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Core of Business “Intelligence” technology. Database warehouse, data mining and on-line analytical processing . Business Intelligence and Analytics for Decision Support. The diagram show the role played by data warehouse, data-mining

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core of business intelligence technology

Core of Business “Intelligence” technology

Database warehouse, data mining and on-line analytical processing

slide2

Business Intelligence and Analytics for Decision Support

The diagram show the role played by data warehouse, data-mining

and olap in the “overall” business “decision making” process

Business intelligence and analytics requires a strong database foundation, a set of analytic tools, and an involved management team that can ask intelligent questions and analyze data.

Laudon and Laudon

Chapter 10

the data warehouse
The Data Warehouse

“A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of “all” an organisation’s data in support of management’s decision making process.”

  • Data warehouses developed because E.G.:
  • if you want to ask “How much does this customer owe?” then the sales database is probably the one to use. However if you want to ask “Was this ad campaign more successful than that one?”, you require data from more disparate sources Other sources e.g. production, marketing etc.
characteristics of a data warehouse
Characteristics of a Data Warehouse
  • Subject oriented – (based around business processes; e.g. sale of products, Customer purchases
  • Integrated – inconsistencies removed
  • Nonvolatile – stored in read-only format
  • Time variant – data is “static” and update periodically;
  • Summarized – in decision-usable format; monthly average; total quanity
  • Large volume – data sets are quite large; all the pertinent data of an organisation
  • Non normalized – often redundant: “non-relational” star flake schema (dimension tables and fact tables):
the atomic schema

Customer

Cust Purchases

Product Ref

Product Code

ProdRef Eff. Date

Customer ID

Activity Date

Product Code

Customer ID

Status Date

ProdRef End Date

Product Name

Unit Price

Product Category

Product Type

Product Sub Type

Cust Addr State

Cust ZIP Code

Customer Type

Customer Status

...

Product Name

Sales Rep ID

Qty Purchased

Total Dollars

Promotion Flag

Cust Averages

Outlet Reference

Customer ID

Cust Average Date

Sales Rep Ref

Store ID

Sales Rep ID

Store Name

Store Location

Distribution Channel

Cust Avg. End Date

Cust Avg. Rev.

Cust Longevity

Sales Person Name

Store ID

The Atomic Schema
for example

Customer Location

Selling Responsibility

Cust ZIP Code

Purchases 1

Sales Rep ID

City

State/Province

Country

Sales Rep ID

Product Code

Cust ZIP Code

Customer Type

Week Ending Date

Sales Rep Name

Store ID

Store Name

Store Location

Sales Channel

Customer Type

Customer Type

Days of Activity

Unit Price

Total Quantity

Total Dollars

Returned Qty

Returned Dollars

Promotion Qty

Cust Type Desc

Product

Product Code

Date Information

Product Name

Prod. Category

Product Type

Prod Sub Type

Week Ending Date

Month

Quarter

Year

For Example:
star schema query
Star Schema Query

Select E.Month, B.Customer_Type, C.Product_Type,

D.Store_Location, sum(A.Total_Quantity) (note A is the fact table)

From Purchases_1 A, Customer_Type B, Product C,

Selling_Responsibility D, Date_Information E

Where B.Customer_Type = A.Customer_Type and

C.Product_Code = A.Product_Code and

D.Sales_Rep_ID = A.Sales_Rep_ID and

E.Week_Ending_Date = AWeek_Ending_Date and

E.Year = “1996” and

C.Product_Category = “V”

Group by E.Month, B.Customer_Type, C.Product_Type,

D.Store_Location;

meta data
Meta Data
  • A key concept behind D.W. is Meta Data.
    • Meta data is data about the data (which has come from the data sources) and shows what data is contained in the DW, where it came from, and what changes have been made to it.
  • The metadata are essential ingredients in the transformation of raw data into knowledge. They are the “keys” that allow us to handle the raw data.
    • For example, a line in a sales database may contain: 1023 K596 111.21
    • This is mostly meaningless until we consult the metadata (in the data directory) that tells us it was store number 1023, product K596 and sales of $111.21.
meta data answers questions for users of the data warehouse
Meta Data Answers Questions for Users of the Data Warehouse
  • How do I find the data I need?
  • What is the original source of the data?
  • How was this summarization created?
  • What queries are available to access the data?
  • How have business definitions and terms changed over time?
  • How do product lines vary across organizations?
  • What business assumptions have been made?
dependent data marts
Dependent Data marts
  • A data mart is a data store that is subsidiary to a data warehouse of integrated data.
  • The data mart is directed at a partition of data (subject area) that is created for the use of a dedicated group of users and is sometimes termed a “subject warehouse”
  • The data mart might be a set of denormalised, summarised or aggregated data that can be placed on the data warehouse database or more often placed on a separate physical store.
  • Data marts can be “dependent data marts” when the data is sourced from the data warehouse.
  • Independent data marts represent fragmented solutions to a range of business problems in the enterprise, however, such a concept should not be deployed as it doesn’t have the “data integration” concept that’s associated with data warehouses.
independent data marts
Independent Data marts
  • However, such marts are not necessarly all bad.
  • Often a valid solution to a pressing business problem:
    • Extremely urgent user requirements
    • The absence of a budget for a full data warehouse
    • The decentralisation of business units
data warehousing architecture
Data Warehousing Architecture
  • Access Tools
    • The principal purpose of the data warehouse is to provide information for strategic decision making.
    • The main Decision tools used to achieve this objective are:
      • Data mining tools
      • On-line analytical processing tools
      • Decision support systems / Executive information system tools
  • The dataware house, like all organisational databases, can be centralised or distributed.
data warehousing typology
Data Warehousing Typology
  • THE D.W. can be at single location i.e. a central data warehouse
  • The collection of data is replicated around multiple locations. This means users have a local copy of the data warehouse. This can improve query run-times, and reduce communications overheads. Distributed Data warehouse (Note: The principles associated with distributed database equally apply to Distributed Data warehouses, however, the static nature of the data needs to be factored in to the design process ) .
data warehouse construction tips
Data Warehouse Construction Tips
  • Accept that your first try will require revision
  • Examine the data: What formats and specific data are needed to support your application?
  • Clean up the data before using it in the warehouse
  • Build a prototype mini-data warehouse as a learning experience and revise strategies as necessary
  • Plan on more users than anticipated wanting to use the warehouse
  • Keep storage requirements constantly in mind
data mining
Data Mining
  • The process of extracting valid, previously unknown, comprehensible, and actionable information from large databases and using it to make crucial business decisions.
  • Involves the analysis of data and the use of software techniques for finding hidden and unexpected patterns and relationships in sets of data.
data mining1
Data Mining
  • Data mining tools uses ,e.g. AI techniques, to help:
    • predict future trends: ,
    • Segment datasets
    • “Product” association
  • allowing businesses to make proactive, knowledge-driven decisions.
data mining a i operations
Data mining: A.I. operations.
  • Some of the most commonly used techniques A.I. techniques in data mining are:
    • Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset.
    • Rule induction: The extraction of useful if-then rules from data based on statistical significance.
    • Artificial neural networks: Predictive models that learn through training and resemble biological neural networks in structure.
    • Clustering: a technique to group data points into different clusters
    • Regression analysis: analysis the relationship between variables
how data mining works
How Data Mining Works
  • For example, say that you are the director of marketing for a insurance company and you'd like to acquire some new customers
    • You could just randomly go out and mail coupons to the general population. However you would not achieve the required result.
    • Alternatively As the marketing director you have access to a lot of information about all of your customers: their age, sex, income range and credit card insurance.
how data mining works1
How Data Mining Works
  • The goal in prospecting is to make some decisions about the information in the lower right hand quadrant based on the model that we build going from Customer General Information to Customer Proprietary Information.
data mining techniques
Data Mining techniques
  • Data mining operations include:
    • Predictive modelling: decision trees, regression analysis…
    • Database segmentation: clustering techniques
    • Link analysis: decision trees, association rules
predictive modeling
Predictive Modeling

Simple decision tree example

  • Applications of predictive modelling include direct marketing and use techniques like decision trees.
    • uses observations to form a model of the important characteristics of some phenomenon: e.g. those traits associated with those who will buy property;
    • can be used for target marketing….
how data mining works2
How Data Mining Works
  • For instance, a simple model for a
  • Insurance company might be:
    • Customers who earn between 50 K to 60 K have a life insurance policy.
  • This model could then be applied to the general population to target those for the life insurance promotion.
  • The tree can be more complex e.g. See figure opposite
database segmentation
Database Segmentation
  • Aim is to partition a database into an unknown number of segments, or clusters, of similar records.
  • Uses clustering techniques in order to group data
  • Applications of database segmentation include fraudulent activity (credit card), market segmentation, customer segmentation….
link analysis
Link Analysis
  • Aims to establish links between records, or sets of records, in a database; one such example would be association discovery….
  • Applications include product affinity analysis.
  • Finds items that imply the presence of other items in the same event.
link analysis associations discovery
Link Analysis - Associations Discovery
  • Affinities between items are represented by association discovery.
    • e.g. ‘When a customer rents property for more than 2 years and is more than 25 years old, in 40% of cases, the customer will buy a property. This association happens in 35% of all customers who rent properties’.
examples of applications of data mining
Examples of Applications of Data Mining
  • Retail / Marketing
    • Predicting response to mailing campaigns
  • Banking:
    • Detecting patterns of fraudulent credit card use.
  • Insurance
    • Claims analysis
data mining in conclusion
Data mining in conclusion
  • Two critical factors for success with data mining are:
    • a large, well-integrated data warehouse and
    • a well-defined understanding of the business process within which data mining is to be applied (e.g. customer prospecting (target marketing), retention, campaign management etc.).
what is olap
What is OLAP
  • OLAP stands for "On-Line Analytical Processing.“
  • OLTP ("On-Line Transaction Processing")
  • OLAP describes a class of technologies that are designed for live ad hoc data access and analysis.
  • OLTP generally relies solely on relational databases,
  • OLAP has become synonymous with multidimensional views of business data supported by multidimensional databases
  • Relational databases were never intended to provide data synthesis, analysis and consolidation functionality.
what is olap1
What is OLAP
  • OLTP databases are optimised for transaction updating however,
  • OLAPapplications are used by managers and analysts for a higher level aggregate view of the data, thus they are designed for analysis.
  • Many problems that people try to solve using relational databases e.g. summaries are handled much more efficiently by an OLAP server than by RDBMS
key olap server features
Key OLAP “server” Features

Although OLAP applications are found in widely divergent functional areas, as illustrate in the table opposite. Moreover they all have the following key features:

multi-dimensional views of data (MD databases via Star Schema)

Support complex calculations

Time intelligence

Data sparicity

slide35

Star Schema: basis of MD view

A star schema for credit card purchases

slide36

Multi-dimensional view as a cube: also represented a 4 column table

  • Example of three-dimensional query.
  • What is the total amount and number of purchases for vehicles in region 2 for December.

Multidimensional cube for credit card purchases

why multidimensional data
Why Multidimensional Data
  • Queries requiring only a single number to be retrieved need not use multidimensional databases.
  • If queries involved retrieving multiple numbers and aggregating them for large databases can become intolerable as relational databases can scan only a few hundred records per second.
  • However multidimensional databases can add up 10,000 or more numbers in rows and columns per second.
  • Thus for such queries multidimensional databases have an enormous performance advantage
multi dimensional operations

Multi-dimensional Operations

Slice – A single dimension operation

Dice – A multidimensional operation

Roll-up – A higher level of generalization

(total sales: can be simple (e.g. region) or multiple (region, product type)

Drill-down – A greater level of detail

Rotation – View data from a new perspective

drill down to core database
Drill down to core database
  • Most organisations now utilise relational databases as standard for their data warehouses.
  • Often there is no need to replicate all the data in the relational database into a MD database for OLAP.
  • Summary level data can be kept in the MD database and detailed data in the relational database.
support for complex calculations
Support for complex calculations
  • Important computational features of OLAP servers inlcude:
    • Independently dimensioned variables (IDV):
      • numeric measures variables (facts) such as Sales, Cost, price…; based on relevant dimensions; region, customer type, product…
    • Statistical calculations
      • provide a range of powerful computational and statistical methods such as that required by sales forecasting: regression analysis , projection . Correlations…
    • Vector Arithmetic
time series data types
Time Series Data Types
  • Users want to look at trends in all aspects of their business e.g. sales trends, market trends etc.
  • A series of numbers representing a particular variable over time is called a time series e.g.. 52 weekly sales numbers is a time series.
  • Utilising a time-series data type allows you to store an entire string of numbers representing daily, weekly or monthly data.
  • Thus an OLAP server that supports time-series data type allows one to store historical data without having to specify a separate dimension for time.
  • Unlike other dimensions time has special attributes and rules: periodicity,
sparse data
Sparse Data
  • When less than 10% of the cells contain data the database is said to be sparsely populated or sparse.
  • Scarcity can also occur if there are many cells that contain the same number e.g.. Price of a product every day.
  • This situation can also be represented by storing the number once along with the number of days that the number is repeated
  • While a relational database would fill up the database with duplicate data an OLAP server that understands sparse data can skip over zeros, missing data and duplicate data.
the atomic schema1

Customer

Cust Purchases

Product Ref

Product Code

ProdRef Eff. Date

Customer ID

Activity Date

Product Code

Customer ID

Status Date

ProdRef End Date

Product Name

Unit Price

Product Category

Product Type

Product Sub Type

Cust Addr State

Cust ZIP Code

Customer Type

Customer Status

...

Product Name

Sales Rep ID

Qty Purchased

Total Dollars

Promotion Flag

Cust Averages

Outlet Reference

Customer ID

Cust Average Date

Sales Rep Ref

Store ID

Sales Rep ID

Store Name

Store Location

Distribution Channel

Cust Avg. End Date

Cust Avg. Rev.

Cust Longevity

Sales Person Name

Store ID

The Atomic Schema
the star schema

Dimension Table 3

Dimension Table 2

Dimension Table 4

The Star Schema

Dimension Table 1

Fact Table

Dimension Key 1

Dimension Key 3

Dimension Key 1

Dimension Key 2

Dimension Key 3

Dimension Key 4

Description 1

Aggregatn Lvl 1.1

Aggregatn Lvl 1.2

Aggregatn Lvl 1.n

Description 3

Aggregatn Lvl 3.1

Aggregatn Lvl 3.2

Aggregatn Lvl 3.n

Fact 1

Fact 2

Fact 3

Fact 4

.

.

.

Fact n

Dimension Key 2

Dimension Key 4

Description 2

Aggregatn Lvl 2.1

Aggregatn Lvl 2.2

Aggregatn Lvl 2.n

Description 4

Aggregatn Lvl 4.1

Aggregatn Lvl 4.2

Aggregatn Lvl 4.n

dimension table

Dimension Table 1

Dimension Key 1

Description 1

Aggregatn Lvl 1.1

Aggregatn Lvl 1.2

Aggregatn Lvl 1.n

Dimension Table
  • Describes the data that has been organized in the Fact Table
  • Key should either be the most detailed aggregation level necessary (e.g. country vs. county), if possible, or...
  • Manageable number of aggregation levels
fact table

Fact Table

Dimension Key 1

Dimension Key 2

Dimension Key 3

Dimension Key 4

Fact 1

Fact 2

Fact 3

Fact 4

.

.

.

Fact n

Fact Table
  • Quantifies the data that has been described by the Dimension Tables
  • Key made up of unique combination of values of dimension keys
    • ALWAYS contains date or date dimension
  • Fact values should be additive
    • Aggregations of quantities or amounts from atomic level
    • Can not be percentages or ratios
for example1

Customer Location

Selling Responsibility

Cust ZIP Code

Purchases 1

Sales Rep ID

City

State/Province

Country

Sales Rep ID

Product Code

Cust ZIP Code

Customer Type

Week Ending Date

Sales Rep Name

Store ID

Store Name

Store Location

Sales Channel

Customer Type

Customer Type

Days of Activity

Unit Price

Total Quantity

Total Dollars

Returned Qty

Returned Dollars

Promotion Qty

Cust Type Desc

Product

Product Code

Date Information

Product Name

Prod. Category

Product Type

Prod Sub Type

Week Ending Date

Month

Quarter

Year

For Example:
star schema query1
Star Schema Query

Select E.Month, B.Customer_Type, C.Product_Type,

D.Store_Location, sum(A.Total_Quantity)

From Purchases_1 A, Customer_Type B, Product C,

Selling_Responsibility D, Date_Information E

Where B.Customer_Type = A.Customer_Type and

C.Product_Code = A.Product_Code and

D.Sales_Rep_ID = A.Sales_Rep_ID and

E.Week_Ending_Date = A.Week_Ending_Date and

E.Year = “1996” and

C.Product_Category = “V”

Group by E.Month, B.Customer_Type, C.Product_Type,

D.Store_Location;

slide50

Star Schema: basis of MD view

A star schema for credit card purchases

slide51

Example of Star Schema query:

  • Example of three-dimensional query.
  • What is the total amount and number of purchases for vehicles in region 2 for December.

Multidimensional cube for credit card purchases

question
Question
  • Business decisions require the delivery of critical information in a timely, suitable format. Explain, using appropriate examples, how OLAP can facilitate the business decision making process.
  • Discuss how a data ware house can play’s key role in strategic decision making.
  • Discuss, using suitable examples how data mining can contribute to companies making a proactive knowledge driven decisions which could help with formulation of a companies strategy.
question1
Question
  • A data warehouse, a data mining systems and OLAP are 3 important technologies used in facilitating business decision making. using a suitable examples.
    • The star schema is a database schema that can be utilised by all three technologies: Describe, using a simple example, The essential elements of this schema
    • (10 marks)
    • Explain how, such star schemas, can be used by any two of the technologies above technologies to provide information to derive simple business strategies.
    • (20 marks)