This portfolio showcases the author's work during the ten-week Business Intelligence Masters program at SetFocus, covering comprehensive Microsoft BI product components including SSIS, SSRS, and SSAS. It outlines the program's objectives such as creating a dimensional data warehouse, generating reports, and developing OLAP databases, along with detailed processes for data integration, cleansing, and reporting. The document includes practical demonstrations of building ETL processes, designing interactive reports, and creating analysis services databases using SQL Server tools.

2. This portfolio showcases the work I performed
during my participation in the Business Intelligence
Masters program from SetFocus. The BI Masters
program is an intensive ten week, project-oriented,
mentored program with top industry experts.
The program modules were designed to reach deeply
into each component of the Microsoft BI product
set. The slides that follow are presented in a
tutorial spirit and constitute but a sampling of the
work performed.
John Paredes

3. Contents
• Integration Services
• Reporting Services
• Analysis Services
• SharePoint/PerformancePoint

4. Product Components Used
• SQL Server Management Studio (SSMS)
• Business Intelligence Development Studio (BIDS)
– Integration Services (SSIS)
– Reporting Services (SSRS)
– Analysis Services (SSAS)
• Microsoft Excel
• PerformancePoint Server
• SharePoint Server

5. Program Objectives Overview
• Use SSMS/T-SQL to create a dimensional data warehouse
• Create packages in SSIS to import data from disparate
sources, perform ETL functions, load the data warehouse,
and perform database maintenance functions
• Define an OLAP database using SSAS and populate it with
data from the data warehouse. Use MDX expressions to
create calculated measures and Key Performance Indicators
(KPI)

6. Program Objectives Overview 2
• Using SSRS, create reports from both relational and
multidimensional data sources. Incorporate advanced report
features such as data grouping, on-the-fly calculations, and data-
driven formatting
• Create reports using the other Microsoft BI platforms: Excel
Pivot Tables, Performance Point, SharePoint
• Create Dashboards (organized presentations of KPI’s and
reports) and deploy them on SharePoint

7. Integration Services
Microsoft SSIS

8. What is SSIS?
Part of the Business Intelligence Development Studio
• Microsoft’s tool for performing a broad range of data migration tasks
including common ETL tasks such as data cleansing and data
transformations
• Can be used to load data from disparate sources into a SQL Server data
warehouse or from a SQL Server database into an Analysis Services
Database (OLAP)
• SSIS applications are built as a series of pre-built packages
– Wide assortment of pre-built packages
– Graphical representation
• SSIS applications have a block-diagram graphical representation making
them “self-documenting”

9. Project Features
• Loading Applicant data from disparate sources
• Data cleansing using the fuzzy grouping
transformation

10. An Illustration of Data Cleansing
The dimensional data in the Applicant table had inconsistencies in the way the data was
entered. Some types of inconsistencies could be corrected by a view that implemented
simple fixes. In the case of the account manager field, a more sophisticated approach
utilizing the fuzzy grouping transformation was used to get the required consistency.
To illustrate its operation, we note that, for example, the data for “MMcConnaug” was
entered in three different variations. Because the number of account managers was
small and the differences between them big enough, it was easy for the Fuzzy Grouping
transformation to identify the inconsistencies and choose a consistent representation.
The account manager dimension table had a simple structure that is described via this
sql statement:
CREATE TABLE [dbo].[dimAcctMgr](
[AcctMgrPK] [int] IDENTITY(1,1) NOT NULL,
[AcctMgr] [varchar](20) NOT NULL,
CONSTRAINT [dimAcctMgr_PK] PRIMARY KEY CLUSTERED

11. An SSIS package was created to populate dimAcctMgr .
The screenshot below shows the data flow task used by
that package.

12. The Data Flow Task Used to Populate the Account
Manager Dimension Table
The OLE DB source transformation uses the
SQL command data access mode to read from
the view that removes nulls and blanks from
the source table.
The statement used is:
SELECT distinct AcctMgr
FROM DimAccount
Order by AcctMgr
The output of the OLE DB Source
transformation is fed to the Fuzzy Grouping
transformation. The figure to the right shows
its output as seen from a data reader.
SSIS lets you create data viewers to show what
is happening inside a package. I created a
data viewer to show what was going on inside
of the Fuzzy Lookup transformation.

13. Output from the Fuzzy Grouping
Transformation
From the display you can see
that the transformation has
selected a representation for
and has correctly associated the
variations with it.
Next, an Aggregate
transformation is used to reduce
the output data set to its unique
values of account manager.
Finally, the Look and
Conditional Split
transformations are used to
ensure only new data is added
to the table.

14. Loading the Fact Table
This application has a single fact table, a “factless” fact table; all measures are counts.
The process of loading the fact table consists of cycling through the Applicant table and
populating a fact table with surrogate keys for each of the base dimensions. A
degenerate key was also added.
Each column of data from the data view is matched with its value in the corresponding
dimension table. Using this match, the value of the integer key is read and is output as a
column of the same name but prefixed with the word fact.
Because the view does not perform the data cleansing for the account manager field,
there will be some cases where there is no match for that field. Those data items will not
have a PK.
The conditional split transformation near the end of this procedure identifies such rows
and directs them to the Fuzzy Lookup transform. This transform translates the text data
into its standard representation after which an analogous process produces the key.

15. Loading the Fact table diagram

16. Reporting Services
Microsoft SSRS
16

17. Reporting Services Overview
• Included as an install option of SQL Server
• Sophisticated, interactive reports can be created with Report
Designer
• Report definition information stored in RDL, an XML-
based language
• Standalone Report Server comes with SQL Server
• Users can interact directly with Report Server or use Report
Manager to view, subscribe to, and manage reports
• Reports can be generated in a variety of formats
17

18. Steps for Creating a Report In SSRS
1. Create a Reporting Services project in the Business
Intelligence Development Studio (BIDS) or Visual Studio
2. Create a Data Source
3. Design the Report
4. Deploy Report to a Report Server so it can be viewed from a
web browser
18

19. 1. Open BIDS and create a new Report Server
Project. Give its name and location.
19

20. 2. Create the Data Source
A data source can be
associated with an
individual report or with
the entire SSRS project.
We will create a shared
data source, which is
associated with the
project.
You can choose from a
variety of different sources
including SQL Server,
Analysis Services, Essbase,
Oracle, and XML. In this
example we will connect to
a SQL Server database.
20

21. 3. Enter Connection Properties
In this panel
you specify the
server, the
method of
authentication,
and the
database –
everything the
system needs to
know to
connect to the
data source.
21

22. With the data source defined, you are ready to
create the report…
Begin by selecting Project/Add New
Item…
Then choose Report and give your
report a name. This creates an XML
file (it has a .rdl extension) that will
save the report information you
enter.
22

23. Designing a report consists of these steps:
• Create a Data Set
– To tell what data you need
• Define the Layout
– To tell where on the page to place the data elements
• Preview the Report
– To make sure it’s good
23

24. First Stop: the Data tab
On the Data tab of the
report designer, click
on the Dataset drop-
down list and select
<New Dataset…>.
This opens the Dataset
dialog box.
You are going to name the data set, then you need to create a query that retrieves the
data that will be used in the report. There is a choice of two interfaces for describing the
data you want:
•Graphical Query Designer
•Generic Query Designer
This illustration is going to use the graphical query designer.
24

25. After clicking <New Dataset…> the dataset dialog
box appears. Enter the dataset name and press OK.
25

26. Unpress the Generic Query Designer toggle button that
is to the left of the exclamation mark. This will bring
you to the graphical query designer (shown below.)
26

27. Right-click the diagram (top) pane of the query designer
and select Add Table from the popup menu. Select the
tables you want and press Add.
27

28. After you’ve added the tables, you’ll get a panel like this
where you choose data columns and set the data
orderings.
28

29. Next Stop: The Layout Tab
With the dataset defined, the Datasets pane displays an item for each data
column. Click the Layout tab. You can place Report Items such as data
tables or charts on the page. Here we will place a table item.
29

30. Now place data items on the table. Below you see that
all four data items have been dragged over to the detail
row of the table region.
30

31. The design surface can have more than one report
item on it. Here an image and text box has been
dragged on to complete the report design.
31

32. Final Stop: Preview tab
With the design complete push the Preview tab to see the report as it would
appear to the end user.
32

33. Deploying A Report
• Deploying means sending the report information to a web
server so the report can be viewed by the user in a web
browser
• From the web browser the report can be viewed and also
can be downloaded in various formats: pdf, XML,CSV,
TIFF, Web Archive, and Excel.
• Steps for Deploying a project
– Select Solutions Configuration
– Set TargetServerURL in ProjectName Properties
– Deploy the project from the Build menu
33

34. Adding Calculations
34

35. SSRS Calculations
• SSRS Can display information that is not explicitly
represented in the data source via calculations
• Calculations are expressions that are evaluated at report
display time
• Calculations can be used to provide new report information
or to provide data driven formatting
35

36. I will be demonstrating these examples:
• Truncate a long text expression to make it fit in the reporting
space
• Show how calculations can access global variable values and,
using the concatenation operator, create a text expression to
display
• Create data driven formatting expressions. Data values will be
used to determine font properties such as color and weight
36

37. To create a calculation, right
click in the data field and
select Expression… in the pop-
up menu.
A dialog box will appear into
which a formula expression is
typed.
37

38. The expression entered truncates the data of the
pr_info field to its first 50 characters.
38

39. In the last example, the calculation was created from one of the report data fields. In
this next example, we will create a calculation starting from a blank text box. The data
that will be displayed will come from global variables. As with the previous example, start
by right clicking in the text box and choose Expression...
39

40. The expression shown uses the concatenation operator
and several global variables to build the text string that
will be displayed.
40

41. Text formatting can be accomplished via static property
values or with dynamic, data-driven values using
expressions.
41

42. Let’s use an expression to define formatting. From the
property sheet of the data field select a color and click
<Expression…>
42

43. If the product of price and quantity is greater than 500,
the data will appear in red, otherwise, it will be black.
43

44. I have also entered an expression to affect the font
weight. After these two data driven fields have been
defined the properties sheet looks as shown below.
44

45. With the formatting complete, click on the Preview
tab to see the report.
45

46. Organizing Data by Groups
46

47. In SRSS, Groups…
• Provide a structure for organizing the data
presentation
• Cluster data together based on a common attribute,
for example, all orders from the same company
• Provide a mechanism for giving hierarchical data
presentations, each grouping variable corresponding to
a hierarchy level
47

48. To create a group, right-click on the header and choose
Insert Group…
48

49. In the Grouping and Sorting Properties dialog box that appears,
insert an expression to define what constitutes a group. Here I use
company name. With this, all purchases from a given company
will appear together.
49

50. Here you can see the new rows introduced by the group,
and the grouping variable, CompanyName.Value
50

51. There can be multiple levels of grouping. Click in the
heading row of the first group to add another grouping
variable. I will add a grouping based on time.
51

52. The grouping variable can be a data field or an
expression you create. I will create one based on time.
Start by choosing <Expression…>
52

53. The expression built uses two data fields and two
functions.
53

54. Set grouping and sorting properties
54

55. This is what the report layout looks like with
both groups defined.
55

56. Preview the final report to see it is grouped on
company and month.
56

57. Analysis Services
Microsoft SSAS

58. Analysis Services Overview
Part of the Business Intelligence Development Studio
• Included with the SQL Server License
• Special version of Visual Studio
• Microsoft’s application for creating multidimensional OLAP databases
which are queried with the MDX language
• Microsoft’s powerful data mining platform. Includes sophisticated
algorithms that can operate on relational or OLAP data
• This presentation demonstrates the creation of an OLAP database, also
called a cube
58

59. Why Store Data in a Cube?
Analysis services, like the SQL Server relational database, is a platform for storing data.
But unlike the relational database, it does not store data in tables–it is stored in other
types of structures comprising a cube.
Why store data in cubes instead of tables? There are a number of reasons:
• better query performance
• fast, optimized aggregations calculations
• more efficient storage of data through its simplified read-oriented design
• richer calculation possibilities, supporting stored measures,
calculated measures, and key performance indicators (KPI)
• an easier to understand data model for the end user
OLAP data is engineered to provide dimensional data views, hierarchical browsing,
attribute-based breakouts and filtering. And OLAP data does not require or even use
table joins!
59

60. Creating an Analysis Services (OLAP)
Database
Creating an analysis services database consists of creating the database
structure, then populating those structures with data from external data
sources. The structures comprising an OLAP cube include:
• Dimensions and their associated elements
– Hierarchies
– Attributes
• Measures and their associated elements
– Stored Measures
– Calculated Measures
– Measure Groups
– Key Performance Measures (KPI)
– Measure Profiles
60

61. Development Steps
1. Create Analysis Services Project
2. Create Data Source
3. Create Data Source View
4. Create cube object definitions
5. Deploy definitions to OLAP server and load
in data
6. Specify partitions and aggregations
61

62. Open BIDS and create a new Analysis
Services Project…
62

63. Create a data source and a data source
view
In the data source you identify the relational data
warehouse (DW) that hosts the source data and
provide the connection information.
In the data source view, you specify which the tables in
the DW you want to use to supply data to the cube.
If the DW has been designed using the classic star
schema or snowflake approaches, the SSAS cube
wizard can examine the DW’s structure and derive a
cube definition from it, making your job easier.
When the initial design from the wizard is complete
you can go back and make modifications, for example,
you can add or modify hierarchies and attribute
relationships,.
Once you have the cube design you want, you load the
data in.
63

64. In this illustration, all tables from the star schema DW are used, only the
sysdiagrams table (which holds the database diagram data) remains unselected.
64

65. Once you have defined the data source view, you can inspect the schema. By
right clicking on a table, you can browse the data and define derived columns.
But at this point, we still have no OLAP database. The creation of the OLAP
database begins with creating a cube.
65

66. Creating the Cube Structure
1. With the data source view in place, you are now ready to
create the cube. Right click on the cubes folder and select
“New Cube.” This launches the cube wizard.
2. Make sure the wizard has
correctly identified which are
dimension and which are fact tables
and tell it which table contains the
data for the time dimension.
66

67. Setting the Time Dimension
Parameters
Time is a unique dimension with
inherent assumptions about how it
should work.
You identify the time dimension as
such so that the MDX functions
(such as PrevMember and
ParallelPeriod) specific to it will
work.
You also tell which of the source
data columns map to well known
time concepts such as years,
quarters, and months.
67

68. After defining the time
properties, the wizard displays
the measures that it defined.
Here you can select which ones
you want to keep. I am keeping
all of them in this illustration.
The “Fact Units Count”
measure counts the number of
records in the source table. This
information is used in
optimizing the aggregation
process.
In the Review New Dimensions
panel the dimensions that were
created along with their
hierarchies and attributes are
displayed.
68

69. A cube has now been defined. This panel lets you review the data model (UDM)
that has been created.
69

70. Completing the wizard, you give the cube a name.
70

71. The solution browser on the right side of the screen now shows the cube and
dimensions that were defined. These objects can be modified by clicking on
them.
71

72. Click on the Product dimension to observe that no hierarchy was by the wizard.
I will create a hierarchy for it.
72

73. A new hierarchy is created by dragging the an aggregate attribute into the center panel. I use
Category Code and Dim Product to form the hierarchy; Category and Item provide the labels the
user will see (they are mapped to the name Property).
Next I define attributes between these hierarchy levels. This is so the aggregation process adds the
data from the level directly beneath it instead of always going to the leaf level, which would be a less
efficient process entailing significantly more calculations.
73

74. In the case of the time dimension,
there is an additional step. We want
the months to display in
chronological order, not alphabetical
order, so we assign a value to the
dimension’s OrderByAttribute
property. The data source has a
column called CHRON_ORDER
that contains this ordering
information.
74

75. The information entered to define the hierarchies and attributes is stored in
XML files. The database doesn’t actually know what you have done yet. You
must “process” the dimension. This brings that information into the OLAP
database, which it then uses to create its internal structures.
75

76. Once a dimension has been processed, you can inspect it in the browser pane to
verify the hierarchy has the expected structure. Note that the months show in
chronological order.
76

77. After all the elements have been defined and the cube has been
completely processed, you can inspect the data in the SSAS data
browser.
77

78. Partitioning and Pre-Aggregating

79. The cube was and created. The leaf level data from the data warehouse was loaded. We inspected the
data in the multidimensional data browser.
We can tweak the physical design of the cube to improve scalability and query performance. Three
primary mechanisms for doing this are:
•Selecting ROLAP/HOLAP/MOLAP data storage options
•Partitioning
•Pre-aggregating
MOLAP is probably the most commonly used data storage option and is the default. It means all the
data will be stored in the multidimensional data cube. The illustration one the succeeding slides will
used MOLAP.
At the opposite end of the spectrum, is ROLAP where all data comes from relational data tables. With
ROLAP, the SSAS database is only providing metadata structures for presenting information in the
dimensional style. You can expect performance to be much slower. This mode is used in situations
where the source data is not static, changes frequently and you want the reports to reflect those changes
immediately.
HOLAP is a hybrid approach where all the aggregate data is in the SSAS database except the leaf level
data which resides in relational tables.
When the volume of data is very large, it can helpful to chop the data store into pieces, or partitions.
Storage mode is selectable per partition. This example uses a small amount of data and only one
partition will be employed.
79

80. Aggregates are summary level data that are computed from the leaf level data that was loaded
from the source. Often the aggregates are totals and subtotals, but other summary statistics such
as averages or maximum values can also be used.
Pre-calculating and storing the aggregate values normally improves query performance (at the
cost of the storage space and time required to compute them.) The default is to do no pre-
aggregates. You can see this from the partitions panel shown below.
The data display shown earlier from the SSAS data browser included many aggregate data
values. Those aggregates were all calculated on the fly.
You can pre-calculate all aggregates or only some of them. If you are going to pre-calculate only
some, there are different strategies that can be employed to determined which are chosen for
calculation. You’ll see this ahead.
80

81. Let’s go through the aggregations process. Click the “Design Aggregations”
hyperlink to bring up the wizard. In the first panel of the wizard, push the count
button to compute the statistics that are used to drive the aggregation
optimization process.
81

82. After a few seconds, the source
data has been analyzed. It
counts the number of records
per partition.
Once the statistics have been
computed, you can ask the system to
identify a set of aggregations to
perform. You can direct the system
to aggregate until A) a certain
amount of storage has been used, B)
a certain level of performance gain
has been achieved, C) you click stop,
or D) don’t do anything
In this illustration, I am asking it to
aggregate until it reaches a
performance gain of 75%. The
system will run an optimization
algorithm to determine the best ones
to use.
82

83. The system generates a chart telling
what percent (of the total possible
number of) aggregations it has
identified and what level of
performance gain would be
achieved by computing them.
At the completion of that step, the
wizard has identified which
aggregations to compute. You may
elect to have it compute them now
or you can defer the calculations till
later. (They could take a while.)
83

84. Selecting “Deploy and Process
now” and pushing Finish, you
arrive at this screen.
Push the RUN button to
perform the calculations.
When it finishes, you get
a message heralding the
successful completion of
the deployment.
The information under
the Aggregations tab will
be updated.
84

85. Creating
Derived Measures and KPIs

86. Different Kinds of Reporting Data
Thus far, all the measures that have been constructed have been displays of
stored data or aggregates either stored or calculated on the fly. There are
other kinds information that can be made available to an end user.
• Calculated measures
– Percents
– Shares
– Differences
• Key Performance Indicators (KPIs)
Calculated measures are calculated on the fly using MDX expressions. KPIs
are measures with associated goals and graphics. I will show an example of
both.
86

87. In this example, I create a calculated measure that gives difference between the
data value at a given time and its value the previous time period. The
calculation is defined from the Calculations tab. It is given a name and an
MDX expression. In this example I make use of the PrevMember function.
87

88. Displaying the Units measure and the Units Increase measure side-by-side
demonstrates that the calculated measure correctly computes the difference
between the current value and the one a month ago.
In the next series of slides I will use this calculated measure to construct a
KPI.
88

89. What is a Key Performance Indicator? (KPI)
Every KPI starts life off as a measure, presumably, a measure that is an indicator of company
performance. With each KPI, we assume that the company has established a target value – goal
– of what that indicator should be. For instance, sales revenue might be a performance
indicator. The goal might be to sell at least $100,000 in a given quarter.
The KPI will calculate the difference between the goal and the actual result. We assume the
company can assess those differences declaring them as either good, so-so, or bad. For instance,
the company may say, revenue > 100,000 is good, 90,000 to 100,000 is so-so, and revenue less
than 90,000 is bad.
This brings us to an essential distinguishing feature of the KPI: a graphical icon, known as an
indicator that is displayed to communicate the status of the KPI to the end user. That graphic
might be a happy face to show good, a neutral face to show so-so, and a frowning face to show
bad. Traffic lights with green, amber, and red are often used. The choice of graphics is up to the
client.
Setting up a KPI in Analysis Services entails computing a value of status. The difference between
the indicator and the goal is calculated, and the differences that are “good” are mapped to the
number 1, so-so to 0 and bad to -1. That number is the KPI’s status.
Optionally, you can define a trend for the KPI. The trend shows if, over time, the performance
measure has been moving upwards or downwards.
89

90. KPI Summary
• Begin with a measure indicating company performance
• Have goals associated with that performance measure
• Translate the difference between performance and goal into
its status with values of 1, 0, -1 (corresponding to good, so-so,
and bad.)
• Display the status of the performance measure to the user as a
graphic
90

91. You define KPIs from the KPI tab of the Cube browser. In this simple
illustration, our calculated measure, “Units difference” is the performance
indicator, and the goal is a constant value of 180. MDX expressions can be
used to provide more complex goal statements.
91

92. Once you have defined the KPI, it may be inspected in the browser tab of the
KPI tab. Here you see the performance metric has a value of 179, just under
the target value. This is “so-so” and you see the neutral face showing.
92

93. SharePoint Server
and
PerformancePoint
Server

94. Overview
• SharePoint Server 2007 and Performance Point Server 2007 are
both part of the Microsoft Office server suite of products, not
Business Intelligence Development Studio
• SharePoint server is a content management system with a
multiplicity of goals including the ability to provision reports and
analytic content created in Business Intelligence Development
Studio and PerformancePoint
• PerformancePoint includes monitoring, analytics, and planning
functionality. Included in the product is Dashboard Designer, an
application for formatting analytic content (including reports,
charts, KPI’s and scorecards) into displays called dashboards

95. DashboardDesigner Content
Development
• Can import measures, KPIs, and dimensional elements from
Analysis Services
• Can Import reports and charts from Reporting Services and Excel
• KPIs are organized and formatted into scorecards
• Scorecards and reports, together with filters and inter-item links, are
formatted into dashboards
• Dashboards are what the end user will see
• Dashboards can be displayed from Dashboard Server or SharePoint
Server

96. Sample Reports from My
SharePoint BI Site developed
at SetFocus