Week 6

Question 1

How do we adapt to hyper competition?

Answer 1

How do we adapt –> Analytics

Analytics - reosurces that help generate customer insights, identify growth opportunities, and stimulate innovations, producing competitve advantage

Question 2

What are some impacts of increased use of technology?

Answer 2

• fast-changing and fragmented customer preferences
• higher rivalry and difficulty in sustaining competitive advantage
• inability of identifying the problems correctly and fast
• ever-increasing influx of data

Question 3

What is the competitive advantage of data analytics

Answer 3

Competitive advantage of data analytics:
- Early or first ability to capture critical data assets can be difference between a dominating firm and an also ran-
- Advantages based on formulas, algorithms, and data tat others can also acquire will be short-lived
- Differentiation (be better, by being different), will be key in distinguishing operationally effective data from those efforts that can yield true strategic positioning

Question 4

Data base

Answer 4

Data base - single table or a collection of related tables

Question 5

Database management systems (DBMS)

Answer 5

Database management systems (DBMS) - software for creating, maintaining, and manipulating data (also known as database software)

Question 6

Structured query language (SQL)

Answer 6

Structured query language (SQL) - a language used to create and manipulate databases

Question 7

Business Intelligence

Answer 7

Business Intelligence - combining aspects of reporting, data exploration and ad hoc queries, and sophisticated data modeling and analysis
- infrastructure for collecting, storing, and analyzing data produced by the business
- databases, data warehouses, data marts all all under the BI umbrella

Question 8

Business Intelligence Vendors

Answer 8

Business Intelligence Vendors - create BI and analytical software purchased by businesses (via the cloud)

Ex. Tableau

Question 9

Data Analytics

Answer 9

Data Analytics - tools and techniques for analyzing data

Ex. OLAP, Statistics, models, data mining

Question 10

Analytics

Answer 10

Analytics - is the use of:
- Data
- Information Technology
- Statistical analysis
- Quantitative methods, and
- Mathematical or computer based models

Analytics are used to help mangers gain improved insight about their business operations and make better, fact based decisions

Question 11

What is a key capability for a competitive advantage

Answer 11

Data is key capability for a competitive advantage

Question 12

What are 4 types of data analytics / approach to analysis

Answer 12

1) Problem driven approach on structured data
2) Problem driven approach on unstructured data
3) Exploratory analysis on structured data
4) Exploratory analysis approach on unstructured data

Question 13

Metrics

Answer 13

Metrics - used to quantify performance

Question 14

Measures

Answer 14

Measures - numerical values of metrics

Question 15

Discrete metrics

Answer 15

Discrete metrics - involve counting …
- on time or not on time
- number or proportion of on time-deliveries

Question 16

Continuous metrics

Answer 16

Continuous metrics are measured on continuum
- delivery time
- package weight
- purchase price

Question 17

Describe the two data types

Answer 17

Data Types:
(1) Categorical
1.1 Nominal
1.2 Ordinal

(2) Numerical
2.1 Interval
2.2 Ratio

Question 18

Numerical Data

Answer 18

Interval Data:
- ordinal data but with constant differences between observations
- no true ‘zero point’
- ratios are NOT meaningful
- Examples: grades, temperature readings
- “ex. indicate your degree of agreement with the following statements by circling the appropriate number”

Ratio Data:
- continuous values
- there is a natural ‘zero point’
- ratios ARE meaningful
- Examples: monthly sales, delivery times
“approx. how many times in the last month did you purchase something over $10 at 7/11?”

Question 19

Categorical Data

Answer 19

Nominal Data:
- Categories t5hat do not have a natural order or ranking
- Data placed in categories according to a specified characteristics
- Categories bear no quantitative relationship to one another
- Examples: customers location (America, Europe, Asia) & employee classification (Manager, Supervisor, Associate)

Ordinal Data:
- Data with a natural order or ranking, which allows for sorting
- Data that is ranked or ordered according to some relationship with one another
- No fixed units of measurement
- Examples: college football rankings, survey responses (poor, average, good, very good, excellent)
- Yes/No Data is a specific type of nominal data where only tow possible responses (its binary - yes or no)

Question 20

Data Warehouse

Answer 20

Data Warehouse - set of databased designed to support decision making in an organization
- aggregate enormous amounts of data from different operating systems
- structured for fast online queries and exploration
- Data Marts and Warehouses may contain huge volumes of data

Question 21

Data Mart

Answer 21

Data Mart - structured database or databases focused on addressing the concerns of a specific problem or business unit
- e.g., increasing customer retention, improving product quality, or Sales team data
- Data Marts and Warehouses may contain huge volumes of data

Question 22

Operations “feed” Information Systems used for _____

Answer 22

Operations “feed” Information Systems used for Analytics

Information Systems Data Flows from operational systems (i.e., a transaction processing system) to Data Analytics

Question 23

3 V’s of Big Data

Answer 23

3 V’s of Big Data: (1) volume, (2) velocity, (3) and variety - that distinguish it from conventional data analysis problems and require a new breed of technology

Question 24

Data Lake

Answer 24

Data Lake - allow for storage of data in both structured as well as unstructured (raw) formats. They provide the tools to pipe out data, filter it, and refine it so it can be turned into information

Question 25

Data Cloud

Answer 25

Data Cloud - cloud service that provides tools to extract, transform, and load (ETL) data from disparate sources into the cloud so it can be analyzed
- Recall: cloud computing can scale up or scale down
- Flexibility, scalability, cost-effectiveness, and fault tolerance all come into play

Question 26

Building a large scale data warehouse - what are some considerations

Answer 26

Building a large scale data warehouse is a MASSIVE undertaking for any organization - its expensive and time consuming, leveraging a variety of technical and business resources across the company

Before you build: you need a clear vision with business focused objectives (goals)
- executives want to understand the payoff/ROI
- you’ll need executive buy-in to champion the transformation - this is a key success factor
- start with the business issues, not the technology itself; the issues will drive the technology choice

Data relevance, data sourcing, data quantity, data quality, data hosting. Also need to consider data governance

Question 27

Data governance

Answer 27

Data Governance - rules and processes needed to manage data from creation to retirement. Legal, Regulatory, Privacy, Information Security, and Operational considerations

Question 28

Types of Data Analytics

Answer 28

(1) Descriptive - what is happening
- general outstanding what’s going on in business’ (i.e., product sales)
- basic use of data, its informative

(2) Diagnostic - why is it happening
- drilling down to the root cause
- answering ‘why’ brings more value/increased complexity

(3) Predictive - what will (likely) happen?
- historical patterns being used to predict outcomes using algorithms and technology
- moves into insights with more complexity

(4) Prescriptive - how can we make it happen
- recommended actions and strategies, often based on champion/challenger or A/B testing outcomes
- most difficult, highest value, and optimized to provide foresight

As we progress: it goes from information to optimization and there are increased value

Question 29

Applying Data Analytics

Answer 29

Applying Data Analytics
- find PATTERNS in the data
- find OUTLIERS in huge data sets
- make a PREDICTION by identifying key data variables for further prediction
- provide INSIGHTS
- generate STRATEGIES
- enhance a COMPETITIVE ADVANTAGE

Question 30

How does google analytics work

Answer 30

Google Analytics collects data from your websites and apps to create reports and visualizations that help you:
- understand your websites performance
- understand demographic and Behavioural insights
- track goals and conversion optimization
- evaluate campaign performance

Question 31

Why does Business Analytics matter to Accounting and Finance

Answer 31

• unlock value: make information transparent and usable at a much higher frequency
• boost performance: capture detailed performance metrics exposing variability and enhancing performance
• tailored products/solutions: empowers businesses to segment customers more precisely, leading to the development of more precisely tailored products or servers
• better decision making: upgrades decision making processes leading to more effective outcomes
• innovation driver - serves as a catalyst for improvement and developmet of next-generation products

Question 32

Benefits from Data Mastery

Answer 32

• Data leverage is at the center of competitve advantage for firms we’ve already discussed: Zara, Netflix, Uber, and Amazon
• Data matters to all businesses
• An ability to leverage data requires thoughtful product design - you must build with intent to gather and use the data

Question 33

Data vs Information vs Knowledge

Answer 33

Data - raw facts and figures
Information - data presented in a context so that it can answer a question or support decision-making
Knowledge - insight derived from experience and expertise (based on data and information)

Question 34

Big Data

Answer 34

Big Data - the collections, storage, and analysis of extremely large, complex, and often unstructured data sets that can be used by organizations to generate insights that would otherwise impossible to make

The massive amount of data available to today’s managers
- unstructured, big, and costly to work through conventional databases
- made available by new tools for analysis and insight

Decision-making is data-driven, fact-based and enabled by:
- standardized corporate data
- Access to third party data set through cheap, fast computing and easier to use software