BI/DW 101Introduction to Business Intelligence at Guaranty Bank

Erik Okerholm, Business Intelligence

• Business Intelligence Overview

• Data Flow, Data Availability/SLAs

• BI at Guaranty Bank

– Query/Report Examples

• Terminology and Concepts (Modeling, Dim/Fact)

• Current Environment

• BI Future

• Q & A

2

Agenda

Multiple Sources Were Leveraged To Gather

Information For This Presentation

3

What is Business Intelligence?

4

“Business Intelligence is actually an environment in which business

users receive data that is reliable, consistent, understandable, easily

manipulated and timely. With this data, business users are able to

conduct analyses that yield overall understanding of where the business

has been, where it is now and where it will be in the near future.

Business Intelligence serves two main purposes:

1. It monitors the financial and operational health of the organization

(reports, alerts, alarms, analysis tools, key performance indicators

and dashboards).

2. It also regulates the operation of the organization providing two-

way integration with operational systems and information feedback

analysis.”

Source: DM Review

What is Business Intelligence?

5

The discipline of understanding the business abstractly

and often from a distance.

With business intelligence, you can see the forest and the trees

BI Reporting Areas

Deposit

Admin &

Risk OpsBank Ops

Accounting

Fraud Retail Bank

Marketing

BI DW

6

What Data is available?

• Deposit information

– IM/ST Account Snapshots

– IM/ST Transactions

– RM Customer Details (Customer Records, Airmiles, AMEX

Rewards, Account Relationships)

– RF (Card) Details

– Branch, Account Types, Sales & Service and VRU Activity

• General Ledger information

– Income & Expense

– Assets & Liabilities

– Responsibility/Cost Center and Structures

– Natural Accounts and Structures

7

The Data Mart contains both

Daily and Monthly Data

8

Daily Data Monthly Data

IM/ST Transactions

Onboarding

RM Customer Details

RF (Card) Detail

Deposits

IM/ST Account Snapshots

S&S, VRU Activity

Account “Events”

General Ledger

RCs, Natural Account

Income and Expense

Assets and Liabilities

Data Availability – Matrix

9

Extract/Transform/

Load (Informatica)

Financial Systems

Lending Systems

Masterpiece

Investments

Retail Systems

Fidelity

Other

Data Sources Data Mart Targets

Deposits

RDBMS

Transform,

Cleanse, & Load

Central Metadata

Data

Profiling,

Source

Analysis,

Extraction

Ad HocReports

Customer

ProfitabilityReports

RDBMS

Future Lending

System

Lending

SystemReports

RDBMS

GL

Future

MDB

GL

SystemReports

ERWIN, Visio

Data Modeling Tool

Business Intelligence Data Flow

Data Warehouse

10

GL

Reports

Lending

Reports

Data Availability – Service Level

Agreements

• Customer Account Activity Data = 7am

• General Ledger Data = 8am

– Historically, over the last few months

• CP is ready by 5:30am and

• GL by 6:30am

11

What is…Hyperion? Business Intelligence tool?

GB Data Warehouse? SQL Databases?

GB Enterprise

Application/Tool

GB Enterprise Data Business Purpose

Hyperion HFM Hyperion Database – GL data

summary & RC level

Vendor application tailored for

external reporting; also used for

internal financial statement

preparation

Hyperion Planning Hyperion Planning Database –

Budget & Planning data at

summary & RC level

Vendor application tailored for

budgeting and planning

Hyperion Interactive Reporting

(aka Business Intelligence/BI Tool)

GB Data Warehouse

• Retail Deposit Data Mart

• General Ledger Data Mart

Vendor tool to enable building of

business cases, in-house

applications, performing enterprise

reporting, ad-hoc queries, what if &

trend analysis

Access or Excel “silo” SQL Databases End user tools for sourcing

disparate data sources, performing

departmental reporting & analysis

3

GB SQL Data Flow

Disparate DBs &

Load Processes

MS Access DBs &

Depart. Processes

End Users

Deposit

Reports

Lending

Reports

Data Sources

SQL Databases

GL

Reports

Departmental Access DBs

& Reporting

Lending Systems

Masterpiece (GL)

Retail Systems

Fidelity

Other

IM ST

RM RF

I&E A&L

Departmental

Report Preparation

MS Access & Excel

Reports

IM

ST

RM

RF

GL

ALS

CLCS

AP

13

Comparison:

GB Data Warehouse vs. SQL Databases

Subject DB(s) Data Sources Data Acquisition

GB Data Warehouse

• Retail Deposit Data Mart

• GL Data Mart

IM, ST, RM, RF, OLB,

VRU, Sales & Service

Masterpiece GL

Automated & repeatable processes;

built-in relationships for consumption

of multiple data sources; application of

standardized business rules

SQL Disparate Databases IM

ST

RM

RF

GL

AP

ALS

CLCS

Manual processes pulled into

secondary, departmental Access

databases for user manipulation,

analysis & reporting; no relationships

between data sources; application of

non-standardized business rules

14

BI Customers & Content

Customers / Content Description

Customers Marketing Intelligence

Bank Operations (Deposits, Risk Ops)

SIG (Retail Finance)

IS&T Finance

Financial Accounting & Reporting

Retail Deposit Data Mart (est. 2004)

Data: 5.5 yrs EOM / 13 Months Rolling Daily

(ADS)

• Analytics & Program Development

• Pricing

• Reporting

• Sales & Service Support

• Consumer Checking Onboarding

• Periodic Bank Ops reports

• Ad-hoc query & analysis

IM/ST Individual Account Records (Daily)

IM/ST Transactions (Summary)

RM Customer Details (Customer Records, Account

Relationships, Airmiles & AMEX Rewards)

RF (Card) Details

Account Types, Branches, Sales & Service and VRU

Activity, Online Banking

Customer Profitability Data Mart (est. 2006)

Data: 5 yrs EOM Rolling

• Monthly P&L Reports and Variance Analysis

Income, Expense, Assets, Liabilities

Detailed Transactions (vendor information)

Responsibility/Cost Center Structures

Natural Account Structures

15

BI Business Value Examples

Business Process Value

Program Development

Consumer Onboarding Projected 5-yr cumulative impact - $6.6M

Projected IRR = 186%

Product Management –

Guaranty Checking

Reversed negative checking account trend

Net increase in 2008 of ~13k accounts with value of $2M

Check Card Utilization Projected 5-yr cumulative impact - $1.8M

Projected ROI = 150%

4Q08 Deposit Gathering Increase CD & liquid savings deposits by $1.5B

Analysis & Reporting

Fee Income Analysis (NSF Tiers) “what if” analysis performed by Marketing in one day vs.

estimated 6-8 weeks w/out BI

Insider Reporting Saving 15+ hours/quarter and 1 hr/month on report

generation and export, submitted to Legal

GL Reporting for Bank Operations Saved 13 hours/month of manual effort on variance analysis

16

Terminology

17

BI Terminology

• OLTP vs. Dimensional vs. OLAP

• Normalization vs. Denormalization

• Schemas, Star vs. Snowflake

• Dim vs. Fact Tables vs. Views (SCDs)

• Relationships (parent/child), Hierarchies

• Facts, Attributes

• Aggregates

• Conformed Dimensions

• Metadata

• Cube (Physical vs Virtual) , Cube Farms

• Object-Oriented

18

OLTP vs. OLAP

• OLTP (Online Transactional Processing)– OLTP systems are optimized for fast and reliable transaction handling.

– Compared to data warehouse systems, most OLTP interactions will

involve a relatively small number of rows, but a larger group of tables.

– Data is more current

• OLAP (Online Analytical Processing)– Dynamic, multidimensional analysis of historical data, which supports

activities such as the following:

• Calculating across dimensions and through hierarchies

• Analyzing trends

• Drilling up and down through hierarchies

• Rotating to change the dimensional orientation

• OLAP tools can run against a multidimensional database or interact

directly with a relational database.

19

Normalization

• Normalization is the process of efficiently organizing data

in a database.

• There are two goals of the normalization process:

1. Eliminating redundant data (for example, storing the same data in

more than one table) and

2. Ensuring data dependencies make sense (only storing related

data in a table).

• Both of these are worthy goals as they reduce the amount

of space a database consumes and ensure that data is

logically stored.

20

Normal Forms (NF)

First Normal Form (1NF)

• First normal form (1NF) sets the very basic rules for an organized database:

Eliminate duplicative columns from the same table.

• Create separate tables for each group of related data and identify each row

with a unique column or set of columns (the primary key).

Second Normal Form (2NF)

• Second normal form (2NF) further addresses the concept of removing

duplicative data: Meet all the requirements of the first normal form.

• Remove subsets of data that apply to multiple rows of a table and place them

in separate tables.

• Create relationships between these new tables and their predecessors

through the use of foreign keys.

Third Normal Form (3NF)

• Third normal form (3NF) goes one large step further: Meet all the

requirements of the second normal form.

• Remove columns that are not dependent upon the primary key.

21

Third Normal Form (3NF)

Third Normal Form (3NF):

• 3NF schemas are typically chosen for large data warehouses, especially

environments with significant data-loading requirements that are used to feed

data marts and execute long-running queries.

22

"Nothing but the key"

A memorable summary of EF Codd's definition of 3NF, paralleling the traditional

pledge to give true evidence in a court of law, was given by Bill Kent:

“Every non-key attribute "must provide a fact about the key, the whole key, and

nothing but the key, so help me Codd”.

Schema Designs - Star

23

The star schema is perhaps the simplest data warehouse schema. It is called a star schema because the

entity-relationship diagram of this schema resembles a star, with points radiating from a central table. The center

of the star consists of a large fact table and the points of the star are the dimension tables.

A star schema is characterized by one or more very large fact tables that contain the primary information in the

data warehouse, and a number of much smaller dimension tables (or lookup tables), each of which contains

information about the entries for a particular attribute in the fact table.

Schema Designs - Snowflake

24

The snowflake schema is a variation of the star schema, featuring normalization of dimension tables.

A snowflake schema is a logical arrangement of tables in a relational database such that the entity relationship diagram resembles a

snowflake in shape. Closely related to the star schema, the snowflake schema is represented by centralized fact tables which are

connected to multiple dimensions. In the snowflake schema, however, dimensions are normalized into multiple related tables whereas the

star schema's dimensions are denormalized with each dimension being represented by a single table. When the dimensions of a

snowflake schema are elaborate, having multiple levels of relationships, and where child tables have multiple parent tables ("forks in the

road"), a complex snowflake shape starts to emerge. The "snowflaking" effect only affects the dimension tables and not the fact tables.

Dimensional Tables (SCDs)

25

In data warehousing, a dimension table is one of the set of companion tables to a fact table.

The fact table contains business facts or measures and foreign keys which refer to candidate

keys (normally primary keys) in the dimension tables.

The dimension tables contain attributes (or fields) used to constrain and group (“slice and dice”)

data when performing data warehousing queries. Typically dimension tables are named with a

“_dim” suffix

Over time, the attributes of a given row in a dimension table may change. For example, the

shipping address for a company may change. Kimball refers to this phenomenon as Slowly

Changing Dimensions (SCD). Strategies for dealing with this kind of change are divided into

three categories:

Type 1 - Simply overwrite the old value(s).

Type 2 - Add a new row containing the new value(s), and distinguish between the rows

where a change occurred

Type 3 - Add a new attribute to the existing row.

Fact Tables

• A table in a star schema that contains facts. A fact table typically has

two types of columns:

1. those that contain facts and

2. those that are foreign keys to dimension tables.

• The primary key of a fact table is usually a composite key that is made

up of all of its foreign keys.

• A fact table might contain either detail level facts or facts that have

been aggregated (fact tables that contain aggregated facts are often

instead called summary tables). A fact table usually contains facts with

the same level of aggregation.

26

Views – The “Other” Database Object

• In database theory, a view consists of a stored query accessible as a virtual

table composed of the result set of a query. Unlike ordinary tables (base

tables) in a relational database, a view does not form part of the physical

schema: it is a dynamic, virtual table computed or collated from data in the

database. Changing the data in a table alters the data shown in subsequent

invocations of the view.

– Views can provide advantages over tables:

– Views can represent a subset of the data contained in a table

– Views can join and simplify multiple tables into a single virtual table

– Views can act as aggregated tables, where the database engine aggregates

data (sum, average etc) and presents the calculated results as part of the data

– Views can hide the complexity of data; for example a view could appear as

Sales2000 or Sales2001, transparently partitioning the actual underlying table

– Views take very little space to store; the database contains only the definition

of a view, not a copy of all the data it presents

– Depending on the SQL engine used, views can provide extra security

27

Hierarchies and M:1 Relationships

Hierarchies

• A hierarchy is a set of levels having many-to-one relationships between each other, and

the set of levels collectively makes up a dimension. In a relational database, the

different levels of a hierarchy can be stored in a single table (as in a star schema) or in

separate tables (as in a snowflake schema).

Many-to-one relationships

• A many-to-one relationship is where one entity (typically a column or set of columns)

contains values that refer to another entity (a column or set of columns) that has unique

values. In relational databases, these many-to-one relationships are often enforced by

foreign key/primary key relationships, and the relationships typically are between fact

and dimension tables and between levels in a hierarchy. The relationship is often used

to describe classifications or groupings.

• For example, in a geography schema having tables Region, State and City, there are

many states that are in a given region, but no states are in two regions. Similarly for

cities, a city is in only one state (cities that have the same name but are in more than

one state must be handled slightly differently). The key point is that each city exists in

exactly one state, but a state may have many cities, hence the term "many-to-one."

28

Region State City

Cube Farms

• Fragmented Management

• Data Latency

• Dedicated Building Process

• Manual to Push to Users

• Limited Data Size

• Manual Security Coding

• Centralized Management

• Automatic Data Refresh

• No Separate Building Process

• On Demand Loading

• Full and Immediate Data Access

• Full Integrated Security29

BI Cube Farms Intelligent Cubes

Relational Database

Cubes for varying

levels of security

Cubes for each application

Cubes for increasing Data Depth

Where We Are And Where We Have

Been With BI

30

Business Intelligence Continues to

Be a Top Business Investment Priority

31

The BI Platform is the Key Component

of A Business Intelligence System

32

33

Eras of BI leading to Enterprise-Wide

BI Standardization

Seamless Migration from Workgroup to Enterprise BI

MicroStrategy Makes Moving to Enterprise BI Easy

34

Scorecards & Dashboards – Pervasive Personalized

Scorecards & Dashboards for Monitoring Performance

35

Nuts and Bolts of BI

36

“Getting Data Into The Warehouse”

• We use The Informatica PowerCenter Suite for ETL

(Extraction, Transformation, and Loading)

• Extremely powerful yet GUI based ETL Tool.

• Industry leader for data integration

• Potential future leverage of this toolset

– Data Profiling and Cleansing

– Data Matching and Lineage

– EAI (Enterprise Application Integration)

– MDM (Master Data Management)37

Data Flows via Informatica

38

Source/Target Types:

• Db and/or Table,

• Flat File (csv, txt),

• Spreadsheet,

• PDF

Transformations:

• Expressions

• Aggregaters

• Filters

• Joiners

• Look ups

• Routers

• Unions

These Mappings Can Easily Get Quite

Complicated

39

“Getting Data Out Of The Warehouse”

• DW initiative 5 years old started with Customer Profitability

(Marketing)

• Toolset = Oracle Hyperion Interactive Reporting

• Database: SQL Server

• Database size: 2.5 TB (Terabytes)

• Users: roughly 65 users (20+ active)

• 450+ reports

40

Tool Demo and Orientation

41

Report and Output Examples

• The Following examples were all created and exported

(via PDF or Excel) from Oracle/Hyperion Intelligent Studio

42

BI Reporting Terminology

• Queries (Filters, Unions, Groupings)

• Tables (Sources, Local/DB)

• Pivots

• Reports/Tables

• Dashboard

• Results (limits, computed columns)

• Chart Types

• 2 and 3 Tiered Architecture

• 5 Styles of BI

• Caching

• WYSIWYG

• Drilling (Up/Down/Across)

• SQL, Multi Pass SQL43

This Chart-Comparison Matrix Indentifies The

Best Chart Type To Maximize Data

Comprehension

44

The 3-Tier Architecture Has

The Following Three Tiers:

45

How 3 User Different Groups Fall Amongst

The Various Layers and Styles of BI

46

A 5 Styles of BI

Users Can Seamlessly Traverse All 5 Styles of BI as

They Need

Event

Based

Schedule

Based

Any Criteria

To Any

Device

47

Caching Dramatically Reduces Average

Response Time

48

Slicing and Dicing within the Data

Warehouse

49

New BI Tool RFI (Completed Fall 2008)

• Over 230 hours were spent on an extensive and

encompassing analysis of business, reporting, user, and

administrative support requirements across the our most

technical business unit, Marketing.

• We

– Participated in numerous Vendor/Analysis calls with Gartner

– Purchased 3rd Party and Vendor analyses

– Requested Information, a completed comprehensive questionnaire

(some 100+ questions), and product quotes from BI Vendors

– We independently and internally scored their responses

– Reviewed with the Business our recommendation and why.

50

Summary

• Top two vendors based on market data & Gartner calls:

1. MicroStrategy (MSTR)

2. Oracle (OBIEE)

• Both MSTR & Oracle offering discount pricing

• MSTR fulfills all business and IT requirements and is noted for

requiring few IT support personnel

• Gartner comments on MSTR:

• Fewest weaknesses

• Elegant

• Strong performance

• Scalable

• PMML support

• Easy IT maintenance

• No main functionality lacking

• Excellent dashboards

• Scalable

• $ only downside (historically)

51

MicroStrategy is the Best Overall BI Technology According to the

Most Recent Analyst Evaluations and Customer Surveys

MicroStrategy

#1BI Platform Capabilities

Rating

Analyst Evaluation

Kurt Schlegel

Bhavish Sood

12 BI Capabilities

220 Distinct Criteria

April 2007

Oracle

#1 tie

IBI

#1 tie

Cognos

#4

SAP

#5

Hyperion

#6

Bus Obj.

#7

QlikTech

#8

Panorama

#8

Microsoft

#10

SAS

#11

Magic Quadrant Customer Survey

MicroStrategy

#1

QlikTech

#2

Oracle

#3

Cognos

#4

Board

#5

SAS

#6

Microsoft

#7

Applix

#8

Bus. Obj.

#9

IBI

#10

Spotfire

#11

Customer Survey

James Richardson

367 Companies

12 Core BI Capabilities

March 2008

Analyst Evaluation

Cindi Howson

Hands-on testing

100+ Criteria Tested

May 2008

MicroStrategy

#1

Bus. Obj.

#2

IBI

#6

Microsoft

#6 tie

QlikTech

#8

Oracle

#3 tie

SAS

#3 tie

Cognos

#3 tie

Customer Survey

Nigel Pendse

1,901 Companies

58 Countries

17 Major Categories

Feb 2008

MicroStrategy

#1

Applix

#2

IBI

#3

Microsoft AS

#4

Hyperion

#5

Microsoft RS

#6

Cognos AS

#7

Cognos RS

#8

Bus. Obj.

#9

SAP

#10

B.O. Crystal

#11

Analyst Evaluation &

Customer Survey

Daan Van Beek

Norman Manley

70 Evaluation Criteria

Nov 2007

MicroStrategy

#1

IBI

#2

Oracle

#3

SAS

#4

Hyperion

#5

Microsoft

#6

Cognos

#7

Bizzcore

#8

Bus. Obj.

#9

SAP

#10

Actuate

#11

52

Gartner Magic Quadrant Customer

Survey: Survey of BI Customers in Support of the Gartner Magic Quadrant

Analysis for BI Platforms

53

BI Survey 7: BI Technology Rankings According to the BI

Survey 7

The Largest Independent Survey of BI, Involving Over 1,900 Companies

54

BI Product Survey: Evaluation and Survey Conducted by

Passioned International, a Leading BI Analyst Firm in the

Netherlands

55

Gartner BI Platform Capability Evaluation:Comprehensive, Point-by-point Evaluation of all Major BI Products

56

The BI Scorecard: Comprehensive Hands-on Evaluation

of BI Products by Cindi Howson, Author, Industry Analyst, and

President of ASK

57

What the future brings…

• …and where we want to go with BI.

58

Sample Weekly Product Scorecard

59

Sample Dashboard

60

All 5 Styles Delivered Through Any Interface

Browser

Desktop

Mobile

Office

Email

61

Mobile BI with Blackberry and iPhone Support

62

Dynamic Dashboards Help Business

People Make Better Decisions Faster

63

Dynamic Dashboards Can Collapse Many

Reports into a Single Dashboard

64

Dynamic Dashboards Can Be

Combined into New Dashboard Books

65

Native Support for Flash Rendering

One Report Design, Render in AJAX or Flash and

Toggle Between

Flash

66

Interactive Flash Dashboards (via email/mobile)

67

Interactive Flash Dashboards (via email/mobile)

68

Interactive Flash Dashboards (via email/mobile)

69

Interactive Flash Dashboards (slider widget)

70

71

MicroStrategy Abstracts the Business Model From the

Physical Model Using a Layered Object-Oriented Metadata

DATA SOURCESAccess all corporate data source

Schema neutrality, Database Optimizations

DATA ABSTRACTIONInsulate business constructs from data sources

Tables, Attributes, Facts, Hierarchies,

Transformations

BUSINESS ABSTRACTIONBuild reusable report components Metrics, Filters,

Prompts, Templates, Custom Groupings

REPORT DESIGNAssemble insightful, visually appealing reports

Layout, Format, Calculations

APPLICATION CONFIGURATIONDefine application-wide settings

User Administration, Security, Performance

WYSIWYG Report Design Makes it Possible for

Business Users to Refine Report Designs Using

Common Microsoft Office-like Skills

72

Time to Deploy Without Using

WYSIWYG Design

73

Time to Deploy Using WYSIWYG

Design

74

Advanced Analysis and Ad Hoc: Predictive

Analysis is now available for Business Users

75

Personalized Information Radar

76

MicroStrategy Provides a Complete Set of Tools

for Automatic Administration at Scale

77

Automated Testing Ensures Information

Integrity at Only 5% of Typical Testing Costs

78

The MicroStrategy Unified Architecture

79

The MicroStrategy Unified Architecture

80

Industrial Strength BI Attributes

1. Ease-Of-Use and Self-Service

2. Highest User Scalability

3. Highest Report Scalability

4. Automated Maintainability at Scale

5. Highest Data Scalability

81

# 1) Ease-Of-Use and Self-Service

82

#2) User Scalability Without The Staffing or

Cost Burden

Total Cost of Ownership (TCO) assesses costs over the lifecycle of an application. Industry analysts

agree that TCO is dominated by recurring costs and not by one-time purchase costs.

Gartner, a leading analyst firm, estimates that customers spend up to four times the initial cost

of their software license every year they own their BI applications. The vast majority of

these recurring costs are personnel or staffing costs.

IDC, another leading research firm, concludes that staffing constitutes 60%-85%

of the overall enterprise software ownership costs over three years.

3 Year Typical Enterprise Software TCO Breakdown

Note: The figure is based on over 300 interviews conducted across numerous platforms, presented in composite form. Source: IDC Study 2007

83

Staffing is Largest

TCO Component

in BI Applications

MicroStrategy Customer Data Shows

Reduced Staffing Costs

400002000010000400020001000500300

50

40

30

20

10

0

User Population

IT S

taff

(6

0%

of

TC

O)

IT Resource Efficiency

Other BI

**Note: MicroStrategy 8 based on results of MicroStrategy customer research study of over 80 production deployments.

Other BI based on competitive sales cycle feedback.84

As BI Systems Expand, Administration Becomes a

Key Driver in the Total Cost of Ownership

Finance

DWH

25 Users

100 Reports

1 BI Application

1 Data Source

Finance

Sales

Marketing

HR

DWHOperational

Databases

Cube

Databases

Many Data Sources

Many BI Applications

1,000 Reports

1,000 Users

1 Full Time Administrator 1 Full Time Administrator85

A Complete and Multilayered Metadata Effectively

Minimizes the Number of Moving Parts

Administrators Need to Create and Maintain

No Atomic Elements Partial Set of Atomic

Elements

Complete Set of Atomic

Elements

Report Creation 1,000 Reports = 1,000 SQL

Statements

1,000 Reports = 200

Metadata Objects**

1,000 Reports = 20

Metadata Objects**

Reusability No Reusability Limited Reusability Full Reusability

Parameterization No Parameterization Limited Parameterization Full Parameterization

Maintenance Overhead •1,000 SQL Statements

•1,000 man hrs at 1 hr per

report

•200 Objects***

•100 man hrs at 0.5 hr per

object

•20 Objects***

•10 man hrs at 0.5 hr per

object

Assumptions:

* Minor changes include changes to calculations, levels of aggregation, attributes,

number of columns, and filtering criteria

** Reports are created with underlying MD objects

*** Assumes changes to metadata objects will automatically cascade to reports

Consider Minor Changes to a BI System with 1,000 Reports:

86

Automatic Monitoring Helps Reduce HW and

Downtime Costs

22% of TCO

1. Performance Analysis:

• Fine tune BI system for maximum performance

• Optimize HW utilization

• Track User Activity

2. Operational Analysis:

• Monitor daily trends

• Reduce unplanned system downtime

• Predict future capacity requirements

Minimize

HW Costs

Minimize

Downtime

Source: IDC Study 2007

87

# 2) Highest User Scalability

88

Reports Can Be Delivered Through Users’ Interface of Choice

89

# 3) Highest Report Scalability

Comparing Reusable Metadata

Report Development Is Faster With Each

New Report

90

91

Dynamic Caching

#4) One Report Definition Can Generate

Hundreds of Report Variations

92

From a Single Report Definition

Dramatically Reduced Number of Reports

“Supported” not “Produced”

93

Semantic-Based User Profiles Enable

Fine-Tuned-Control

94

The security architecture gives administrators fine-grained control of every

user along three dimensions of privileges and permissions, allowing each user to access just

the functionality their skills can accommodate and just the data they are allowed to see.

#5) Highest Data Scalability

95

Heterogeneous Database Access via MicroStrategy ROLAP Architecture

Embedded Object Definitions Ensure that Object

Updates Are Necessary in One Place Only

96

Meta Data and Project Documentation

97