1. User Notes
1.1. ...notes on tools used in this session...
1.1.1. Zoom
1.1.1.1. Zoom Cheat Sheet
1.1.2. Mindmeister
1.1.2.1. Instructions to Copy Mindmaps
1.1.3. Jamboards
1.1.3.1. Instructions to Copy Jamboards
1.1.3.2. ...board links...
1.1.3.2.1. Team #1
1.1.3.2.2. Team #2
1.1.3.2.3. Team #3
1.2. ...link for this mindmap...
1.2.1. www.mindmeister.com/1811656805/stb-data-2021
1.3. ...export...
1.3.1. PDF
2. Simple Starting Points
2.1. ...data and analytics are now established drivers of value across sectors and industries...
2.1.1. Deloitte: Analytics and AI-driven Enterprises Thrive in the Age of With
2.2. ...this trend is very clear in travel and tourism sectors...
2.2.1. Datahut: Big Data in Tourism: How Big Data Analytics can Help the Travel and Tourism Industry Grow
2.2.2. BCG: In Travel, It’s Time to Push AI Beyond the Pilot Phase
2.3. ...a series of "data driven" innovations have emerged...
2.3.1. Contagious: The German Rail Campaign Facebook’s Sheryl Sandberg Called ‘The Future of Advertising'
2.3.1.1. Task
2.3.1.1.1. Question #1. What data is being used in this example?
2.3.1.1.2. Question #2. How is this data being used to create value in this example?
2.3.1.1.3. ...board links...
3. This Course
3.1. ...the ambition of this course is to use data and data analytics to create value for the customer and the business...
3.1.1. Objective #1. Learn how to derive data use cases and extract insights to improve sales and customer satisfaction...
3.1.2. Objective #2. Build a data model for your business, the pen-and-paper way...
3.1.3. Objective #3. Know how to build data partnerships and an ecosystem...
3.1.4. Objective #4. Understand and apply data security and privacy in the tourism context...
3.1.5. Objective #5. Know what a data-driven organisation looks like and how to get there...
3.2. ...our ambition is to build confidence as "analytics translators"...
3.2.1. McKinsey: Analytics Translators
4. Analytics Recipes
4.1. ...we can think of data and analytics initiatives as recipes that combine raw ingredients to create value...
4.1.1. Vertical Leap: Five Examples of Data Science in the Travel Industry
4.2. ...it is very typical to copy recipes that have been crafted by others...
4.2.1. ActiveWizards: Top 7 Data Science Use Cases in Travel
4.2.2. Kapture CRM: Smart Travel CRM & Booking Management Platform
4.3. ...but we can also craft our own...
4.3.1. The Strategic Choice Cascade
4.3.1.1. Aspiration: what value we hope to create
4.3.1.2. Insight: the insight we need to take action
4.3.1.3. Method: how we will create insight
4.3.1.4. Tools: the technology we choose
4.3.1.5. Capabilities: the skills we need
5. Thinking About "Aspiration"
5.1. ...in order to direct data and analytics initiatives towards value, we need a sense of what data can "do"...
5.1.1. The Analytics Curve
5.1.1.1. ...long read...
5.1.1.1.1. Intel: Analytics Maturity Curve
5.2. ...we can simplify this view down into two key value propositions for data...
5.2.1. Performance Improvement
5.2.1.1. ...looks like...
5.2.1.1.1. Alphabyte: 360° Hotel Group
5.2.2. Predictive Decision Making
5.2.2.1. ...looks like...
5.2.2.1.1. BCG: A New Blueprint for Pricing and Revenue Management in Travel and Tourism
5.3. Task
5.3.1. Setup. Sketch a business that is close to your team's heart
5.3.2. Question #1. What are your team's "Performance Improvement" priorities?
5.3.3. Question #2. What are your team's "Predictive Decision Making" priorities?
5.3.4. ...board links...
5.3.4.1. Team #1
5.3.4.2. Team #2
5.3.4.3. Team #3
6. Thinking About "Insight"
6.1. ...data-driven decisions require metrics to be defined...
6.1.1. Kata
6.1.1.1. ...kata is simple: define a metric, set a goal, measure a baseline, and experiment to improve...
6.1.1.1.1. O'Reilly: Four Steps of the Improvement Kata
6.1.1.2. ...there is a world of metrics to choose from...
6.1.1.2.1. Visual Capitalist: 34 Startup Metrics for Tech Entrepreneurs
6.1.1.3. ...many sector specific metrics have been developed...
6.1.1.3.1. Xotels: KPIs
6.1.1.4. ...and many "ready-made" sets of metrics have been developed for customer services...
6.1.1.4.1. "Usability"
6.1.1.4.2. "NPS"
6.1.1.4.3. "CLV"
6.1.1.4.4. "Lean"
6.1.1.5. ...but we must pick metrics that connect directly to our aspirations...
6.1.1.5.1. Weekdone: OKRs
6.1.1.6. Task
6.1.1.6.1. Question #1. What do our "Performance Improvement" priorities look like if we break them down using the OKR method?
6.1.1.6.2. Question #2. Do any of the ready-made metrics help us make progress with our "Performance Improvement" priorities?
6.1.1.6.3. ...board links...
7. Thinking About "Method"
7.1. ...sharp insights emerge when we bring together metrics, contexts, and conditions, we will explore four formulations...
7.1.1. Kata + Journey
7.1.1.1. ...kata can be enhanced by adding context and conditions, the most common approach is to blend it with a "customer journey" perspective...
7.1.1.1.1. Datahut: Big Data in Tourism: How Big Data Analytics can Help the Travel and Tourism Industry Grow
7.1.1.2. ...a set of "patterns" are very common...
7.1.1.2.1. Pointillist: How to Successfully Implement Customer Journey Analytics
7.1.1.3. ...in order to make it work, we need to sketch our "customer journey"...
7.1.1.3.1. Lucidspark: The 5 Es of Every Customer Journey
7.1.1.4. ...the "Journey Goal" pattern becomes more advanced when a string of metrics representing multiple steps in the customer journey are brought together...
7.1.1.4.1. McKinsey: Four Ways to Shape Customer Experience Measurement For Impact
7.1.1.5. ...the use of journeys with kata is most common in the cases of "funnels" and "a/b tests"...
7.1.1.5.1. "Funnels"
7.1.1.5.2. "A/B Tests"
7.1.1.6. Task
7.1.1.6.1. Question #1. Create a simple "5E" customer journey for a service provided by your team's business.
7.1.1.6.2. Question #2. How might we apply kata with this journey map to help us achieve our "Performance Improvement" aspirations?
7.1.1.6.3. ...board links...
7.1.2. Day 1 Recap
7.1.2.1. ...key takeaways...
7.1.2.1.1. Creating Analytics Use Cases
7.1.2.1.2. What Analytics Can Do
7.1.2.1.3. The Kata Pattern
7.1.2.1.4. Metrics to Track
7.1.2.2. ...consolidation...
7.1.2.2.1. Example
7.1.2.2.2. Task
7.1.3. Kata + Personalisation
7.1.3.1. ...rather than enhancing with "journey" contexts, kata can be enhanced by blending with customer profile data...
7.1.3.1.1. Towards Data Science: Customer Segmentation
7.1.3.2. ...this enables personalisation: fine tuning service design for different customer profiles, based on feedback in the metrics...
7.1.3.2.1. Inspirock
7.1.3.2.2. Facebook Travel Ads
7.1.3.3. ...we can incorporate this into our service operations using specialised "customer relationship management" tools...
7.1.3.3.1. Segment
7.1.3.3.2. Qubit for Travel and Tourism
7.1.3.4. ...chatbots are an increasingly important subset of this group of tools...
7.1.3.4.1. Chatfuel for Travel and Tourism
7.1.3.5. Task
7.1.3.5.1. Question #1. Identify three metrics in your developing data and analytics framework that would benefit from segmentation.
7.1.3.5.2. Question #2. Sketch how you think the Facebook Travel Ads system works.
7.1.3.5.3. ...board links...
7.1.4. Kata + Journey + Personalisation
7.1.4.1. ...the ultimate stage of kata-based patterns is to blend the kata cycle with journey context and customer profile data: creating insight into "what works, for whom, when"...
7.1.4.1.1. Pointillist: How to Successfully Implement Customer Journey Analytics
7.1.4.1.2. CleverTap: Journey Personalisation
7.1.4.2. ...this type of insight sits at the heart of automated "AI" platforms...
7.1.4.2.1. 30 Seconds to Fly
7.1.4.2.2. HelloGbye
7.1.4.2.3. Travel Compositor
7.1.4.3. Task
7.1.4.3.1. Question #1. Shape a kata that incorporates journey context and customer profile, ensure it connects back to your team's aspiration.
7.1.4.3.2. ...board links...
7.2. ...whilst a large part of analytics work focusses on "Performance Improvement", we can push data and analytics one step further to enable predictive decision making...
7.2.1. The Analytics Curve
7.2.1.1. ...key technique...
7.2.1.1.1. Regression
7.2.2. Task
7.2.2.1. Question #1. Shape a hypothetical regression model for a predictive insight that is critical to your team's business.
7.2.2.2. ...board links...
7.2.2.2.1. Team #1
7.2.2.2.2. Team #2
7.2.2.2.3. Team #3
8. Thinking About "Tools"
8.1. ...all data and analytics technology adopts the same fundamental shape...
8.1.1. Microsoft: Components of a Big Data Architecture
8.1.1.1. ...five components are always present...
8.1.1.1.1. Sources
8.1.1.1.2. Sorting and Shaping Tools
8.1.1.1.3. Storage Solutions
8.1.1.1.4. Data Science Tools
8.1.1.1.5. Services
8.1.1.2. ...example...
8.1.1.2.1. Microsoft: Modern Data Warehouse Architecture
8.1.1.2.2. Branger: BI Technical Architecture
8.1.1.3. ...deeper dive...
8.1.1.3.1. Chartio: The Business Buyer’s Guide to Self-Service Business Intelligence
8.2. ...there are many ways to bring this "5S" structure to a business...
8.2.1. Spreadsheets and Spreadsheets+: build your own "5S" analytics workflow...
8.2.1.1. ...options...
8.2.1.1.1. Google Sheets
8.2.1.1.2. Microsoft Excel
8.2.1.1.3. Airtable
8.2.1.1.4. KNIME
8.2.1.2. ...extension...
8.2.1.2.1. ClicData
8.2.1.2.2. Google Data Studio
8.2.1.3. ...example...
8.2.1.3.1. CRM,org: Google Sheet CRMs
8.2.1.3.2. KNIME: B2B Customer Analytics
8.2.2. CRMs: blend customer communications, service management, and analytics...
8.2.2.1. ...options...
8.2.2.1.1. HubSpot
8.2.2.1.2. Zoho CRM Plus
8.2.2.1.3. Pipedrive
8.2.2.2. ...extensions...
8.2.2.2.1. Customer Data Platforms
8.2.2.3. ...example...
8.2.2.3.1. Freshworks: Travel Agency CRM
8.2.2.3.2. Optimove: Relationship Marketing
8.2.2.3.3. CleverTap: Travel
8.2.3. ERPs: integrating analytics within the operational management process...
8.2.3.1. ...options...
8.2.3.1.1. Xero
8.2.3.1.2. QuickBooks
8.2.3.1.3. FreshBooks
8.2.3.1.4. Sage
8.2.3.1.5. Wave
8.2.3.1.6. Zoho Books
8.2.3.2. ...example...
8.2.3.2.1. Xero and Float: Cashflow Analytics
8.2.4. CRM+ERP: "business in a box" solutions blending customer analytics and operational insights...
8.2.4.1. ...options...
8.2.4.1.1. Zoho One
8.2.4.1.2. Freshworks
8.2.4.1.3. Monday
8.2.4.1.4. O365
8.2.4.1.5. KissFlow
8.2.4.2. ...example...
8.2.4.2.1. Zoho Desk + Zoho CRM
8.2.5. Cloud: designing and building a tailored "5S" architecture in a cloud platform, typically as part of a broader shift to digitalise the business...
8.2.5.1. ...options...
8.2.5.1.1. AWS
8.2.5.1.2. Azure
8.2.5.1.3. Google
8.2.5.2. ...example...
8.2.5.2.1. AWS: Case Study of Pinoy Travel
8.2.5.2.2. Azure: Alpha Travel
8.2.5.2.3. Azure: Melco
8.2.5.3. ...more...
8.2.5.3.1. AWS: 6R Strategies
8.3. ...arguably the most important question to answer is how you will capture the data you need...
8.3.1. Instrumentation
8.3.1.1. ...installing methods and tools of information capture throughout service journeys and operations...
8.3.1.1.1. Usabilla
8.3.1.1.2. Pointillist
8.3.1.1.3. Mopinion
8.3.1.1.4. Groove
8.3.1.1.5. Google Analytics
8.3.1.1.6. MailChimp
8.3.1.1.7. Optimizely
8.3.1.1.8. Google Forms
8.3.1.2. Task
8.3.1.2.1. Question #1. Identify five tools that could be used to instrument the team's customer journey.
8.3.1.2.2. ...board links...
8.3.2. Partnerships
8.3.2.1. ...creation and management of data partnerships designed to boost or enhance our existing data assets...
8.3.2.1.1. Buying Data
8.3.2.1.2. Sharing Data
8.3.2.1.3. Consuming Data
8.3.3. Governance
8.3.3.1. Talend: Data Governance
8.3.3.1.1. ...details...
9. Thinking About "Capabilities"
9.1. ...achieving success with a data initiative is about much more than technology choices...
9.1.1. Deloitte: Insight Driven Organisation
9.1.2. McKinsey: Achieving Business Impact With Data - McKinsey
9.1.3. Accenture: Building An Analytics-Driven Organization
9.2. ...pitfalls and successes show the need for a holistic approach...
9.2.1. McKinsey: Ten Red Flags Signaling Your Analytics Program Will Fail
9.2.1.1. ...details...
9.2.1.1.1. 1. The executive team doesn’t have a clear vision for its advanced-analytics programs
9.2.1.1.2. 2. No one has determined the value that the initial use cases can deliver in the first year
9.2.1.1.3. 3. There’s no analytics strategy beyond a few use cases
9.2.1.1.4. 4. Analytics roles - present and future - are poorly defined
9.2.1.1.5. 5. The organization lacks analytics translators
9.2.1.1.6. 6. Analytics capabilities are isolated from the business, resulting in an ineffective analytics organization structure
9.2.1.1.7. 7. Costly data-cleansing efforts are started en masse
9.2.1.1.8. 8. Analytics platforms aren’t built to purpose
9.2.1.1.9. 9. Nobody knows the quantitative impact that analytics is providing
9.2.1.1.10. 10. No one is hyperfocused on identifying potential ethical, social, and regulatory implications of analytics initiatives
9.2.1.2. Task
9.2.1.2.1. Question #1. How will your team avoid the "red flag" pitfalls?
9.2.1.2.2. ...board links...
9.2.2. Uber: Michelangelo Project
9.3. ...results depend on merging individual data and analytics initiatives, or "use cases", with business fundamentals...
9.3.1. McKinsey: Use Case Prioritisation
9.3.1.1. Task
9.3.1.1.1. Question #1. Which of the initiatives you have considered should be prioritised?
9.3.1.1.2. ...board links...
9.3.2. Dataiku: RoI
9.3.3. McKinsey: RoI
9.3.4. Revenue, Savings, Productivity
9.4. ...human skills remain essential to make this happen...
9.4.1. McKinsey: Analytics Translator, The New Must-Have Role
9.4.2. Mckinsey on Revenue Management
9.4.3. Gartner: Three Cycles
9.4.4. Pilcher: Product Management Framework