Week 1: Business Analytics Flashcards
Data analytics (definition)
Cleaning, processing,
and analyzing data to tell stories,
help decision-making, improve
business operation, performance,
customer satisfaction/experience, etc.
Business analytics (definition)
Use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models
Goals of business analytics
- Uncover patterns, relationships, and insights
- Enable better business decision-making
- Solve business problems, monitor their
business fundamentals, identify new growth
opportunities - Enhance customer experience and satisfaction
Five stages of business analytics (maturity toward business value)
- Data Wrangling
- Descriptive Analytics
- Predictive Analytics
- Prescriptive Analytics
- Storytelling
1st stage of business analytics
Data wrangling
Data wrangling (definition)
Preparing data for analytics.
Examples of data wrangling
Data transformation, data structuring, and SQL
2nd stage of business analytics
Descriptive analytics
Descriptive analytics (definition)
Describing what has happened; identifying trends/patterns in historical data
Examples of descriptive analytics
Data mining, web analytics, and IoT analytics
3rd stage of business analytics
Predictive analytics
Predictive analytics (definition)
Predicting future outcomes (demand forecasting)
Examples of predictive analytics
A/B testing and forecasting
4th stage of business analytics
Prescriptive analytics
Prescriptive analytics (definition)
Deciding what we should do
Example of prescriptive analytics
Optimization
5th stage of business analytics
Storytelling
Storytelling (definition)
Communicating analytics for decision-making
Example of storytelling
Visualization
How business analytics affects firms
It provides data and informs actions
How do the firm’s actions affect business analytics
It provides market data
Data (definition)
Facts, numbers, words, observations, or other useful information
Quantitative data (definition)
Data that can be quantified or measured
Qualitative data (definition)
Descriptive information related to concepts and characteristics rather than numbers
Structured data (definition)
Data residing in a fixed field within a file or record
Unstructured (definition)
Data not in a specific format
Firm-generated data [FGD] (definition)
Information created and collected by the company itself
Consumer-generated data [CGD] (definition)
Data that is created and shared voluntarily by customers
Big data (definition)
Large, hard-to-manage volumes of structured/unstructured that flood businesses on a day-to-day basis
5 Vs of Big Data
Volume, velocity, veracity, value, and variety
Data volume (definition)
Size of the data
Data velocity (definition)
The speed data appears and disappears
Data veracity (definition)
Reliability of the data
Data value (definition)
Relevance of the data
Data variety (definition)
Types of data
Sources of data
-Operational data
- Social media
- Review sites
- Customer data
- Payment information
- Mobile apps
Guest customer journey in Digital Transformation Era steps
- Pre-travel
- Research
- Booking
- On-site experience
- Post-travel
Pre-Travel Technology Digitalization examples
Social media marketing
Pre-Travel Data Digitalization examples
Social medial KPI’s
Research Technology digitalization examples
Website, search engine marketing, and meta search reviews
Research Data digitization examples
Website KPI’s and online reviews
Booking technology digitization examples
Website and mobile app
Booking data digitization examples
Guest and transaction data
On-site experience technology digitization examples
Mobile app, in-room technology, and AI assistants
On-site experience data digitization examples
Guest behavioral data and transaction data
Post-travel technology digitization examples
Social media and mobile app
Post-travel data digitization examples
Direct feedback and online reviews
Innovative data collection technologies
- Facial recognition
- Robotics
- Smart assistant
- Virtual reality
- Mobile applications
Data types we can collect
- Guest Info
- Expenditure/payment
- Room preferences and usage
- Interaction data with AI
- Booking and transaction
- Internet usage
- Movement
- Energy consumption
- Social media and online interaction
Simulation algorithms (definitions)
Recommended actions/strategies for desired outcomes
Diagnostic analytics (definitions)
Causes of observed patterns
CRISP-DM acronym
Cross-industry standard process for data mining
Tools for data & text analysis
Rapid Miner, XLMiner, Nvivo, LIWC, Sentiment Analysis, SAS Enterprise Miner, SAS Enterprise Guide
Tools for data collection and programming
Java, Excel VBA, ASP, SQL, Python
Tools for statistical analysis
R, STATA, SPSS, SAS
Tools for data visualization
Tableau, Power BI
4 reasons why business analytics are relevant
- Addressing industry challenges
- Changing and growing competition
- More data, better tools
- Smarter decisions for everyone
How business analytics address industry challenges
Forecasting demand, optimizing staffing, improving pricing, and resolving guest dissatisfaction
