BI/DW 101
Introduction to Business Intelligence at Guaranty Bank

              Erik Okerholm, Business Intelligence
Agenda
•   Business Intelligence Overview
•   Data Flow, Data Availability/SLAs
•   BI at Guaranty Bank
     – Query/Repor...
Multiple Sources Were Leveraged To Gather
Information For This Presentation




                                          ...
What is Business Intelligence?


 “Business Intelligence is actually an environment in which business
 users receive data ...
What is Business Intelligence?


   The discipline of understanding the business abstractly
                  and often fr...
BI Reporting Areas


                     Accounting

        Deposit
        Admin &                   Bank Ops
        R...
What Data is available?


 • Deposit information
    – IM/ST Account Snapshots
    – IM/ST Transactions
    – RM Customer ...
The Data Mart contains both
Daily and Monthly Data


Daily Data                     Monthly Data
  Deposits
     IM/ST Acc...
Data Availability – Matrix




                             9
Business Intelligence Data Flow

                                                          Data Warehouse
   Masterpiece
 ...
Data Availability – Service Level
Agreements


 • Customer Account Activity Data = 7am

 • General Ledger Data = 8am
    –...
What is…GB Data Warehouse? Intelligence tool?
        Hyperion? Business
                            SQL Databases?



   ...
GB SQL Data Flow


                         IM
    IM    ST
    RM    RF
                        ST                       ...
Comparison:
GB Data Warehouse vs. SQL Databases


       Subject DB(s)             Data Sources                   Data Acq...
BI Customers & Content

         Customers / Content                                      Description
Customers           ...
BI Business Value Examples

         Business Process                                         Value
Program Development
  ...
Terminology




              17
BI Terminology

 •   OLTP vs. Dimensional vs. OLAP
 •   Normalization vs. Denormalization
 •   Schemas, Star vs. Snowflake...
OLTP vs. OLAP


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


 • Normalization is the process of efficiently organizing data
   in a database.
 • There are two goals of...
Normal Forms (NF)

 First Normal Form (1NF)
 • First normal form (1NF) sets the very basic rules for an organized database...
Third Normal Form (3NF)


Third Normal Form (3NF):
• 3NF schemas are typically chosen for large data warehouses, especiall...
Schema Designs - Star




The star schema is perhaps the simplest data warehouse schema. It is called a star schema becaus...
Schema Designs - Snowflake




The snowflake schema is a variation of the star schema, featuring normalization of dimensio...
Dimensional Tables (SCDs)

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

...
Fact Tables


• A table in a star schema that contains facts. A fact table typically has
  two types of columns:
    1.   ...
Views – The “Other” Database Object

•   In database theory, a view consists of a stored query accessible as a virtual
   ...
Hierarchies and M:1 Relationships

 Hierarchies
 • A hierarchy is a set of levels having many-to-one relationships between...
Cube Farms

               BI Cube Farms                                        Intelligent Cubes
                      Cu...
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
Eras of BI leading to Enterprise-Wide
BI Standardization




                                        33
Seamless Migration from Workgroup to Enterprise BI
MicroStrategy Makes Moving to Enterprise BI Easy




                  ...
Scorecards & Dashboards – Pervasive Personalized
Scorecards & Dashboards for Monitoring Performance




                  ...
Nuts and Bolts of BI




                       36
“Getting Data Into The Warehouse”


 • We use The Informatica PowerCenter Suite for ETL
   (Extraction, Transformation, an...
Data Flows via Informatica




    Source/Target Types:
       • Db and/or Table,
       • Flat File (csv, txt),
       • ...
These Mappings Can Easily Get Quite
Complicated




                                      39
“Getting Data Out Of The Warehouse”


• DW initiative 5 years old started with Customer Profitability
  (Marketing)

• Too...
Tool Demo and Orientation




                            41
Report and Output Examples


 • The Following examples were all created and exported
   (via PDF or Excel) from Oracle/Hyp...
BI Reporting Terminology

•   Queries (Filters, Unions, Groupings)
•   Tables (Sources, Local/DB)
•   Pivots
•   Reports/T...
This Chart-Comparison Matrix Indentifies The
Best Chart Type To Maximize Data
Comprehension




                          ...
The 3-Tier Architecture Has
The Following Three Tiers:




                              45
How 3 User Different Groups Fall Amongst
The Various Layers and Styles of BI




                                         ...
A 5 Styles of BI
Users Can Seamlessly Traverse All 5 Styles of BI as
They Need




                                       ...
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 busine...
Summary

•   Top two vendors based on market data & Gartner calls:
    1. MicroStrategy (MSTR)
    2. Oracle (OBIEE)

•   ...
MicroStrategy is the Best Overall BI Technology According to the
    Most Recent Analyst Evaluations and Customer Surveys
...
Gartner Magic Quadrant Customer
Survey: Survey of BI Customers in Support of the Gartner Magic Quadrant
Analysis for BI Pl...
BI Survey 7: BI Technology Rankings According to the BI
Survey 7
 The Largest Independent Survey of BI, Involving Over 1,9...
BI Product Survey: Evaluation and Survey Conducted by
Passioned International, a Leading BI Analyst Firm in the
Netherland...
Gartner BI Platform Capability Evaluation:
Comprehensive, Point-by-point Evaluation of all Major BI Products




         ...
The BI Scorecard:          Comprehensive Hands-on Evaluation
of BI Products by Cindi Howson, Author, Industry Analyst, and...
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                               Office


                         ...
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




                     ...
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
MicroStrategy Abstracts the Business Model From the
Physical Model Using a Layered Object-Oriented Metadata



           ...
WYSIWYG Report Design Makes it Possible for
Business Users to Refine Report Designs Using
Common Microsoft Office-like Ski...
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




                                ...
Personalized Information Radar




                                 76
MicroStrategy Provides a Complete Set of Tools
for Automatic Administration at Scale




                                 ...
Automated Testing Ensures Information
Integrity at Only 5% of Typical Testing Costs




                                  ...
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

      ...
# 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 lifecyc...
MicroStrategy Customer Data Shows
Reduced Staffing Costs


                                                  IT Resource E...
As BI Systems Expand, Administration Becomes a
Key Driver in the Total Cost of Ownership

                                ...
A Complete and Multilayered Metadata Effectively
Minimizes the Number of Moving Parts
Administrators Need to Create and Ma...
Automatic Monitoring Helps Reduce HW and
Downtime Costs



                                                        22% of ...
# 2) Highest User Scalability


   Reports Can Be Delivered Through Users’ Interface of Choice




                       ...
# 3) Highest Report Scalability


              Comparing Reusable Metadata




                                          ...
Report Development Is Faster With Each
New Report




                                         90
Dynamic Caching




                  91
#4) One Report Definition Can Generate
Hundreds of Report Variations




                               From a Single Repo...
Dramatically Reduced Number of Reports
“Supported” not “Produced”




                                         93
Semantic-Based User Profiles Enable
Fine-Tuned-Control




            The security architecture gives administrators fine...
#5) Highest Data Scalability


    Heterogeneous Database Access via MicroStrategy ROLAP Architecture




                ...
Embedded Object Definitions Ensure that Object
Updates Are Necessary in One Place Only




                               ...
Meta Data and Project Documentation




                                      97
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Bi Lunch And Learn Examples

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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 future

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Bi Lunch And Learn Examples

  1. 1. BI/DW 101 Introduction to Business Intelligence at Guaranty Bank Erik Okerholm, Business Intelligence
  2. 2. Agenda • 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
  3. 3. Multiple Sources Were Leveraged To Gather Information For This Presentation 3
  4. 4. What is Business Intelligence? “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 4
  5. 5. What is Business Intelligence? The discipline of understanding the business abstractly and often from a distance. With business intelligence, you can see the forest and the trees 5
  6. 6. BI Reporting Areas Accounting Deposit Admin & Bank Ops Risk Ops BI DW Fraud Retail Bank Marketing 6
  7. 7. 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
  8. 8. The Data Mart contains both Daily and Monthly Data Daily Data Monthly Data Deposits IM/ST Account Snapshots IM/ST Transactions S&S, VRU Activity Onboarding Account “Events” RM Customer Details General Ledger RF (Card) Detail RCs, Natural Account Income and Expense Assets and Liabilities 8
  9. 9. Data Availability – Matrix 9
  10. 10. Business Intelligence Data Flow Data Warehouse Masterpiece Data GL Profiling, GL Source RDBMS System Analysis, Reports Fidelity Reports Extraction GL Future Transform, MDB Retail Systems Cleanse, & Load Customer RDBMS Profitability Reports Investments Deposits Ad Hoc Reports Lending Systems RDBMS Lending Lending Central Metadata System Reports Financial Systems Future Lending Reports System Other Data Modeling Tool ERWIN, Visio Extract/Transform/ Data Sources Data Mart Targets 10 Load (Informatica)
  11. 11. 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
  12. 12. What is…GB Data Warehouse? Intelligence tool? Hyperion? Business SQL Databases? GB Enterprise GB Enterprise Data Business Purpose Application/Tool Hyperion HFM Hyperion Database – GL data Vendor application tailored for summary & RC level external reporting; also used for internal financial statement preparation Hyperion Planning Hyperion Planning Database – Vendor application tailored for Budget & Planning data at budgeting and planning summary & RC level Hyperion Interactive Reporting GB Data Warehouse Vendor tool to enable building of (aka Business Intelligence/BI Tool) • Retail Deposit Data Mart business cases, in-house • General Ledger Data Mart 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
  13. 13. GB SQL Data Flow IM IM ST RM RF ST Deposit Fidelity Reports I&E A&L RM Masterpiece (GL) RF GL GL Reports Retail Systems ALS CLCS Lending Lending Systems Reports AP Other SQL Databases Departmental Access DBs & Reporting End Users Disparate DBs & MS Access DBs & Departmental MS Access & Excel Data Sources Load Processes Depart. Processes Report Preparation Reports 13
  14. 14. Comparison: GB Data Warehouse vs. SQL Databases Subject DB(s) Data Sources Data Acquisition GB Data Warehouse IM, ST, RM, RF, OLB, Automated & repeatable processes; • Retail Deposit Data Mart VRU, Sales & Service built-in relationships for consumption • GL Data Mart Masterpiece GL of multiple data sources; application of standardized business rules SQL Disparate Databases IM Manual processes pulled into ST secondary, departmental Access RM databases for user manipulation, RF analysis & reporting; no relationships GL between data sources; application of AP non-standardized business rules ALS CLCS 14
  15. 15. 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 IM/ST Individual Account Records (Daily) (ADS) IM/ST Transactions (Summary) • Analytics & Program Development • Pricing RM Customer Details (Customer Records, Account • Reporting Relationships, Airmiles & AMEX Rewards) • Sales & Service Support RF (Card) Details • Consumer Checking Onboarding Account Types, Branches, Sales & Service and VRU • Periodic Bank Ops reports Activity, Online Banking • Ad-hoc query & analysis Customer Profitability Data Mart (est. 2006) Income, Expense, Assets, Liabilities Data: 5 yrs EOM Rolling Detailed Transactions (vendor information) • Monthly P&L Reports and Variance Analysis Responsibility/Cost Center Structures Natural Account Structures 15
  16. 16. BI Business Value Examples Business Process Value Program Development Consumer Onboarding Projected 5-yr cumulative impact - $6.6M Projected IRR = 186% Product Management – Reversed negative checking account trend Guaranty Checking 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
  17. 17. Terminology 17
  18. 18. 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
  19. 19. 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
  20. 20. 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
  21. 21. 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
  22. 22. 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. "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”. 22
  23. 23. Schema Designs - Star 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. 23
  24. 24. Schema Designs - Snowflake 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. 24
  25. 25. Dimensional Tables (SCDs) 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. 25
  26. 26. 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
  27. 27. 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
  28. 28. 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." Region State City 28
  29. 29. Cube Farms BI Cube Farms Intelligent Cubes Cubes for each application Cubes for varying levels of security Relational Database Cubes for increasing Data Depth • Fragmented Management • Centralized Management • Data Latency • Automatic Data Refresh • Dedicated Building Process • No Separate Building Process • Manual to Push to Users • On Demand Loading • Limited Data Size • Full and Immediate Data Access • Manual Security Coding • Full Integrated Security 29
  30. 30. Where We Are And Where We Have Been With BI 30
  31. 31. Business Intelligence Continues to Be a Top Business Investment Priority 31
  32. 32. The BI Platform is the Key Component of A Business Intelligence System 32
  33. 33. Eras of BI leading to Enterprise-Wide BI Standardization 33
  34. 34. Seamless Migration from Workgroup to Enterprise BI MicroStrategy Makes Moving to Enterprise BI Easy 34
  35. 35. Scorecards & Dashboards – Pervasive Personalized Scorecards & Dashboards for Monitoring Performance 35
  36. 36. Nuts and Bolts of BI 36
  37. 37. “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
  38. 38. Data Flows via Informatica Source/Target Types: • Db and/or Table, • Flat File (csv, txt), • Spreadsheet, • PDF Transformations: • Expressions • Aggregaters • Filters • Joiners • Look ups • Routers • Unions 38
  39. 39. These Mappings Can Easily Get Quite Complicated 39
  40. 40. “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
  41. 41. Tool Demo and Orientation 41
  42. 42. Report and Output Examples • The Following examples were all created and exported (via PDF or Excel) from Oracle/Hyperion Intelligent Studio 42
  43. 43. 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 SQL 43
  44. 44. This Chart-Comparison Matrix Indentifies The Best Chart Type To Maximize Data Comprehension 44
  45. 45. The 3-Tier Architecture Has The Following Three Tiers: 45
  46. 46. How 3 User Different Groups Fall Amongst The Various Layers and Styles of BI 46
  47. 47. A 5 Styles of BI Users Can Seamlessly Traverse All 5 Styles of BI as They Need Any Criteria Event Schedule To Any Based Based Device 47
  48. 48. Caching Dramatically Reduces Average Response Time 48
  49. 49. Slicing and Dicing within the Data Warehouse 49
  50. 50. 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
  51. 51. 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 • Easy IT maintenance • Elegant • No main functionality lacking • Strong performance • Excellent dashboards • Scalable • Scalable • PMML support • $ only downside (historically) 51
  52. 52. MicroStrategy is the Best Overall BI Technology According to the Most Recent Analyst Evaluations and Customer Surveys Customer Survey QlikTech Oracle Cognos Board SAS #2 #3 #4 #5 #6 James Richardson MicroStrategy Magic Quadrant 367 Companies Customer Survey 12 Core BI Capabilities #1 Microsoft Applix Bus. Obj. IBI Spotfire March 2008 #7 #8 #9 #10 #11 Customer Survey Applix IBI Microsoft AS Hyperion Microsoft RS Nigel Pendse #2 #3 #4 #5 #6 1,901 Companies MicroStrategy 58 Countries 17 Major Categories #1 Cognos AS Cognos RS Bus. Obj. SAP B.O. Crystal Feb 2008 #7 #8 #9 #10 #11 Analyst Evaluation & IBI Oracle SAS Hyperion Microsoft Customer Survey #2 #3 #4 #5 #6 MicroStrategy Daan Van Beek Norman Manley #1 Cognos Bizzcore Bus. Obj. SAP Actuate 70 Evaluation Criteria #7 #8 #9 #10 #11 Nov 2007 Analyst Evaluation Oracle IBI Cognos SAP Hyperion Kurt Schlegel #1 tie #1 tie #4 #5 #6 BI Platform Bhavish Sood MicroStrategy Capabilities 12 BI Capabilities #1 Bus Obj. QlikTech Panorama Microsoft SAS Rating 220 Distinct Criteria #7 #8 #8 #10 #11 April 2007 Analyst Evaluation Bus. Obj. Cognos SAS Oracle IBI #2 #3 tie #3 tie #3 tie #6 Cindi Howson MicroStrategy Hands-on testing 100+ Criteria Tested #1 Microsoft QlikTech May 2008 #6 tie #8 52
  53. 53. Gartner Magic Quadrant Customer Survey: Survey of BI Customers in Support of the Gartner Magic Quadrant Analysis for BI Platforms 53
  54. 54. BI Survey 7: BI Technology Rankings According to the BI Survey 7 The Largest Independent Survey of BI, Involving Over 1,900 Companies 54
  55. 55. BI Product Survey: Evaluation and Survey Conducted by Passioned International, a Leading BI Analyst Firm in the Netherlands 55
  56. 56. Gartner BI Platform Capability Evaluation: Comprehensive, Point-by-point Evaluation of all Major BI Products 56
  57. 57. The BI Scorecard: Comprehensive Hands-on Evaluation of BI Products by Cindi Howson, Author, Industry Analyst, and President of ASK 57
  58. 58. What the future brings… • …and where we want to go with BI. 58
  59. 59. Sample Weekly Product Scorecard 59
  60. 60. Sample Dashboard 60
  61. 61. All 5 Styles Delivered Through Any Interface Browser Office Mobile Desktop Email 61
  62. 62. Mobile BI with Blackberry and iPhone Support 62
  63. 63. Dynamic Dashboards Help Business People Make Better Decisions Faster 63
  64. 64. Dynamic Dashboards Can Collapse Many Reports into a Single Dashboard 64
  65. 65. Dynamic Dashboards Can Be Combined into New Dashboard Books 65
  66. 66. Native Support for Flash Rendering One Report Design, Render in AJAX or Flash and Toggle Between Flash 66
  67. 67. Interactive Flash Dashboards (via email/mobile) 67
  68. 68. Interactive Flash Dashboards (via email/mobile) 68
  69. 69. Interactive Flash Dashboards (via email/mobile) 69
  70. 70. Interactive Flash Dashboards (slider widget) 70
  71. 71. MicroStrategy Abstracts the Business Model From the Physical Model Using a Layered Object-Oriented Metadata APPLICATION CONFIGURATION Define application-wide settings User Administration, Security, Performance REPORT DESIGN Assemble insightful, visually appealing reports Layout, Format, Calculations BUSINESS ABSTRACTION Build reusable report components Metrics, Filters, Prompts, Templates, Custom Groupings DATA ABSTRACTION Insulate business constructs from data sources Tables, Attributes, Facts, Hierarchies, Transformations DATA SOURCES Access all corporate data source Schema neutrality, Database Optimizations 71
  72. 72. WYSIWYG Report Design Makes it Possible for Business Users to Refine Report Designs Using Common Microsoft Office-like Skills 72
  73. 73. Time to Deploy Without Using WYSIWYG Design 73
  74. 74. Time to Deploy Using WYSIWYG Design 74
  75. 75. Advanced Analysis and Ad Hoc: Predictive Analysis is now available for Business Users 75
  76. 76. Personalized Information Radar 76
  77. 77. MicroStrategy Provides a Complete Set of Tools for Automatic Administration at Scale 77
  78. 78. Automated Testing Ensures Information Integrity at Only 5% of Typical Testing Costs 78
  79. 79. The MicroStrategy Unified Architecture 79
  80. 80. The MicroStrategy Unified Architecture 80
  81. 81. 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
  82. 82. # 1) Ease-Of-Use and Self-Service 82
  83. 83. #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. 3 Year Typical Enterprise Software TCO Breakdown Staffing is Largest TCO Component in BI Applications Note: The figure is based on over 300 interviews conducted across numerous platforms, presented in composite form. Source: IDC Study 2007 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. 83
  84. 84. MicroStrategy Customer Data Shows Reduced Staffing Costs IT Resource Efficiency 50 40 Other BI IT Staff (60% of TCO) 30 20 10 0 300 500 1000 2000 4000 10000 20000 40000 User Population **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
  85. 85. As BI Systems Expand, Administration Becomes a Key Driver in the Total Cost of Ownership 1,000 Users 25 Users 1,000 Reports 100 Reports 1 BI Application Many BI Applications Finance Finance Marketing Sales HR 1 Data Source Many Data Sources DWH Operational Cube Databases DWH Databases 1 Full Time Administrator 1 Full Time Administrator 85
  86. 86. A Complete and Multilayered Metadata Effectively Minimizes the Number of Moving Parts Administrators Need to Create and Maintain Consider Minor Changes to a BI System with 1,000 Reports: No Atomic Elements Partial Set of Atomic Complete Set of Atomic Elements Elements Report Creation 1,000 Reports = 1,000 SQL 1,000 Reports = 200 1,000 Reports = 20 Statements Metadata Objects** Metadata Objects** Reusability No Reusability Limited Reusability Full Reusability Parameterization No Parameterization Limited Parameterization Full Parameterization Maintenance Overhead •1,000 SQL Statements •200 Objects*** •20 Objects*** •1,000 man hrs at 1 hr per •100 man hrs at 0.5 hr per •10 man hrs at 0.5 hr per report object 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 86
  87. 87. Automatic Monitoring Helps Reduce HW and Downtime Costs 22% of TCO Source: IDC Study 2007 1. Performance Analysis: • Fine tune BI system for maximum performance Minimize • Optimize HW utilization HW Costs • Track User Activity 2. Operational Analysis: • Monitor daily trends Minimize • Reduce unplanned system downtime Downtime • Predict future capacity requirements 87
  88. 88. # 2) Highest User Scalability Reports Can Be Delivered Through Users’ Interface of Choice 88
  89. 89. # 3) Highest Report Scalability Comparing Reusable Metadata 89
  90. 90. Report Development Is Faster With Each New Report 90
  91. 91. Dynamic Caching 91
  92. 92. #4) One Report Definition Can Generate Hundreds of Report Variations From a Single Report Definition 92
  93. 93. Dramatically Reduced Number of Reports “Supported” not “Produced” 93
  94. 94. Semantic-Based User Profiles Enable Fine-Tuned-Control 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. 94
  95. 95. #5) Highest Data Scalability Heterogeneous Database Access via MicroStrategy ROLAP Architecture 95
  96. 96. Embedded Object Definitions Ensure that Object Updates Are Necessary in One Place Only 96
  97. 97. Meta Data and Project Documentation 97

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