Slide
Heading
Understanding
the
Business Intelligence
Framework
Michael J Scarbrough
09 November 2015
Introductions
• Michael J Scarbrough, CISA, CRISC, MBA
– VP-Senior Audit Manager @ BMO (Bank of Montreal)
Financial Group – Corporate Audit Division
– Data analysis
– Data modeling of controls for 100%
effectiveness testing
– TDWI Executive Committee
– Committee to develop control framework
methodology for BI environments in 2005-2009
– Expert at auditing BI environments (since 2004)
Agenda
Defining Business Intelligence
Business Intelligence Framework
Business Intelligence
Risks-Exposures-Controls
Slide
Heading
Metadata Types
Metadata Sources
Business Intelligence: History
• BI formally coined in 1998
• Dynamic & evolving at a rapid pace
• BI Common Body of Knowledge maintained by
TDWI.
– The Data Warehouse Institute: “TDWI is dedicated to
educating business and information technology
professionals about the strategies, techniques, and
tools required to successfully design, build, and
maintain business intelligence and data warehousing
solutions. It also fosters the advancement of business
intelligence and data warehousing research and
contributes to knowledge transfer and professional
development of its Members.”
Credits: The Data Warehouse Institute
4
Business Intelligence: Definition
• “…a set of concepts and methodologies to improve
decision making in business through use of facts and
fact-based systems.” --Howard Dresner, The Gartner
Group
• “An architecture & collection of integrated operational
as well as decision support applications & databases
that provide the business community easy access to
business data. --Larissa T. Moss & Shaku Arte, BI
Roadmap
• “…processes, technologies, & tools needed to turn
data into information, information into knowledge, and
knowledge into plans that drive profitable business
actions.” --David Loshin, BI: The Savvy Manager’s
Guide
Credits: The Data Warehouse Institute
5
BI Requirements
• Organizational skills & motivation to develop a BI
program where results are applied back into the
business.
• Knowledge & skills to use business analysis to
identify/create business information
• Collections of quality data & metadata important to
the business
• Application of analytic tools, techniques, &
processes
Credits: The Data Warehouse Institute, MJ Scarbrough
6
BI as a Data “Refinery”
BI As a Data Refinery
Credits: The Data Warehouse Institute
7
TDWI: BI Components Framework
TDWI Business Intelligence Framework: April 2004
Business Requirements
BI Architecture
Data Warehousing/Data Mart
Data Resource Administration
Data Acquisition, Cleansing, & Integration
Development
BI & DW Operations
Program Management
Data Sources
Data Stores
Information Services
Information Delivery
Business Analytics
Business Applications
Business Value
Credits: The Data Warehouse
Institute, MJ Scarbrough
8
1.
2.
3.
4.
5.
6.
7.
Business Layer
BI results meet business requirements
BI program is business owned & driven
BI contributes to strategic positioning
BI is a key enabler of business tactics
Solutions are aligned with business processes
Business metrics are meaningful & valued
BI delivers measurable business value
Business Value
· Business Impact
· Strategic Positioning
· Tactical Effectiveness
· Insight & Innovation
· Organization Learning & Growth
BI Component Descriptions
Business
architecture
guides business
applications.
Applications
(business uses)
are central to
creating business
value
Value is created by
meeting business
requirements
Business Requirements
· Respond to business drivers
· Achieve business goals
· Implement business strategies
· Enable & inform business tactics
· Optimize business processes
· Meet business information needs
Development
· Data resources
· Information services
· Business metrics
· Dashboards & scorecards
· Analytic applications
Program Management
· Charter & sponsorship
· Cohesive projects
· Organizational impact
· Managed quality
· Measures of success
1.
2.
3.
4.
5.
6.
7.
Getting from
requirements to value
demands a
commitment to
program mgmt & a
sound approach to
development
Sustainable BI
requires sound
administrative &
operations
practices
**Items are color coordinated w/BI Framework
Components tab
Infrastructure (Data Store) Layer
Data manipulation & derivation standards
Identify data gaps
Paint the business picture & integrate the technology
Engineer the data warehouse for value, revenue, & cost reduction
Data integration
Data quality
Data warehousing is the “engine” that drives business intelligence
1.
2.
3.
4.
5.
6.
1.
2.
3.
4.
5.
6.
Administration & Operations Layer
The business is connected with the data
Decision-makers have access to information
Business analytics support business mgmt
Information is of high quality
Information services are reliable
BI services adapt & change with the business
BI Architecture
· Business architecture
· Information/data architecture
· Process architecture
· Organizational architecture
· Technology architecture
Business Applications
· Performance management
· Business analytics
· Actionable information
· Discovery & forecasting
· Decision support
Data Resource Administration
· Data governance
· Data ownership
· Data stewardship
· Data custodianship
· Data security
BI & DW Operations
· Scheduling
· Execution
· Support
· Maintenance
Information Services Layer
Query & reporting
OLAP (Online Analytical Processing)
DSS/EIS (Decision Support System/Executive Information System)
Analytic applications
Data Mining
Information services turns warehousing results into BI products.
Credits: The Data Warehouse Institute, MJ Scarbrough
9
Business Layer
(Green)
• Business value
– Benefits anticipated
• Business requirements
– Reasons to implement BI
– Results required (e.g. metrics, information needs)
• Development
– Project activities that create & deploy BI & DW/DM
products
• Program Management
– Ongoing activities to manage BI program
Credits: The Data Warehouse Institute, MJ Scarbrough
10
Business Layer Goals
•
•
•
•
•
•
BI delivers business value
Results meet business requirements
BI program is business owned & driven
BI contributes to strategic positioning
BI is a key enabler of business tactics
Solutions aligned with business
processes
• Identified and integrated business
metrics are value-added
Credits: The Data Warehouse Institute, MJ Scarbrough
11
Administration & Operations Layer
(Yellow)
• BI Architecture
– Framework, standards, & conventions that describe
BI environment components
• Business applications
– Business processes & procedures that access/receive
information for results
• Data Resource Administration
– Data governance, including data owner, steward, and
custodian
• BI & DW/DM Operations
– Execution, monitoring, and maintaining quality,
availability, and performance
Credits: The Data Warehouse Institute, MJ Scarbrough
12
Administration & Operations Layer
Goals
•
•
•
•
•
•
Business is connected with the data
Decision makers have access to information
Business analytics support business objectives
Information is of high quality
Information services & resources are reliable
BI services adapt & change with the business
Credits: The Data Warehouse Institute, MJ Scarbrough
13
Infrastructure (Data Store) Layer
(Blue)
• BI Implementation & Infrastructure
– Technical components needed to:
• Capture data
• Turn data into value-added information
• Deliver information to the business
Credits: The Data Warehouse Institute, MJ Scarbrough
14
Infrastructure Layer
Goals
• Data manipulation & derivation standards
• Identify data gaps
• Ensure the business information is integrated
with the technology
• Engineer the DW/DM for to meet business
objectives
• Data integration
• Data quality
• DW/DM acting as a successful technology to
support BI
Credits: The Data Warehouse Institute, MJ Scarbrough
15
Information Services Layer
(Orange)
• BI Analytical Components that place
information into business function context
(i.e CRM, SCM, BPM, etc.).
• Would include:
– Static Reporting
– Dashboards
– Scorecards
Credits: The Data Warehouse Institute, MJ Scarbrough
16
Information Services Layer
Goals
• Information Services turn DW/DM results
into the following BI products:
– Query & reporting
– OLAP (Online Analytical Processing)
– DSS/EIS (Decision Support
System/Executive Information System)
– Analytic Applications
– Data Mining
Credits: The Data Warehouse Institute, MJ Scarbrough
Information Services Layer
Implementation
The Landscape for Analytical Tools
Credits: The Data Warehouse Institute, MJ Scarbrough
18
BI Technology Framework
CRM Analytics
Desktop
Email
Wireless
Analytic Applications
BPM (Scorecards & Dashboards)
Supply Chain Analytics
Operations Analytics
Analytic Apps Development
Tools, Packages, Templates
B2E Portal (Intranet)
Infrastructure
Web
Collaboration
Email, Groupware, Workflow
Data Access & Analysis
Query, Reporting, OLAP, Mining,
Forecasting
Text Analysis
Text Search & Text Mining
Data Warehouse / Data Marts
Content Management
Data Integration
B2B & B2C Portals (Internet/Extranet)
Storage, Servers, Databases, Metadata, Administration & Management, Networking
TDWI Business Intelligence Technology Framework: April 2004
Modeling, Mapping, Cleansing, ETL
Data Resources
Operational Systems, Documents, Images, External Data, Audio/Visual
Credits: The Data Warehouse Institute, MJ Scarbrough
19
BI Organizational Framework
TDWI Business Intelligence Organizational Framework: April 2004
BI Program
Sponsorship
Program Management
Data Governance
BI Projects
Database Development
ETL Development
Project Management
Data Integration & Cleansing
Data Access, Analysis & Mining
Business Metrics Usage
System & Database Administration
Process Execution & Monitoring
Training & Support
Data Mart Development
Metadata Management
Business Requirements Definition
BI Operations
Source Data Analysis
Business Rules Specification
Integration Design
BI Application Development
Architecture Specification
Quality Management
Credits: The Data Warehouse Institute, MJ Scarbrough
20
BI Corporate Governance
Structure
1. Establish Corporate BI Steward
•
Accountability for overall information management
2. Establish a Corporate Domain
Responsible for BI/IM P&P
3. Establish Corporate Custodianship for
Enforcement/Exception Approval
•
Programs, Projects, Operations
21
BI/IM Governance
Risks
1.
Business decisions executed on inaccurate data and/or models
maintained in data stores.
2.
Increased costs and overhead associated with line of business
vs. consortium, enterprise purchases of BI-related software and
hardware.
3.
Incompatibility of data elements and systems when information
is shared between lines of business and networks, impeding the
ability to successfully cross sell products across lines of
business.
4.
Non-alignment of innovation and/or research and development
within lines of business, resulting in unsupported, non-compliant
technologies and inefficient use of resources.
5.
Data integrity compromised due to a lack of uniform data and/or
its associated dimensions across lines of business and/or
system platforms.
22
BI/IM Governance
Risks
6.
Impediment of executive information and customer strategies
due to disparate, non-uniform policies, procedures, and
guidelines among lines of business.
7.
Incomplete or inaccurate data resulting from inadequate
monitoring of interfacing data feeds originating from the systems
of record.
8.
Exposure of private and protected customer information
maintained outside of business line systems of record.
9.
Increased latency resulting from inefficient and/or
undocumented design and structure of a non-uniform BI
environment.
10. Unavailability resulting from unstructured maintenance,
insufficient change control & problem management, and/or
untested designs or data structures among BI-reliant systems.
23
Business Layer
Risks
• Data store and BI is not in line with the business
organization's strategic objectives and is not meeting
business requirements.
• Insufficient resources, training (e.g. money, people,
funding) to meet business objectives
• Corporate strategy not keeping pace with business
needs or Business strategy not in line with Corporate
Information Strategy resulting in inefficient ROI for
Corporate Information Management resources.
24
Business Layer
Exposures
• Poor Technology ROI
• Bad Business Decisions
25
Business Layer
Controls
• Corporate AND LOB BI Steering & Data Strategy
Committees (PO1, O3)
• Alignment w/Corporate Strategy (P01, O3)
• Build & Model the Strategy (P01,O3)
• Establish/measure milestones associated w/strategy
(M1)
• Track performance & errors
• Training (P05, O7)
• Funding (PO5, O7)
26
Administration & Operations Layer
Risks
• Insufficient data sources required for an effective
decision support system.
• Data may be accessed by persons without a business
need to know.
• Noncompliance with the Privacy, and Solicitation policies
can lead to criticism and/or penalties from regulators and
adverse publicity. Failure to have sufficient monitoring
and effectiveness testing in place increases the risk that
circumstances of noncompliance will go undetected.
• Insufficient resources and training of team members
supporting the data store can impact system availability,
data integrity, BI accuracy, etc.
27
Administration & Operations Layer
Risks (cont’d)
• Data(BI) is not defined with regards to the owner,
purpose, its use, alignment, lineage, transformation.
• After a business disruption key systems are not
recovered in a timely and effective manner.
• Inadequate capacity due to a lack of BI utilization &
performance monitoring.
• Changes to BI systems and applications are made which
are not authorized, tested, or appropriate.
• Problems are not resolved in a timely manner.
28
Administration & Operations Layer
Exposures
• Poor Technology ROI
• Bad Business Decisions
• Inefficient database operations and analysis
• System unavailability
• Poor data integrity
• Fraud
• Unauthorized Data Access/Alteration
• Disclosure of Confidential/Restricted Data
• Loss of customers
• Regulatory Violations  Fines
29
Administration & Operations Layer
Exposures (cont’d)
• Processing delays
• Excessive costs
• Inconsistent/unreliable MIS
• Untimely business decisions
30
Administration & Operations Layer
Controls
• Business requirements defined/documented/modeled (logical &
physical models) (PO10, DS11)
• Compliance w/Corporate P&P: Security, BI, IM
– Encryption (DS5, PO8)
– Access (DBA, User, Contractors, Archives) (DS1, 2, 5, 11, PO8)
– Operations (DS11)
– Change Control (AI6)
– Disaster Recovery (DS4)
– Archives (DS4)
• Data Classification, Governance, Stewardship, & Custodianship
(DS5)
• Monitoring of Performance & Capacity
31
Administration & Operations Layer
Controls
• Training (DS5, PO8)
• Metadata (DS11)
–
–
–
–
–
Field Definitions
Lineage
Transformations
Models
Report Logic & Fields
32
Infrastructure Layer
Risks
• Data transformations are not appropriate or consistent.
• Incomplete, inaccurate, untimely ETL processing results
in loss of data integrity.
• Data Quality issues not monitored on an ongoing basis.
• Lack of or poorly defined measurements/standards of
what quality data includes (i.e. integrity, accuracy, etc.).
33
Infrastructure Layer
Exposures
• Poor data integrity
• Operational Reporting Errors  Ineffective/ unreliable
MIS
• Data Gaps
• Poor Technology ROI
• Bad Business Decisions
• Unauthorized Data Access/Alteration
• Regulatory Violations  Fines
34
Infrastructure Layer
Controls
•
•
•
•
Transformations are modeled & tested regularly (DS11)
SLA’s w/Data Stewards (DS11)
Control totals/CRCs integrated into ETL (DS11)
Data quality standards documented & monitored (DS11)
35
Information Services Layer
Risks
• Data within the ad-hoc environment is used outside of
the chartered use
• Data is analyzed inefficiently and/or ineffectively
• Inappropriate deployment/misalignment of analytical
tools with specified business need
• Dashboard reports are not timely, do not have data
integrity, lack consistency, do not meet business
requirements
• Inconsistent business definitions
• Dashboard reports are not timely, do not have data
integrity, lack consistency, do not meet business
requirements
36
Information Services Layer
Exposures
• Poor data integrity
• Operational Reporting Errors  Ineffective/ unreliable
MIS
• Inappropriate data analysis conclusions
• Underutilization or misuse of analysis tools\
• Bad Business Decisions
• Poor ROI
37
Information Services Layer
Controls
• Operational report requirements documented & tested
regularly (DS11)
• Formal process established for requests to obtain data
from the approved data store. (DS11)
– Is it going to be used for production (e.g. financial reporting)
• Training
• Communications
• Use of Analysis Tools
– Access (DS5)
– Purpose
38
Information Services Layer
Controls (cont’d)
• Monitoring & QA Testing of Analysis (PO11)
• Project management P&P (PO10)
• Metadata (DS11)
–
–
–
–
–
Lineage
Purpose
Definitions
Structure
Model
• Funding
– Expensive $$$$
39
Metadata Classifications
•
•
•
•
Business
Process
Technical
Application
40
Business Metadata
•
•
•
•
•
•
Business definitions
Data structures & hierarchy
Aggregation rules
Ownership characteristics
Subject areas
Business-rule-based transformation
rules
• Definitions of business objectives &
associated metrics
41
Business Metadata Specifics
• Definitions
– Data
– Metrics
• Models
– Subject
– Data
• Rules
– Business
– Data
• Data Owners & Stewards
42
Process Metadata
• Origins of data  Data lineage
– SORs
– Databases
• “The When”
– Schedule, frequency, & history of data
captured
• “The How”
– Tools used for transformations
– How is the data loaded
43
Process Metadata Specifics
• Source/Target Maps
• Rules
– Transformations
– Data cleansing
• Audit trails
– Extracts
– Transformations
– Loads
• Data quality audits
44
Technical Metadata
• Data element descriptions of:
– Physical locations
– Formats
– Data types
• Data file & table structures
• Database index schemes
• Implementation of data transformation
rules
45
Technical Metadata Specifics
• Data
– Locations
– Formats
– Sizes
– Types
– Structures
• Technical names
• Indexing
46
Application Metadata
•
•
•
•
•
How data is accessed & used
When data is accessed
How frequent data is used
Who is using the data
Who is authorized to access the data
47
Application Metadata Specifics
• Data access history
– Who?
– Frequency?
– When?
– How?
48
Metadata Sources
• ETL & data quality tools provide:
– Data transformation rules
– Load statistics
– Data lineage
– Program dependencies
– Capturing of process metadata
• Data modeling tools provide
– Logical & physical models
– Entity and attribute definitions
49
Metadata Sources (cont’d)
• Analytic tools
– Application metadata
• Vendor applications
– Data dictionaries
– Logical & physical models
• Business documentation
– Business policies & rules
– Data ownership & stewardship
– Definitions of business terms, products,
processes, metrics, etc.
50
Summary
• BI Background
• BI Framework
• BI Risks – Exposures – Controls




Business
Administration & Operations
Infrastructure
Information Services
• Metadata Types & Sources

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Business
Process
Technical
Application
Questions?