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Boiler Insight Data Mart Training – Part 1 Dimensional Modeling 101
Boiler Insight Data Mart Training – Part 1
Dimensional Modeling
101
Objective
Give you an overview of:
• The basic concepts of Dimensional Modeling
•What dimensional models look like in Cognos
• The ways dimensionally modeled data is accessed
• How historical data is handled
• The eight “Key Concepts” of dimensionally modeled data
to give you a good foundation when you attend the data
training
2
It’s all Ralph’s Fault
• Widely regarded as one of the original architects of data warehousing
• Developed a methodology for building the data marts in a data warehouse
• Kimball’s methodology results in a structure for the data that is easy to use and very fast to access
• When following the Kimball methodology you are doing dimensional modeling
Ralph Kimball
3
So what?
All this is great, but why do we care about Ralph and his
methodology?
Kimball’s methodology results in a structure for the data that is easy to use
and very fast to access.
4
Kimball Methodology
Four-Step Dimensional Design Process for a Star Schema as part of
a Data Mart:
1. Select the subject area.
2. Declare the grain of the business process.
3. Choose the dimensions that apply to each fact table row and
define their attributes.
4. Identify the numeric facts that will populate each fact table
row.
5
Kimball Methodology
Four-Step Dimensional Design Process for a Star Schema as part of
a Data Mart:
1. Select the subject area.
2. Declare the grain of the business process.
3. Choose the dimensions that apply to each fact table row and
define their attributes.
4. Identify the numeric facts that will populate each fact table
row.
6
Subject Area & Data Mart
• Subject Area – The data related to one area of the business
• Data Mart – A group of data from a given subject area
specifically structured for access and reporting
The first subject area selected for the Boiler Insight (BI) Data Warehouse
was Human Resources (HR).
So, we built an HR Data Mart.
7
Star Schema (a.k.a. Star)
• A Star Schema is the structure of the data in the data mart
• It is the result of executing the Kimball methodology (doing
dimensional modeling)
• Each Star Schema holds data for a specific subset of the Data
Mart
Employee Actions
Employee Appointment
Employee Leave
Compensation Plan
Payroll Charge
Employee Quota
Examples of star schemas in the HR Data Mart:
8
Star Schema
Fact
Table
Dimension 2 Dimension 5
Dimension 3 Dimension 4
Dimension 1
9
Facts (a.k.a. Measures)
• Facts are the numbers that result from transactions being
performed
Examples:
The amount we pay each employee at each payroll
The number of days the employee is on leave
The count of each employee action that occurs
• Facts are additive (that is, they can be summed)
10
Fact Table
• Facts are grouped into the Fact Table
• Each row usually represents one specific transaction
Fact
Table
Dimension 2 Dimension 5
Dimension 3 Dimension 4
Dimension 1
11
Dimensions
• Dimensions provide the means to filter, group, and label the facts.
Examples:
Employee
Date
Organization
Cost Center
• Dimensions are often used in “by” phrases:
“I want total payroll by Org Unit, by Month.”
Remember: FGL (“figgle”)
12
Dimensions
Fact
Table
Dimension 2 Dimension 5
Dimension 3 Dimension 4
Dimension 1
• There are typically 5-15 dimensions in a Star Schema
• Dimensions are made up of Attributes
13
Attributes
• Attributes are the actual fields in each dimension
Examples:
Employee Dimension:
PERNR
Name
Employee Group
Employment Status
Date Dimension:
Date
Month
Year
Quarter
Fiscal Period
Cost Center Dimension:
Cost Center
Department
Person Responsible
Locked Flag
• There are special attributes that are in the Fact table called Degenerate Dimension attributes
• They are like miscellaneous attributes that don’t fit cleanly in any dimension
• Treat them like any other attribute
14
Grain
• A statement that defines the most atomic (smallest) level for the records in a dimension or fact table.
Examples:
Employee Dimension – One record for each employee in a position
Payroll Charge Fact Table – One record per employee per wage type per payroll period
• The grain for a fact table is often put in terms of the dimensions.
15
Key Concept #1: Understand the Basic Building Blocks
• Data Marts are made up of star schemas
• Stars have one fact table and many dimensions
• The fact table contains facts and links to the dimensions
• Facts are numbers and are additive
• The dimensions contain attributes
• Dimensions (or rather their attributes) are used to filter,
group, and label (“figgle”)
16
HR Data Mart HR Data Mart HR Data Mart
Putting it all together
SAP
Boiler Insight Data Warehouse
Fact
Table
Dimension 2 Dimension 5
Dimension 3 Dimension 4
Dimension 1
Fact
Table
Dimension 2 Dimension 5
Dimension 3 Dimension 4
Dimension 1
17
HR Data Mart HR Data Mart HR Data Mart
Putting it all together
Boiler Insight Data Warehouse
Fact
Table
Dimension 2 Dimension 5
Dimension 3 Dimension 4
Dimension 1
Fact
Table
Dimension 2 Dimension 5
Dimension 3 Dimension 4
Dimension 1
Cognos
18
Cognos
• So how does this all look in Cognos?
Cognos Production
19
Accessing a Data Mart
• There are three primary ways to access data mart:
1. Query one dimension without using the whole star
2. Query a single star
3. Query multiple stars
20
Key Concept #2: Know Which Access You Are Using
• You will always be using one of the three methods:
1. Single dimension
2. Single Star
3. Multiple Stars
• As soon as you are using two dimensions you have moved
from access method 1 to 2 and you are using the relations
to the fact table.
21
Payroll Charge Star
Payroll
Charge
Fact
Table
Posting
Date GL Account
Funds
Center
Fund
Sponsored
Program
Cost
Center
FI Document
Type
Organization
Position
Payroll Run
Info
Wage Type Payroll
Period Grant
Functional
Area
Order
Employee
Commitment
Item Job
22
Role Playing Dimensions
Payroll
Charge
Fact
Table
Fund
HR Fund
Role Playing Dimension
23
Simplified Payroll Charge Star
Payroll
Charge
Fact
Table
Date
Cost
Center Employee
24
Attributes
Employee Dimension:
PERNR
Name
Employee Group
Employment Status
Date Dimension:
Date
Month
Year
Quarter
Fiscal Period
Cost Center Dimension:
Cost Center
Department
Person Responsible
Locked Flag
25
Employee Dimension
PERNR Name Employee
Group
Employment
Status
90025609 Bob Admin/Prof Active
90045001 Sue Faculty Active
90033276 John Clerical Withdrawn
90025640 Mary Admin/Prof Active
90070013 Bill Service Active
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Date Dimension
Date Month Year Quarter Fiscal
Period
7/29/12 7 2012 Q3 1/2013
7/30/12 7 2012 Q3 1/2013
7/31/12 7 2012 Q3 1/2013
8/1/12 8 2012 Q3 2/2013
8/2/12 8 2012 Q3 2/2013
8/3/12 8 2012 Q3 2/2013
8/4/12 8 2012 Q3 2/2013
27
Cost Center Dimension
Cost Center Department Person
Responsible
Locked
Flag
4008007002 4008007 Jose N
4008007003 4008007 Mary N
4090023001 4090023 Jane Y
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Payroll Charge Star
Payroll
Charge
Fact
Table
Date Month Year Quarter Fiscal
Period
7/29/12 7 2012 Q3 1/2013
7/30/12 7 2012 Q3 1/2013
7/31/12 7 2012 Q3 1/2013
8/1/12 8 2012 Q3 2/2013
8/2/12 8 2012 Q3 2/2013
8/3/12 8 2012 Q3 2/2013
8/4/12 8 2012 Q3 2/2013
Cost Center Department Person
Responsible
Locked Flag
4008007002 4008007 Jose N
4008007003 4008007 Mary N
4090023001 4090023 Jane Y
Date
Employee Cost Center PERNR Name Employee
Group
Employment
Status
90025609 Bob Admin/
Prof
Active
90045001 Sue Faculty Active
90033276 John Clerical Withdrawn
90025640 Mary Admin/
Prof
Active
90070013 Bill Service Active
29
Payroll Charge Fact Table
Employee Date Cost Center Payroll
Amount
90025609 7/30/12 4008007002 $750
90045001 7/30/12 4090023001 $934
90033276 7/30/12 4008007003 $235
90025640 7/30/12 4008007003 $1,023
90070013 7/30/12 4008007003 $125
90033276 7/31/12 4008007003 $45
90025609 8/2/12 4008007002 $750
90045001 8/2/12 4090023001 $934
90025640 8/2/12 4008007003 $1,023
90070013 8/2/12 4008007003 $125
Payroll run
Paid
Everyone
Off-cycle
Payroll to
fix
problem
for John John Quits
Payroll run
Paid
Everyone
30
PERNR Name Employee
Group
Employment
Status
90025609 Bob Admin/
Prof
Active
90045001 Sue Faculty Active
90033276 John Clerical Withdrawn
90025640 Mary Admin/
Prof
Active
90070013 Bill Service Active
Payroll Charge Star Date Month Year Quarter Fiscal
Period
7/29/12 7 2012 Q3 1/2013
7/30/12 7 2012 Q3 1/2013
7/31/12 7 2012 Q3 1/2013
8/1/12 8 2012 Q3 2/2013
8/2/12 8 2012 Q3 2/2013
8/3/12 8 2012 Q3 2/2013
8/4/12 8 2012 Q3 2/2013
Cost Center Department Person
Responsible
Locked Flag
4008007002 4008007 Jose N
4008007003 4008007 Mary N
4090023001 4090023 Jane Y
Employee Date Cost Center Payroll
Amount
90025609 7/30/12 4008007002 $750
90045001 7/30/12 4090023001 $934
90033276 7/30/12 4008007003 $235
90025640 7/30/12 4008007003 $1,023
90070013 7/30/12 4008007003 $125
90033276 7/31/12 4008007003 $45
90025609 8/2/12 4008007002 $750
90045001 8/2/12 4090023001 $934
90025640 8/2/12 4008007003 $1,023
90070013 8/2/12 4008007003 $125
Date
Employee Cost Center
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Accessing a Dimension
PERNR Name Employee
Group
Employment
Status
90025609 Bob Admin/Prof Active
90045001 Sue Faculty Active
90033276 John Clerical Withdrawn
90025640 Mary Admin/Prof Active
90070013 Bill Service Active
Question:
Is John an active
employee?
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Accessing a Star Schema Date Month Year Quarter Fiscal
Period
7/29/12 7 2012 Q3 1/2013
7/30/12 7 2012 Q3 1/2013
7/31/12 7 2012 Q3 1/2013
8/1/12 8 2012 Q3 2/2013
8/2/12 8 2012 Q3 2/2013
8/3/12 8 2012 Q3 2/2013
8/4/12 8 2012 Q3 2/2013
Cost Center Department Person
Responsible
Locked Flag
4008007002 4008007 Jose N
4008007003 4008007 Mary N
4090023001 4090023 Jane Y
Employee Date Cost Center Payroll
Amount
90025609 7/30/12 4008007002 $750
90045001 7/30/12 4090023001 $934
90033276 7/30/12 4008007003 $235
90025640 7/30/12 4008007003 $1,023
90070013 7/30/12 4008007003 $125
90033276 7/31/12 4008007003 $45
90025609 8/2/12 4008007002 $750
90045001 8/2/12 4090023001 $934
90025640 8/2/12 4008007003 $1,023
90070013 8/2/12 4008007003 $125
Question:
What is the total
payroll for department
4008007 for July 2012?
Department = 4008007
Month = 7
Year = 2012
Automatically
Summed
Payroll
Depart Month/Year Amount
4008007 7/2012 $2,176
Date
Employee Cost Center PERNR Name Employee
Group
Employment
Status
90025609 Bob Admin/
Prof
Active
90045001 Sue Faculty Active
90033276 John Clerical Withdrawn
90025640 Mary Admin/
Prof
Active
90070013 Bill Service Active
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PERNR Name Employee
Group
Employment
Status
90025609 Bob Admin/
Prof
Active
90045001 Sue Faculty Active
90033276 John Clerical Withdrawn
90025640 Mary Admin/
Prof
Active
90070013 Bill Service Active
Emp. Payroll
Depart Month/Year Name Amount
4008007 7/2012
Bob $750
John $280
Mary $1,023
Bill $125
Total $2,176
Accessing a Star Schema Date Month Year Quarter Fiscal
Period
7/29/12 7 2012 Q3 1/2013
7/30/12 7 2012 Q3 1/2013
7/31/12 7 2012 Q3 1/2013
8/1/12 8 2012 Q3 2/2013
8/2/12 8 2012 Q3 2/2013
8/3/12 8 2012 Q3 2/2013
8/4/12 8 2012 Q3 2/2013
Cost Center Department Person
Responsible
Locked Flag
4008007002 4008007 Jose N
4008007003 4008007 Mary N
4090023001 4090023 Jane Y
Employee Date Cost Center Payroll
Amount
90025609 7/30/12 4008007002 $750
90045001 7/30/12 4090023001 $934
90033276 7/30/12 4008007003 $235
90025640 7/30/12 4008007003 $1,023
90070013 7/30/12 4008007003 $125
90033276 7/31/12 4008007003 $45
90025609 8/2/12 4008007002 $750
90045001 8/2/12 4090023001 $934
90025640 8/2/12 4008007003 $1,023
90070013 8/2/12 4008007003 $125
Question:
What is the total
payroll for department
4008007 for July 2012?
Department = 4008007
Month = 7
Year = 2012
Display by Employee
Name.
Automatically
Sum John’s
Values
Payroll
Depart Month/Year Amount
4008007 7/2012 $2,176
Date
Employee Cost Center
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Key Concept #3: Understand Auto-Aggregation
• Cognos will always auto-aggregate facts for you in
dimensional models
•Only facts aggregate not attributes
• The fields you display in your report/query determine the
aggregation; it will aggregate facts across every
dimension attribute you do not display (after filtering)
• Tip: Filter first and be as narrow as you can
•Wide filters will require Cognos to retrieve and aggregate
a lot of rows
35
Key Concept #4: Understand Semi-Additive Facts
• A semi-additive fact is a fact that cannot be aggregated
for a certain dimension
• You must include at least one attribute from that
dimension in the display of your report/query or filter to
a single value in that dimension
• Noted as “Semi-additive on <dimension name>”
36
Semi-additive Fact Example
• In Employee Appointment, Head Count is semi-additive on
the Summary Month dimension; it cannot be aggregated
across months
• Anytime you use this fact, you must either filter to a
specific month or you must display your data by month
• If you do not display or filter by month, you will count the
same person multiple times and get an incorrect answer
37
History
• History happens naturally in the fact tables
• Changes can also occur that affect the dimensions
PERNR Name Employee
Group
Employment
Status
90025609 Bob Admin/Prof Active
90045001 Sue Faculty Active
90033276 John Clerical Withdrawn
90025640 Mary Admin/Prof Active
90070013 Bill Service Active
In the current version of our
Employee Dimension,
history is lost.
Example: John quit
38
Dimension History
• To keep history for a dimension, you need to create a new
row each time something changes
PERNR Name Employee
Group
Employment
Status
90025609 Bob Admin/Prof Active
90045001 Sue Faculty Active
90033276 John Clerical Active
90033276 John Clerical Withdrawn
90025640 Mary Admin/Prof Active
90070013 Bill Service Active
Now we have the history of
John but with two
problems:
1. We still don’t know when
the change happened
2. We have duplicated the
“key” that is used to link
to the fact table
39
Dimension History
• To fix the problem of knowing when changes happen, we
need to add some dates
PERNR First
Effective
Date
Last
Effective
Date
Name Employee
Group
Employment
Status
90025609 1/1/07 12/31/9999 Bob Admin/Prof Active
90045001 1/1/07 12/31/9999 Sue Faculty Active
90033276 1/1/07 7/31/2012 John Clerical Active
90033276 8/1/12 12/31/9999 John Clerical Withdrawn
90025640 1/1/07 12/31/9999 Mary Admin/Prof Active
90070013 1/1/07 12/31/9999 Bill Service Active
Defines the timeframe for when
the data in given row applies
40
Dimension History
• To fix the problem of duplicate keys we need to add
another column that holds a special key that will never be
duplicated called a Surrogate Identifier (SID)
SID PERNR First
Effective
Date
Last
Effective
Date
Name Employee
Group
Employment
Status
1 90025609 1/1/07 12/31/9999 Bob Admin/Prof Active
2 90045001 1/1/07 12/31/9999 Sue Faculty Active
3 90033276 1/1/07 7/31/2012 John Clerical Active
4 90033276 8/1/12 12/31/9999 John Clerical Withdrawn
5 90025640 1/1/07 12/31/9999 Mary Admin/Prof Active
6 90070013 1/1/07 12/31/9999 Bill Service Active
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Dimension History
• Let’s introduce another change
SID PERNR First
Effective
Date
Last
Effective
Date
Name Employee
Group
Employment
Status
1 90025609 1/1/07 12/31/9999 Bob Admin/Prof Active
2 90045001 1/1/07 8/1/2012 Sue Faculty Active
7 90045001 8/2/12 12/31/9999 Suzie Faculty Active
3 90033276 1/1/07 7/31/2012 John Clerical Active
4 90033276 8/1/12 12/31/9999 John Clerical Withdrawn
5 90025640 1/1/07 12/31/9999 Mary Admin/Prof Active
6 90070013 1/1/07 12/31/9999 Bill Service Active
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Emp. Payroll
Depart Date Name Amount
4090023 7/2012
Sue $934
Total $934
Dimension History Date Month Year Quarter Fiscal
Period
7/29/12 7 2012 Q3 1/2013
7/30/12 7 2012 Q3 1/2013
7/31/12 7 2012 Q3 1/2013
8/1/12 8 2012 Q3 2/2013
8/2/12 8 2012 Q3 2/2013
8/3/12 8 2012 Q3 2/2013
8/4/12 8 2012 Q3 2/2013
Cost Center Department Person
Responsible
Locked Flag
4008007002 4008007 Jose N
4008007003 4008007 Mary N
4090023001 4090023 Jane Y
Employee Date Cost Center Payroll
Amount
1 7/30/12 4008007002 $750
2 7/30/12 4090023001 $934
3 7/30/12 4008007003 $235
5 7/30/12 4008007003 $1,023
6 7/30/12 4008007003 $125
3 7/31/12 4008007003 $45
1 8/2/12 4008007002 $750
7 8/2/12 4090023001 $934
5 8/2/12 4008007003 $1,023
6 8/2/12 4008007003 $125
Question:
What is the total
payroll where:
SID PERNR First
Effective
Date
Last
Effective
Date
Name Employee
Group
Employment
Status
1 90025609 1/1/07 12/31/9999 Bob Admin/Prof Active
2 90045001 1/1/07 8/1/2012 Sue Faculty Active
7 90045001 8/2/12 12/31/9999 Suzie Faculty Active
3 90033276 1/1/07 7/31/2012 John Clerical Active
4 90033276 8/1/12 12/31/9999 John Clerical Withdrawn
5 90025640 1/1/07 12/31/9999 Mary Admin/Prof Active
6 90070013 1/1/07 12/31/9999 Bill Service Active
Emp. Payroll
Depart Date Name Amount
4090023 8/2012
Suzie $934
Total $934
Department = 4090023
Month = August
Year = 2012
Date
Employee Cost Center
43
Emp. Payroll
Depart Date Name Amount
4090023 7/2012
Sue $934
Total $934
Dimension History Date Month Year Quarter Fiscal
Period
7/29/12 7 2012 Q3 1/2013
7/30/12 7 2012 Q3 1/2013
7/31/12 7 2012 Q3 1/2013
8/1/12 8 2012 Q3 2/2013
8/2/12 8 2012 Q3 2/2013
8/3/12 8 2012 Q3 2/2013
8/4/12 8 2012 Q3 2/2013
Cost Center Department Person
Responsible
Locked Flag
4008007002 4008007 Jose N
4008007003 4008007 Mary N
4090023001 4090023 Jane Y
Employee Date Cost Center Payroll
Amount
1 7/30/12 4008007002 $750
2 7/30/12 4090023001 $934
3 7/30/12 4008007003 $235
5 7/30/12 4008007003 $1,023
6 7/30/12 4008007003 $125
3 7/31/12 4008007003 $45
1 8/2/12 4008007002 $750
7 8/2/12 4090023001 $934
5 8/2/12 4008007003 $1,023
6 8/2/12 4008007003 $125
Question:
What is the total
payroll where:
Department = 4090023
Month = July
Year = 2012
SID PERNR First
Effective
Date
Last
Effective
Date
Name Employee
Group
Employment
Status
1 90025609 1/1/07 12/31/9999 Bob Admin/Prof Active
2 90045001 1/1/07 8/1/2012 Sue Faculty Active
7 90045001 8/2/12 12/31/9999 Suzie Faculty Active
3 90033276 1/1/07 7/31/2012 John Clerical Active
4 90033276 8/1/12 12/31/9999 John Clerical Withdrawn
5 90025640 1/1/07 12/31/9999 Mary Admin/Prof Active
6 90070013 1/1/07 12/31/9999 Bill Service Active
Emp. Payroll
Depart Date Name Amount
4090023 7/2012
Sue $934
4090023 8/2012
Suzie $934
Total $1868
Department = 4090023
Month = July - August
Year = 2012
Date
Employee Cost Center
44
Dimension History
• A dimension in which you are keeping track of the changes
of its attributes is called a Slowly Changing Dimension
• You decide for each attribute in the dimension whether it
is slowly changing or not
• There are four types of slowly changing dimensions:
Type 1
Type 2
Type 3
Type 6
45
SCD – Type 1
•Only keep the current value
PERNR Name Employee
Group
Employment
Status
90025609 Bob Admin/Prof Active
90045001 Sue Faculty Active
90033276 John Clerical Withdrawn
90025640 Mary Admin/Prof Active
90070013 Bill Service Active
Advantage: Simple
Disadvantage: Lose history
46
SCD – Type 2
• Keep all historic values
SID PERNR First
Effective
Date
Last
Effective
Date
Name Employee
Group
Employment
Status
1 90025609 1/1/07 12/31/9999 Bob Admin/Prof Active
2 90045001 1/1/07 8/1/2012 Sue Faculty Active
7 90045001 8/2/12 12/31/9999 Suzie Faculty Active
3 90033276 1/1/07 7/31/2012 John Clerical Active
4 90033276 8/1/12 12/31/9999 John Clerical Withdrawn
5 90025640 1/1/07 12/31/9999 Mary Admin/Prof Active
6 90070013 1/1/07 12/31/9999 Bill Service Active
Advantage: Can see history and partition historic facts
Disadvantage: Can’t associate new values with historic facts
47
SCD – Type 3
• Keep one historic value in another attribute
PERNR Name Previous
Name
Employee
Group
Employment
Status
90025609 Bob Bob Admin/Prof Active
90045001 Suzie Sue Faculty Active
90033276 John John Clerical Withdrawn
90025640 Mary Mary Admin/Prof Active
90070013 Bill Bill Service Active
Advantage: Can associate the current or previous value with facts
Disadvantage: Still lose history (only one previous value)
48
SCD – Type 6
• Combination of Types 1, 2 & 3
Advantage: Full history and can associate the current value with all facts
Disadvantage: Very Complex
SID PERNR First
Effective
Date
Last
Effective
Date
Name Current
Name
Employee
Group
Employment
Status
1 90025609 1/1/07 12/31/9999 Bob Bob Admin/Prof Active
2 90045001 1/1/07 8/1/2012 Sue Suzie Faculty Active
7 90045001 8/2/12 12/31/9999 Suzie Suzie Faculty Active
3 90033276 1/1/07 7/31/2012 John John Clerical Active
4 90033276 8/1/12 12/31/9999 John John Clerical Withdrawn
5 90025640 1/1/07 12/31/9999 Mary Mary Admin/Prof Active
6 90070013 1/1/07 12/31/9999 Bill Bill Service Active
49
Accessing a Dimension (Revisited)
Question:
SID PERNR First
Effective
Date
Last
Effective
Date
Name Employee
Group
Employment
Status
1 90025609 1/1/07 12/31/9999 Bob Admin/Prof Active
2 90045001 1/1/07 8/1/2012 Sue Faculty Active
7 90045001 8/2/12 12/31/9999 Suzie Faculty Active
3 90033276 1/1/07 7/31/2012 John Clerical Active
4 90033276 8/1/12 12/31/9999 John Clerical Withdrawn
5 90025640 1/1/07 12/31/9999 Mary Admin/Prof Active
6 90070013 1/1/07 12/31/9999 Bill Service Active
Is John an active
employee as of today?
For a dimension with type 2 or 6
attributes, you must include some
date component in your query!
50
Accessing a Dimension (Revisited)
SID PERNR First
Effective
Date
Last
Effective
Date
Name Employee
Group
Employment
Status
Current
Record
Flag
1 90025609 1/1/07 12/31/9999 Bob Admin/Prof Active Y
2 90045001 1/1/07 8/1/2012 Sue Faculty Active N
7 90045001 8/2/12 12/31/9999 Suzie Faculty Active Y
3 90033276 1/1/07 7/31/2012 John Clerical Active N
4 90033276 8/1/12 12/31/9999 John Clerical Withdrawn Y
5 90025640 1/1/07 12/31/9999 Mary Admin/Prof Active Y
6 90070013 1/1/07 12/31/9999 Bill Service Active Y
51
Key Concept #5: Use the Correct Dates
SID PERNR First
Effective
Date
Last
Effective
Date
Name Employee
Group
Employment
Status
1 90025609 1/1/07 12/31/9999 Bob Admin/Prof Active
2 90045001 1/1/07 8/1/2012 Sue Faculty Active
7 90045001 8/2/12 12/31/9999 Suzie Faculty Active
3 90033276 1/1/07 7/31/2012 John Clerical Active
4 90033276 8/1/12 12/31/9999 John Clerical Withdrawn
5 90025640 1/1/07 12/31/9999 Mary Admin/Prof Active
6 90070013 1/1/07 12/31/9999 Bill Service Active
Payroll
Charge
Fact
Table
Date
Cost
Center Employee
When querying against a dimension
use First and Last Effective Dates (or Current Record Flag)
When querying against a
Star use one of the Date
Dimensions
52
Accessing Multiple Stars (a.k.a. cross-star query)
Payroll
Charge
Fact
Table
Date
Cost Center Employee
Employee
Leave
Date
Employee Leave Type
53
Accessing Multiple Stars
Payroll
Charge
Fact
Table
Cost Center Employee
Employee
Leave
Date
Leave Type
Conformed Dimensions
54
Accessing Multiple Stars
Payroll
Charge
Fact
Table
Cost Center
Employee
Leave
Leave Type Employee
Date
Emp. Payroll
Depart Date Leave Name Amount
4090023 8/2012 Sabbatical Suzie $934
55
Key Concept #6: Understand Conformed Dimensions
• A conformed dimension is a dimension used in more than
one star
• To do a cross-star query, the stars must share a conformed
dimension
56
Key Concept #7: Understand the
Dimensionality of Facts
•When doing cross-star queries, a fact cannot be
aggregated on attribute values for a dimension it does not
have.
• If you try to do this, Cognos will simply replicate the
values for the facts aggregated on the dimensions it does
have.
57
Dimensionality of Facts
Payroll
Charge
Fact
Table
Cost Center
Employee
Leave
Leave Type Employee
Date
Emp. Payroll
Depart Date Leave Name Amount
4090023 8/2012 Sabatical Suzie $934
58
Dimensionality of Facts
Emp. Payroll
Depart Date Leave Name Amount
4090023 8/2012 Sabbatical Suzie $934
59
Dimensionality of Facts
Emp. Payroll
Depart Date Leave Name Amount
4090023 8/2012 Sabbatical Suzie $934
4090023 8/2012 Sick Suzie $934
60
Key Concept #8: Understand Subset Conformed Dimensions
• Two dimensions can be treated as a single conformed
dimension provided one dimension is a complete subset of
the other
• For example, the Month Year dimension is a complete
subset of the Date Dimension
• If one star has the full dimension and another star has the
subset dimension, you can do a cross-star query
• But, the grain of the query can be no more detailed than
the grain of the subset dimension
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Subset Dimensions
Payroll
Charge Fact
Table
Compensation
Plan Fact
Table
Date
Month Year
Cross star query possible, but the date grain
can be no more detailed than monthly totals.
62
Metadata
•Metadata = Data about data
For Example:
• Field Type (number, date, character, etc.)
• Length
• Definition
• Appropriate Usage
• Source of the Data
• There is a complete Metadata Repository for the data marts.
• It is a star that can be queried using Cognos.
• It will be covered in part 2 of the data training.
63
Security
• The data marts have a comprehensive security model
• There are three areas that are secured:
1. Cognos tool access (licenses)
2. Cognos content
3. Data Access
64
Cognos Tool Access Report Studio Query Studio
Business Insight
Advanced
Report Viewer
Professional Advanced Business Author Enhanced Consumer
Business Insight
License Type:
Super User Power User End User User Type:
65
Cognos Content Security
66
Cognos Content Security
Boiler Insight
Standard Content
Packages
Shared Content
Departmental Content
Location for “approved” Content Read-only for all
Write access for select few
Access controlled centrally
Private location for an area to
use for their own content Write access for that area
Access controlled by that area
Location for all packages Read-only for all
Write access for IT only
Access controlled centrally
Can be used for sharing BI
content with other users Write access for all
Access controlled centrally
Public Folders
67
Data Access Security
• Entire Data Mart
• Entire Star Schema
• Entire Dimension
• Specific Field(Attributes or Facts)
• Specific Records
More difficult
to implement
And negatively
impacts
performance!
68
Data Access Security
Level1 Level2 Level3
Least Restrictive Most Restrictive
HR Payroll Charge Fact Table
Date
Cost Center
Employee Age Gender Ethnicity DOB
69
Data Access Security
•We ended up with three subject
areas:
1. HR
2. OIE
3. IAMO
70
Data Access Security
Level1 Level2 Level3
Least Restrictive Most Restrictive
HR
Age Ethnicity DOB
OIE Disability
Career Account
Email Address
Employee
Gender
Highest Degree
PUID Visa
I9 Info Minority
Race Veteran
IAMO
71
Summary
You now know:
•General structure of dimensionally modeled data: data
marts, stars, dimensions, facts, etc.
•What dimensional modeled data looks like in Cognos
• The three ways to access data from a data mart:
• Single Dimension
• Star
• Multiple-stars
• How history is handled and the four types of slowly-
changing dimensions: Types 1,2,3,6
72
Summary (cont.)
You now know:
• The eight “Key Concepts” of dimensionally modeled data:
1. Understand the basic building blocks
2. Understand which access method you are using
3. Understand auto-aggregation
4. Understand semi-additive facts
5. Use the correct dates
6. Understand conformed dimensions
7. Understand the dimensionality of facts
8. Understand subset conformed dimensions
73
You now know:
•What metadata is
• The three areas of security:
1. Tool Access
2. Cognos Content Access
3. Data Access
Summary (cont.)
74
Questions?
75
