bi lunch and learn examples
DESCRIPTION
BI 101 Presentation and examples of some of my work. Background information on Business Intelligence; BI Tool and Vendor Analysis; Current/Upcoming technology we are exploring and hope to leverage in the near futureTRANSCRIPT
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,
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
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
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