Week 6 Flashcards
How do we adapt to hyper competition?
How do we adapt –> Analytics
Analytics - reosurces that help generate customer insights, identify growth opportunities, and stimulate innovations, producing competitve advantage
What are some impacts of increased use of technology?
- 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
What is the competitive advantage of data analytics
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
Data base
Data base - single table or a collection of related tables
Database management systems (DBMS)
Database management systems (DBMS) - software for creating, maintaining, and manipulating data (also known as database software)
Structured query language (SQL)
Structured query language (SQL) - a language used to create and manipulate databases
Business Intelligence
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
Business Intelligence Vendors
Business Intelligence Vendors - create BI and analytical software purchased by businesses (via the cloud)
Ex. Tableau
Data Analytics
Data Analytics - tools and techniques for analyzing data
Ex. OLAP, Statistics, models, data mining
Analytics
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
What is a key capability for a competitive advantage
Data is key capability for a competitive advantage
What are 4 types of data analytics / approach to analysis
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
Metrics
Metrics - used to quantify performance
Measures
Measures - numerical values of metrics
Discrete metrics
Discrete metrics - involve counting …
- on time or not on time
- number or proportion of on time-deliveries
Continuous metrics
Continuous metrics are measured on continuum
- delivery time
- package weight
- purchase price
Describe the two data types
Data Types:
(1) Categorical
1.1 Nominal
1.2 Ordinal
(2) Numerical
2.1 Interval
2.2 Ratio
Numerical Data
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?”
Categorical Data
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)
Data Warehouse
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
Data Mart
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
Operations “feed” Information Systems used for _____
Operations “feed” Information Systems used for Analytics
Information Systems Data Flows from operational systems (i.e., a transaction processing system) to Data Analytics
3 V’s of Big Data
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
Data Lake
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
Data Cloud
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
Building a large scale data warehouse - what are some considerations
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
Data governance
Data Governance - rules and processes needed to manage data from creation to retirement. Legal, Regulatory, Privacy, Information Security, and Operational considerations
Types of Data Analytics
(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
Applying Data Analytics
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
How does google analytics work
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
Why does Business Analytics matter to Accounting and Finance
- 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
Benefits from Data Mastery
- 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
Data vs Information vs Knowledge
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)
Big Data
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
