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INFS8210 Business Analytics for Managers

Week 2
Descriptive Analytics: Data Warehousing
Chapter 3 – 10th edition | not in 11th edition
Learning Objectives
• Understand the basic definitions and concepts of data warehouses
• Learn different types of data warehousing architectures; their comparative advantages and disadvantages
• Describe the processes used in developing and managing data warehouses
• Explain data warehousing operations
• …
Learning Objectives
• Explain the role of data warehouses in decision support
• Explain data integration and the extraction, transformation, and load (ETL) processes
• Describe real-time (a.k.a. right-time and/or active) data warehousing
• Understand data warehouse administration and security issues
Main Data Warehousing Topics (acronym overload)
• DW definition
• Characteristics of DW
• Data Marts
• ODS, EDW, Metadata
• DW Framework
• DW Architecture & ETL Process
• DW Development
• DW Issues
What is a Data Warehouse?
• A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format
• “The data warehouse is a collection of integrated, ,subject-oriented databases designed to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time”
A Historical Perspective to Data Warehousing 1970s 1980s 1990s 2000s 2010s
ü Mainframe computers
ü Simple data entry
ü Routine reporting
ü Primitive database structures
ü Teradata incorporated
ü Mini/personal computers (PCs)
ü Business applications for PCs
ü Distributer DBMS
ü Relational DBMS
ü Teradata ships commercial DBs
ü Business Data Warehouse coined
ü Centralized data storage
ü Data warehousing was born
ü Inmon, Building the Data Warehouse
ü Kimball, The Data Warehouse Toolkit
ü EDW architecture design
ü Exponentially growing data Web data
ü Consolidation of DW/BI industry
ü Data warehouse appliances emerged
ü Business intelligence popularized
ü Data mining and predictive modeling
ü Open source software
ü SaaS, PaaS, Cloud Computing
ü Big Data analytics
ü Social media analytics
ü Text and Web Analytics
ü Hadoop, MapReduce, NoSQL
ü In-memory, in-database
Characteristics of DWs
• Subject oriented
• Integrated
• Time-variant (time series)
• Nonvolatile
• Summarized
• Not normalized
• Metadata
• Web based, relational/multi-dimensional
• Client/server, real-time/right-time/active...
Example documentation – Oracle Database Data Warehousing Guide
https://docs.oracle.com/en/database/oracle/oracle-database/12.2/dwhsg/introduction-data-warehouse-concepts.html
Data Mart
A departmental small-scale “DW” that stores only limited/relevant data
– Dependent data mart A subset that is created directly from a data warehouse
– Independent data mart
A small data warehouse designed for a strategic business unit or a department
Other DW Components
• Operational data stores (ODS)
– A type of database often used as an interim area for a data warehouse
• Oper-marts
– An operational data mart.
• Enterprise data warehouse (EDW)
– A data warehouse for the enterprise.
• Metadata (Data about data)
– In a data warehouse, metadata describe the contents of a data warehouse and the manner of its acquisition and use
A Generic DW Framework Data Sources ERP Legacy POS Other OLTP/wEB External Select
Transform
Extract
Integrate
Load
ETL
Process
Enterprise
Data warehouse
Metadata
Replication
API/Middleware Data/text mining
Custom built applications OLAP,
Dashboard, Web
Routine
Business
Reporting
Applications
(Visualization)
Data mart
(Engineering)
Data mart
(Marketing)
Data mart
(Finance)
Data mart
(...)
Access
No data marts option
DW Architecture
• Three-tier architecture
1. Data acquisition software (back-end)
2. The data warehouse that contains the data & software
3. Client (front-end) software that allows users to access and analyze data from the warehouse
• Two-tier architecture
First two tiers in three-tier architecture is combined into one
… sometimes there is only one tier?
DW Architectures
Tier 2:
Application server
Tier 1:
Client workstation
Tier 3:
Database server
Tier 1:
Client workstation
Tier 2:
Application & database server
Data Warehousing Architectures
• Issues to consider when deciding which architecture to use:
– Which database management system (DBMS) should be used?
– Will parallel processing and/or partitioning be used?
– Will data migration tools be used to load the data warehouse?
– What tools will be used to support data retrieval and analysis?
A Web-Based DW Architecture
Web
Server
Client
(Web browser)
Application
Server
Data
warehouse
Web pages
Internet/
Intranet/
Extranet
Alternative DW Architectures
Source
Systems
Staging
Area
Independent data marts
(atomic/summarized data)
End user
access and
applications
ETL
Source
Systems
Staging
Area
End user
access and
applications
ETL
Dimensionalized data marts
linked by conformed dimensions
(atomic/summarized data)
Source
Systems
Staging
Area
End user
access and
applications
ETL
Normalized relational
warehouse (atomic data)
Dependent data marts
(summarized/some atomic data)
(a) Independent Data Marts Architecture
(b) Data Mart Bus Architecture with Linked Dimensional Datamarts
(c) Hub and Spoke Architecture (Corporate Information Factory)
Alternative DW Architectures
• Each architecture has advantages and disadvantages!
• Which architecture is the best?
Source
Systems
Staging
Area
Normalized relational warehouse (atomic/some summarized data)
End user access and applications
End user access and applications
Logical/physical integration of common data elements
Existing data warehouses
Data marts and legacy systems
ETL
Data mapping / metadata
(d) Centralized Data Warehouse Architecture
(e) Federated Architecture
Ten factors that potentially affect the architecture selection decision
1. Information interdependence between organizational units
2. Upper management’s information needs
3. Urgency of need for a data warehouse
4. Nature of end-user tasks
5. Constraints on resources
6. Strategic view of the data warehouse prior to implementation
7. Compatibility with existing systems
8. Perceived ability of the in-house IT staff
9. Technical issues
10. Social/political factors
Teradata Corp. DW Architecture
Data Integration and the Extraction,
Transformation, and Load Process
• ETL = Extract Transform Load
• Data integration
– Integration that comprises three major processes: data access, data federation, and change capture.
• Enterprise application integration (EAI)
– A technology that provides a vehicle for pushing data from source systems into a data warehouse
• Enterprise information integration (EII)
– An evolving tool space that promises real-time data integration from a variety of sources, such as relational or multidimensional databases, Web services, etc.
Data Integration and the Extraction,
Transformation, and Load Process
Packaged application
Legacy system
Other internal applications
Transient data source
Extract Transform Cleanse Load
Data warehouse
Data mart
ETL (Extract, Transform, Load)
• Issues affecting the purchase of an ETL tool
– Data transformation tools are expensive
– Data transformation tools may have a long learning curve
• Important criteria in selecting an ETL tool
– Ability to read from and write to an unlimited number of data sources/architectures
– Automatic capturing and delivery of metadata
– A history of conforming to open standards
– An easy-to-use interface for the developer and the functional user
Data Warehouse Development
Data warehouse development approaches
– Inmon Model: EDW approach (top-down)
– Kimball Model: Data mart approach (bottom-up)
– Which model is best?
• Table 3.3 provides a comparative analysis between EDW and Data Mart approach
– Note the source is 15 years old – focus on differences
• One alternative is the hosted warehouse
Additional DW Considerations
Hosted Data Warehouses
• Benefits:
– Requires minimal investment in infrastructure
– Frees up capacity on in-house systems
– Frees up cash flow
– Makes powerful solutions affordable
– Enables solutions that provide for growth
– Offers better quality equipment and software
– Provides faster connections
– … more in the book
Representation of Data in DW
• Dimensional Modeling
– A retrieval-based system that supports high-volume query access
• Star schema
– The most commonly used and the simplest style of dimensional modeling
– Contain a fact table surrounded by and connected to several
dimension tables
• Snowflakes schema
– An extension of star schema where the diagram resembles a snowflake in shape
Multidimensionality
The ability to organize, present, and analyze data by several dimensions, such as sales by region, by product, by salesperson, and by time (four dimensions)
• Multidimensional presentation
– Dimensions: products, salespeople, market segments, business units, geographical locations, distribution channels, country, or – Measures: money, sales volume, head count, inventory profit, actual versus forecast
– Time: daily, weekly, monthly, quarterly, or yearly
Star versus Snowflake Schema
Fact Table
SALES
UnitsSold
...
Dimension
TIME
Quarter
...
Dimension
PEOPLE
Division
...
Dimension
PRODUCT
Brand
...
Dimension
GEOGRAPHY
Country
...
Fact Table
SALES
UnitsSold
...
Dimension
DATE
Date
...
Dimension
PEOPLE
Division
...
Dimension
PRODUCT
LineItem
...
Dimension
STORE
LocID
...
Dimension
BRAND
Brand
...
Dimension
CATEGORY
Category
...
Dimension
LOCATION
State
...
Dimension
MONTH
M_Name
...
Dimension
QUARTER
Q_Name
...
Star Schema Snowflake Schema
Analysis of Data in DW
• OLTP vs. OLAP…
• OLTP (online transaction processing)
– Capturing and storing data from ERP, CRM, POS, …
– The main focus is on efficiency of routine tasks
• OLAP (Online analytical processing)
– Converting data into information for decision support
– Data cubes, drill-down / rollup, slice & dice, …
– Requesting ad hoc reports
– Conducting statistical and other analyses
– Developing multimedia-based applications
– …more in the book
OLAP vs. OLTP
OLAP Operations
• Slice - a subset of a multidimensional array
• Dice - a slice on more than two dimensions
• Drill Down/Up - navigating among levels of data ,ranging from the most summarized (up) to the most detailed (down)
• Roll Up - computing all of the data relationships for one or more dimensions
• Pivot - used to change the dimensional orientation of a ,report or an ad hoc query-page display
OLAP
• Slicing
Operations on a Simple Three-Dimensional
Data Cube
Product Time Geography
Sales volumes of a specific Product on variable Time and Region
Sales volumes of a specific Region on variable Time and Products
Sales volumes of a specific Time on variable Region and Products
Cells are filled with numbers representing sales volumes
A 3-dimensional
OLAP cube with slicing operations
Variations of OLAP
• Multidimensional OLAP (MOLAP)
OLAP implemented via a specialized multidimensional
database (or data store) that summarizes transactions into
multidimensional views ahead of time
• Relational OLAP (ROLAP)
The implementation of an OLAP database on top of an
existing relational database
• Database OLAP and Web OLAP (DOLAP and
WOLAP); Desktop OLAP,…
DW Implementation Issues
• Identification of data sources and governance
• Data quality planning, data model design
• ETL tool selection
• Establishment of service-level agreements
• Data transport, data conversion
• Reconciliation process
• End-user support
• Political issues
• … more in the book
Successful DW Implementation
Things to Avoid
• Starting with the wrong sponsorship chain
• Setting expectations that you cannot meet
• Engaging in politically naive behavior
• Loading the data warehouse with information just because it is available
• Believing that data warehousing database design is the same as transactional database design
• Choosing a data warehouse manager who is technology oriented rather than user oriented
• … more in the book
Failure Factors in DW Projects
• Lack of executive sponsorship
• Unclear business objectives
• Cultural issues being ignored
– Change management
• Unrealistic expectations
• Inappropriate architecture
• Low data quality / missing information
• Loading data just because it is available
Massive DW and Scalability
• Scalability
– The main issues pertaining to scalability:
– The amount of data in the warehouse
– How quickly the warehouse is expected to grow
– The number of concurrent users
– The complexity of user queries
– Good scalability means that queries and other data- access functions will grow linearly with the size of the warehouse
Real-Time/Active DW/BI
• Enabling real-time data updates for real-time analysis and real-time decision making is growing rapidly
– Push vs. Pull (of data)
• Concerns about real-time BI
– Not all data should be updated continuously
– Mismatch of reports generated minutes apart
– May be cost prohibitive
– May also be infeasible
Enterprise Decision Evolution and Data
Warehousing
Real-Time/Active DW at
Teradata
Traditional versus Active DW
DW Administration and Security
• Data warehouse administrator (DWA)
– DWA should…
• have the knowledge of high-performance software, hardware and networking technologies
• possess solid business knowledge and insight
• be familiar with the decision-making processes so as to suitably design/maintain the data warehouse structure
• possess excellent communications skills
• Security and privacy is a pressing issue in DW
– Safeguarding the most valuable assets
– Government regulations (HIPAA, etc.)
– Must be explicitly planned and executed
The Future of DW
• Sourcing…
– Web, social media, and Big Data
– Open source software
– SaaS (software as a service)
– Cloud computing
• Infrastructure…
– Columnar (data in columns instead of row-based)
– Real-time DW
– Data warehouse appliances
– Data management practices/technologies
– In-database & In-memory processing
– New DBMS
– Advanced analytics
– …
Questions?