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

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

• Symbolic Reasoning/Planned AI
  • Abstract problems, but know steps
• Machine Learning
  • Look for patterns
• Artificial neural networks

• 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

• Software-defined product
• Hardware-defined product
• Network fabric
• External system interface

• Cyber Model
  • digital simulation of the product's functionalities
  • represented by statistics
• Application
  • Functionality
  • Human interface
  • Data interface

• IoT value modeling

• 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

• 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

• Analytics
• Data services
• Business systems
• Other IoT products

• 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

• 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

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

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

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

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