_rcI
+  * Data and Digital platform
+    * Data Lakes is next-gen Data Warehouses
+    * (frm Google) A data lake is a vast pool of raw data, the purpose for which is not yet defined. A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose.
+  * Cloud Computing
+  * Cybersecurity
-=== Digital Transformation ===
+=== Digital Transformation: The Bionic Company ===
+Strategy and Purpose
+  * Purpose
+    * Human potential
+    * Organisation
+    * Ecosystem
+    * Societal
+  * Customer centric
+Outcomes
+  * Customer
+  * Operational
+  * New offers/innovations
+Human
+Technology: Data and Digital Platform
+BCG-KLM Partnership: https://youtu.be/Ad31TwLJCPA
@@ Line 42: / Line 265: @@
   * Identify outgrowths
   * Refresh plans frequently