====== Week 005 ====== ===== 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 ===== Monday 19 July 2021 ===== ==== Grantly Mailes ==== === Data and Digital Disruption === Technological Ages * Desktop: MS * Internet: Amazon, Google * Mobile * 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: 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 ==== e-Learning ==== Digital Transformation * Digitisation: Conversion of analogue or physical information to digital format * Digitalisation: Use of digital technologies to enable or improve business models and processes * Digital Transformation: Coordinated change effort at scale, diffused through all aspects of the business * Digitally Active: Transactional solutions to increase revenue * Digitally Engaged: Solutions and processes * Digitally Mature: Artificial intelligence solutions * Investigate new technologies * Work with customers * Events and focus groups * Share insights * Get suggestions * Identify modifications to existing plans * Identify outgrowths * Refresh plans frequently