1. User Notes
1.1. ...notes on tools used in this session...
1.1.1. Mindmeister
1.1.1.1. Link: bit.ly/SMUBAA
1.1.1.2. Copy Map
1.1.2. Jamboards
1.1.2.1. Example
1.1.2.2. Copy Boards
2. Course
2.1. ...objectives...
2.1.1. "...Gain an overview of business applications of big data and analytics techniques..."
2.1.2. "...Gain real-world insights into various applications of big data analytics and how it can be used to fuel better decision-making within an organisation/business..."
2.1.3. "...Learn how big data and analytics techniques can create business value..."
2.1.4. "...Understand how to manage big data and analytics projects and teams..."
3. Preview
3.1. ...the core principles of big data...
3.1.1. The "What?"
3.1.1.1. ...technical perspective...
3.1.1.1.1. Classic IBM Definition
3.1.1.2. ...outcomes perspective...
3.1.1.2.1. Facebook and German Rail
3.1.2. The "Why?"
3.1.2.1. ...prediction and prescription use cases...
3.1.2.1.1. Use Case Template
3.1.3. The "How?"
3.1.3.1. Big Data Technology Landscape
3.1.3.2. Azure Data Platform
3.1.3.2.1. ...three distinctive challenges...
4. The Purpose of Big Data
4.1. ...how data creates value...
4.1.1. The Analytics Spectrum
4.1.1.1. ...data is used to "Describe", "Diagnose", "Predict", or "Prescribe"...
4.1.1.1.1. The Analytics Curve
4.1.1.2. ..."Prediction" and "Prescription" is the domain of "Machine Learning"...
4.1.1.2.1. Machine Learning Family Tree
4.1.1.2.2. Forms of Machine Learning
4.1.1.3. ..."Machine Learning" is the engine of "Artificial Intelligence"...
4.1.1.3.1. Artificial Intelligence
4.2. ...how big data creates value...
4.2.1. Principal Use Cases for Big Data
4.2.1.1. ...the majority of big data use cases can be summarised as packaging a "Predictive" or "Prescriptive" data engine...
4.2.1.1.1. Complex Pattern Insights
4.2.1.1.2. Predictive Decision Support
4.2.1.1.3. Personalisation
4.2.1.1.4. Cognitive Automations
4.3. Tasks
4.3.1. ...Task #1...
4.3.1.1. Identify an example of Machine Learning being used inside the Personetics solutions, how might it work?
4.3.1.1.1. Example: Personetics
4.3.1.1.2. ...boards...
4.3.2. ...Task #2...
4.3.2.1. Craft a "Predictive or Prescriptive" concept for an industry represented in your team - start by answering "What and Why"?
4.3.2.1.1. ...boards...
5. The Process of Big Data
5.1. ...process patterns are critical to unlocking the value of big data...
5.1.1. Principal Data Process Pattern
5.1.1.1. ...some process patterns are concerned with anchoring the data science to business needs...
5.1.1.1.1. CRISP-DM
5.1.2. Machine Learning Process Patterns
5.1.2.1. ...some process patterns are concerned with enabling the machine to learn...
5.1.2.1.1. Machine Learning Process Patterns
5.1.2.1.2. Supervised Learning Deep Dive
5.1.3. Hybrid Process Pattern
5.1.3.1. ...success requires fusions of CRISP-DM and Supervised Learning Pattern...
5.1.3.1.1. Example: Uber Michaelangelo
5.1.3.1.2. Illustration: Particle Data
5.2. Tasks
5.2.1. ...Task #1...
5.2.1.1. Example: German Rail
5.2.1.1.1. Consider how the image recognition element in this example was put together from a process perspective...
5.2.2. ...Task #2...
5.2.2.1. Develop your "Predictive or Prescriptive" concept - what is the process?
5.2.2.1.1. ...process details...
5.2.2.1.2. ...boards...
6. The Tech of Big Data
6.1. ...the technology dimension of big data is unavoidable - technology is a critical enabler...
6.1.1. Previous Starting Point
6.1.1.1. Big Data Technology Landscape
6.1.1.2. Azure Data Platform
6.1.1.2.1. ...three distinctive challenges...
6.1.2. Big Data Stacks
6.1.2.1. ...in big data, the technology stack typically takes the form of a pipeline architecture...
6.1.2.1.1. Pipeline Architecture Outline
6.2. Tasks
6.2.1. ...Task #1...
6.2.1.1. Observe tech in StackShare.io
6.2.2. ...Task #2...
6.2.2.1. Build a stack in Crowdcraft.co
6.2.2.1.1. Target Stack
6.2.2.1.2. ...steps...
6.2.3. ...Task #3...
6.2.3.1. Sketch a stack for your team's "Predictive or Prescriptive" concept...
6.2.3.1.1. ...try to answer each of these questions...
6.2.3.1.2. ...boards...
7. The Governance of Big Data
7.1. ...strong governance of data is an increasingly challenging requirement...
7.1.1. Big Data Governance
7.1.1.1. Frameworks
7.1.1.1.1. DAMA DMBOK
7.1.1.2. Challenges
7.1.1.2.1. Privacy, Access, and Use
7.1.1.2.2. Accuracy, Explainability, and Fairness
7.1.1.3. Tools
7.1.1.3.1. ...general...
7.1.1.3.2. ...privacy...
7.1.1.3.3. ...FEAT...
7.2. Tasks
7.2.1. ...Task #1...
7.2.1.1. Assess the "FEAT" exposure of your team's big data concept...
7.2.1.1.1. MAS FEAT
7.2.1.1.2. ...boards...
7.2.2. ...Task #2...
7.2.2.1. Assess the GDPR/PDPA exposure of your team's big data concept...
7.2.2.1.1. Exhibit: GDPR and PDPA
7.2.2.1.2. ...boards...
8. The Skills of Big Data
8.1. ...despite all the "tech", success and failure in big data still hinges on people...
8.1.1. Complex Skills Requirements
8.1.1.1. ...problematic unicorn models...
8.1.1.1.1. Example: Data Science Unicorns
8.1.1.2. ...emerging solutions...
8.1.1.2.1. Red, Blue, and Purple Profiles
8.1.1.2.2. Team Structures
8.2. Tasks
8.2.1. ...Task #1...
8.2.1.1. Sketch the skills profile for your team's "Predictive or Prescriptive" concept...
8.2.1.1.1. ...boards...
9. The Organisation of Big Data
9.1. ...when we move from one use case to an broader big data strategy, we must consider organisation...
9.1.1. Points of Failure
9.1.1.1. ...big data analytics initiatives routinely fail, why?...
9.1.1.1.1. Framing: McKinsey's 10 Flags
9.1.1.2. ...many of these failures derive from organisational, not technical issues...
9.1.1.2.1. Critical Mindsets
9.1.2. Crafting a CoE
9.1.2.1. ...a big data Centre of Excellence trading on an internal market...
9.1.2.1.1. Content
9.1.2.1.2. Positioning
9.1.2.1.3. ...we can look at the "playbooks" of "big data" leaders to consolidate our understanding...
9.2. Tasks
9.2.1. ...Task #1...
9.2.1.1. Translate your team's big data concept to a "Play to Win" canvas...
9.2.1.1.1. ...guide...
9.2.1.1.2. ...boards...
9.2.2. ...Task #2...
9.2.2.1. Make the case for your big data concept...
9.2.2.1.1. Case Making
9.2.2.1.2. ...boards...
9.2.3. ...Task #3...
9.2.3.1. Where will you take your concept next?
9.2.3.1.1. Portfolios
9.2.3.1.2. ...boards...
10. Final Perspective
10.1. ...the data landscape is always moving...
10.1.1. Data Trends
11. Big Data Use Case Inspiration
11.1. Personalisation
11.1.1. ...examples...
11.1.1.1. Service Personalisation
11.1.1.1.1. Example: Retail Omnichannel Experiences
11.1.1.2. Choice Recommendation
11.1.1.2.1. Example: Recommendation Engines
11.1.1.3. Behavioural Nudge
11.1.1.3.1. Example: Congestion Management
11.1.1.4. Data Driven Marketing
11.1.1.4.1. Example: Facebook Travel Ads
11.2. Operational Optimisation
11.2.1. ...examples...
11.2.1.1. Example: Logistics Optimisation
11.2.1.2. Example: Predicitve Maintenance
11.2.1.3. Example: Intelligence Process Automation
11.2.1.4. Example: Autonomous Service Bots
11.3. Economics and Finance
11.3.1. ...examples...
11.3.1.1. Credit and Premium Scoring
11.3.1.1.1. Example: Health Insurance Models
11.3.1.2. Dynamic Pricing
11.3.1.2.1. Example: Uber
11.3.1.2.2. Example: Dynamic B2B Pricing
11.3.1.3. Fraud Detection
11.3.1.3.1. Example: Internal Audit Analytics
11.3.1.4. Algorithmic Trading
11.3.1.4.1. Example: Algorithmic Trading
11.3.1.4.2. Perspective: Zoom Technologies Inc
11.3.1.5. Market Forecasting
11.3.1.5.1. Example: IMF Outlooks
11.4. Other
11.4.1. ...examples...
11.4.1.1. Sports
11.4.1.1.1. Example: FiveThirtyEight
11.4.1.2. Risk Sensing
11.4.1.2.1. Example: Risk Sensing
11.4.1.3. Environment
11.4.1.3.1. Example: Singapore MSS
11.4.1.4. Robotics
11.4.1.4.1. Example: Level 5 Autonomous Vehicles
11.5. General
11.5.1. Tableau
11.5.2. IBM