BA 205 Business Analytics- Chapter 1

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What are the three enablers of growth in the use of analytical methods in business applications?

1. Technological advances that produce more data for businesses
2. New methodological developments
3. Explosion in computing power and storage capability
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what are the technological advances that produce more data for businesses?

1. internet of things
2. e-commerce
3. social media
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what are the new methodological developments?
* turnover rate
* turnover by job role
* turnover by gender
* turnover by department
* turnover by tenure
* example) sales representative in sales department, men, leave after 1-2 years
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what performance power does an exascale computer have?
1\.1 exaflops 10 to the 18th power of computing performance
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Decision ma__king:__ senior management
senior management is unstructured

* ex) decide entrance or exit from markets.
* ex) approve capital budget
* ex) decide long-term goals
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Decision making: middle management
middle management is semistructured

* ex) design a marketing plan
* ex) develop a departmental budget
* ex) design a new corporate web site
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Decision making: operational management
operational management ( individuals employees and teams) is structured

* ex) determine overtime eligibility
* ex) restock inventory
* ex) offer credit to customers
* ex) determine special offers to customers
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Decision making:

what are strategic decisions?
* involve higher-level issues concerned with the overall direction of the organization
* define the organization’s overall goals and aspirations for the future
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Decision making:

what are tactical decisions?
* concern how the organization should achieve the goals and objectives set by its strategy
* are usually the responsibility of midlevel management
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Decision making:

what are operational decisions?
* affect how the firm is run from day to day
* are the domain of operations managers, who are the closest to the customer
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what are the steps of rational decision making?

1. identify and define the problem or opportunity
2. generative alternative solutions based on a criteria
3. evaluate and select alternative solutions
4. implement and evaluate the solution chosen
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what are the other approaches to decision making?
* tradition
* intuition
* rules of thumb
* using the relevant data available
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what is the definition of business analytics?
scientific process of transforming data into insight for making better decisions
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what are the components of business analytics?
* predictive analytics
* data collection
* data analysis
* statistical analysis
* modeling
* implementation
* problem definition
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what are the reported benefits of business analytics?
* 68% achieve competitive advantage
* 55% find new revenue opportunities
* 52% increase profitability
* 45% increase customer service
* 45% gain operational efficiencies
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descriptive analytics
* what happened?
* examines historical data to answer questions
* it is more basic value, insights, and complexity
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predictive analytics
* what will happen?
* uses current and historical data to predict future activity
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prescriptive analytics
* how do we make it happen?
* applies rules and modeling for better decision-making
* higher value, insights, and complexity
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what are descriptive analytics tools?
* data queries
* reports
* descriptive statistics
* data mining techniques
* dashboards
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what are predictive analytics tools?
* surveys
* linear regression
* time series analysis
* scenario simulation
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descriptive

predictive

prescriptive
* understand the past
* predict the future
* modify the future
* understand the past 
* predict the future 
* modify the future
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what are the prescriptive analytics: optimization models?
* model analysis
* decision analysis
* model analysis 
* decision analysis
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what is the definition of big data?
any set of data that is too large or too complex to be handled by standard data-processing techniques
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what is the collection of big data?
* internet cookies
* email tracking
* smart phones
* online purchase transaction forms
* website interactions
* transaction histories
* third-party trackers: companies that collect and sell clients and profitable data
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what are the data sources of big data?
* social network data
* financial data
* multimedia data
* data from ERP systems
* internet of things
* mobile apps data
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Big data example: Amazon

how much data does amazon collect?
1 exabyte of purchase history from their consumer base
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Big data example: Amazon

how amazon collects data?

1. personalized recommendation system
2. anticipatory shipping model
3. alexa
4. one-click ordering
5. book recommendations from kindle highlighting
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Big data example: Amazon

how amazon uses big data?

1. supply chain optimization
2. price optimization
3. additional purchases per order
4. to keep you scrolling
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What are the 5v’s of big data?
* velocity
* volume
* variety
* veracity
* value
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Big Data:

what is velocity?
speed at which data is emanating and changes are occurring between the diverse data sets
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Big Data:

what is volume?
this refers to the sheer volume of data being generated every second
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Big Data:

what is variety?
can use structured as well as unstructured data
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Big Data:

what is veracity?
data reliability and trust. verifying and validating the data.
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Big Data:

what is value?
having access to big data is all well and good but that’s only useful if we can turn it into a value
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What does big data in the health care industry?
* volume - 2,314 exabytes
* velocity - patient records and test results
* variety - structured data, semi-structured data, unstructured data
* veracity - accuracy. trustworthiness.
* value - disease detection, better treatment, and reduced costs
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what are the pros of big data?
* increased productivity
* reduced costs
* improved customer service
* fraud detection
* more significant innovation
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what are the cons of big data?
* need for talent
* cybersecurity risks
* hardware needs
* data quality
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what is artificial intelligence?
“artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving “…” which seek to create expert systems which make predictions or classifications based on input data.”
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AI Applications: speech recognition
* process human speech into a written format
* ex) siri
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AI Applications: customer service
* online virtual agents answer frequently asked questions (FAQS) or provide personalized advice, or cross-selling products
* ex) messaging bots on e-commerce sites with virtual agents, messaging apps, such as slack and facebook messenger
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AI Applications: computer vision
* ai derives information form digital images, videos and other visual inputs
* ex) photo tagging in social media, radiology imaging in healthcare, and self-driving cars within the automotive industry
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AI Applications: recommendation engines
* ai algorithms can help to discover data trends to develop more effective cross-selling strategies
* ex) amazon add-on recommendations to customers during the checkout process
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Business Analytics in practice: financial analytics
using predictive moles to:

* forecast financial performance
* assess the risk of investment portfolios and projects

construct optimal portfolios of investments

create optimal capital budgeting plans
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Business Analytics in practice: human resource (HR) analytics
ensuring that the organization:

* has the mix of skill sets necessary to meet its needs
* is hiring the highest-quality talent and providing an environment that retains it
* achieves its organizational diversity goals
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Business Analytics in practice: marketing analytics
* better use of advertising budgets
* more effective pricing strategies
* improved forecasting of demand
* improved product-line management
* increased customer satisfaction and loyalty
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Business Analytics in practice: health care analytics
used to improve:

* patient, staff, and facility scheduling
* patient flow
* purchasing
* inventory control

use of prescriptive analytics for diagnosis and treatment
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what are principles of data ethics for business analytics?
* ownership
* transparency
* privacy
* intention
* outcomes
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Ethics for business analytics: ownership
* an individual has ownership over their personal information
* it is unlawful and unethical to collect someone’s personal data without their consent
* use written agreements, digital privacy policies, pop-ups with checkboxes
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Ethics for business analytics: transparency
* an individual has a right to know how to plan to collect, store, and use their personal information
* withholding or lying about your intentions is unlawful and unfair to your data subjects
* write a policy explaining how you will be using personal information
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Ethics for business analytics: privacy
* personally identifiable information will not be publicly available
* use a secure database
* de-identifying a dataset leaving only anonymous data
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Ethics for business analytics: intention
* why you need it, what you’ll gain from it, and what changes you’ll be able to make after analysis
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Ethics for business analytics: outcomes
* be aware of “disparate impact”
* how the data analysis might result of unintentional bias in an algorithm that disproportionately impacts a specific group of people