Data Management Journey
EIM in the UAE
DAMA Ottawa
2011
Also Known As…
Bill and Ted’s
Excellent
Adventure
…The End
Ted
Data Management Journey
EIM in the UAE
DAMA Ottawa
2011
There Was A Change Of Locale
Key Questions We’ll Answer At The End
1.
What was the catalyst for change?
2.
Was a business case made, what was involved, what level in the
organization reviewed and approved it?
3.
How did NBAD get their EIM program going?
4.
How was the maturity assessment conducted (how long, to whom
and ease of administration)?
5.
Did the DAMA maturity assessment help develop the roadmap and
set priorities?
6.
Were there culture/adoption challenges and how were they
addressed?
My Background
•
Manager/Architect EIM
•
DW/BI Architect
•
Data Modeller/Architect
•
DBA
•
Manager
•
Team Lead
•
Analyst
•
Programmer
•
Data Centre Operator
About The UAE
The Burg Khalifa
•
Hot – 50+ in the summer, 23 in the winter
•
10M People…Ethnic Groups:
•
16.5% – Emirati
•
23% – Other Arabs, Iranian
•
60.5% – South Asian, Indian, Pakistani,
Bangladeshi, Chinese, Filipino, Thai,
Westerners
•
World Oil Reserves
•
7.25% UAE
B/E ~= $1.50/barrel
•
13.21% Canada B/E ~= $50/barrel
•
HNWI – High Net Worth Individuals
Tom Cruise
Mission
Impossible:
Ghost
Protocol
The Trucial States
(1971)
About NBAD
•
Incorporated 1968
•
5,500 people internationally
•
70% owned by the Abu Dhabi
Investment Council
•
Second largest UAE bank …
Total Assets ~US$67bn
•
NBAD Academy trains staff in
core banking skills and IT
courses as well
•
A safe bank
•
One of the best employers in
the UAE…
NBAD Org Chart
Srood Sherif
CIO of the Year
Agent of Change
Chairman of the Board
Chief Executive Officer
Chief Operating Officer
Chief Information Officer
Deputy CIO & Head of Strategy and
Planning
•
Has good advisors
•
Understands what’s required & the effort
•
Hires the right staff
•
Delegates decisions; listens to advice; makes decisions
•
Has the respect of senior executives throughout the
organization
Catalyst For Change?
•
DCIO recognized...
•
Lots of systems…new requirements never stop
•
IT Staff head count had been growing
•
To compete locally and internationally
...
“We either work harder, or we work smarter”
•
DCIO hired…
1.
Group Leader – IT Strategy & Planning
2.
Group Leader – ITIL
3.
Group Leader – PMO
4.
Group Leader – Project Managers
5.
Manager, EIM
Manager EIM – Job Scope
•
Optimize the economics of corporate data management by ensuring
that existing practices are efficient and scalable.
•
Enhance the business return on existing data assets by ensuring that
data is kept in an accurate, coherent and accessible manner.
•
Control and mitigate risks in the areas of – data theft, data privacy,
data vandalism, data incoherence, data obsolescence, data loss, and
data inaccessibility.
•
Plan and manage a data governance framework in close
co-ordination with corporate governance unit (e.g. audit, compliance,
risk management, strategic planning, etc.).
“A Green Field Opportunity”
•
The head hunter said it was a green field opportunity...
•
Number of systems ~= 160
•
Data Models = 0
•
Data Dictionary = A start up as a Wiki
•
DBMS = 3
•
DW/BI project was in SW/HW procurement phase
• DW/BI expertise = 0
•
Data Security was in place
A Green Field Opportunity
Across The Empty Quarter
Great Journeys
(Rub' al Khali)
Milestones in the Journey
1.
Choose DAMA DM-BOK
Marketing Campaign &
Self Measurement
Marketing Campaign &
Self Measurement
Data Governance
The exercise of
authority and control
(planning, monitoring and
enforcement)
over the
Management
Of
Data Assets
Data
Governance
Data Architecture Management Data Development Data Operations Management Data Security Management Reference and Master DW and BI Document and Content Management Meta-data Management Data Quality ManagementDM-BOK 1 of 10
Data Architecture Management
Defining the data needs of
the enterprise
and
designing the
master blueprints
to meet those needs
Data
Governance
Data Architecture Management Data Development Data Operations Management Data Security Management Reference and Master Data Management DW and BI Management Document and Content Management Meta-data Management Data Quality ManagementDM-BOK 2 of 10
Designing, implementing
and maintaining
solutions to meet
the data needs
of the enterprise through
coordination
of individual project
data-related
analysis and design
Data
Governance
Data Architecture Management Data Development Data Operations Management Data Security Management Reference and Master DW and BI Document and Content Management Meta-data Management Data Quality ManagementDM-BOK 3 of 10
Data Operations Management
Planning, control and
support for
structured data
assets across the
data lifecycle
Data
Governance
Data Architecture Management Data Development Data Operations Management Data Security Management Reference and Master Data Management DW and BI Management Document and Content Management Meta-data Management Data Quality ManagementDM-BOK 4 of 10
Planning, development and
execution of
security policies
and procedures
to provide proper
• Authentication
• Authorization
• Access
• Auditing
Data
Governance
Data Architecture Management Data Development Data Operations Management Data Security Management Reference and Master DW and BI Document and Content Management Meta-data Management Data Quality ManagementDM-BOK 5 of 10
Planning, definition and
control activities to
ensure consistency with
the golden version
of contextual data values
Data
Governance
Data Architecture Management Data Development Data Operations Management Data Security Management Reference and Master Data Management DW and BI Management Document and Content Management Meta-data Management Data Quality ManagementDM-BOK 6 of 10
Planning, implementation
and control process to
provide
decision support data
and to
support knowledge workers
engaged in reporting,
query and analysis
Data
Governance
Data Architecture Management Data Development Data Operations Management Data Security Management Reference and Master DW and BI Document and Content Management Meta-data Management Data Quality ManagementDM-BOK 7of 10
Document and Content Management
Planning, implementation
and control activities to
store, protect and access
unstructured data
found within electronic files
and physical records.
This includes text, graphics,
images, audio and video
Data
Governance
Data Architecture Management Data Development Data Operations Management Data Security Management Reference and Master Data Management DW and BI Management Document and Content Management Meta-data Management Data Quality ManagementDM-BOK 8 of 10
Metadata Management
Planning, implementation
and control activities to
enable easy access
to high quality, integrated
metadata, and
connect from it
to relevant enterprise
information
Data
Governance
Data Architecture Management Data Development Data Operations Management Data Security Management Reference and Master Data Management DW and BI Management Document and Content Management Meta-data Management Data Quality ManagementDM-BOK 9 of 10
Data Quality Management
Planning, implementation and control
activities that apply quality
management techniques to
• Assess
• Improve
• Measure
• Ensure the fitness of
data for use.
Data
Governance
Data Architecture Management Data Development Data Operations Management Data Security Management Reference and Master Data Management DW and BI Management Document and Content Management Meta-data Management Data Quality ManagementAccuracy
Completeness
Consistency
Latency
Precision
Privacy
Reasonableness
Timeliness
Uniqueness
Validity
Referential Integrity
DM-BOK 10 of 10
Milestones in the Journey
1.
DAMA DM-BOK
2.
Marketing Campaign
Capability Maturity Model
(SEI CMU CMM) For EIM
0 - Non-Existent/Not Defined
1 - Initial/Ad Hoc
There is evidence that the enterprise has recognised that issues exist and need to be addressed. There
are, however, no standardised processes; instead there are ad hoc approaches that tend to be applied
on an individual or case-by-case basis.
2 - Repeatable
Processes have developed to the stage where similar procedures are followed by different
people undertaking the same task. There is no formal training or communication of standard
procedures, and responsibility is left to the individual. There is a high degree of reliance on
the knowledge of individuals
3 - Defined
Procedures have been standardized and documented, and communicated
through training. It is, however, left to the individual to follow these processes,
and it is unlikely that deviations will be detected.
Information assets are perceived as necessary for improved business
performance.
4 - Managed
It is possible to monitor and measure compliance with procedures
and to take action where processes appear not to be working
effectively. Processes are under constant improvement and provide
good practice.
Information is perceived as a critical component of the business.
5 - Optimizing
Processes have been refined to a level of best practice
based on the results of continuous improvement. IT is
used in an integrated way to automate the workflow,
providing tools to improve quality and effectiveness,
making the enterprise quick to adapt.
Information is recognized as a competitive differentiator
and source of operational efficiency.
Capability Maturity Model
Adapted to
Enterprise Information Management
‘Detailed Comments’
Capability Maturity Model
(SEI CMU CMM) For EIM
1 - Initial/Ad Hoc
There is evidence that the enterprise has recognised that issues exist and need to be addressed. There
2 - Repeatable
Processes have developed to the stage where similar procedures are followed by different
people undertaking the same task. There is no formal training or communication of standard
procedures, and responsibility is left to the individual. There is a high degree of reliance on
the knowledge of individuals
3 - Defined
Procedures have been standardized and documented, and communicated
through training. It is, however, left to the individual to follow these processes,
and it is unlikely that deviations will be detected.
Information assets are perceived as necessary for improved business
performance.
4 - Managed
It is possible to monitor and measure compliance with procedures
and to take action where processes appear not to be working
effectively. Processes are under constant improvement and provide
good practice.
Information is perceived as a critical component of the business.
5 - Optimizing
Processes have been refined to a level of best practice
based on the results of continuous improvement. IT is
used in an integrated way to automate the workflow,
providing tools to improve quality and effectiveness,
making the enterprise quick to adapt.
Information is recognized as a competitive differentiator
and source of operational efficiency.
Capability Maturity Model
Adapted to
Enterprise Information Management
‘Detailed Comments’
5 - Optimizing
Processes have been refined to a level of best practice
based on the results of continuous improvement. IT is
used in an integrated way to automate the workflow,
providing tools to improve quality and effectiveness,
making the enterprise quick to adapt
.
Information is recognized as a competitive differentiator
and source of operational efficiency.
Capability Maturity Model
(SEI CMU CMM) For EIM
0 - Non-Existent/Not Defined
1 - Initial/Ad Hoc
Heroic Efforts.
2 – Repeatable
Very Busy.
3 – Defined
Awareness and Management Begins.
4 – Managed
Effective and Efficient.
5 – Optimizing
Continuous Improvement.
Capability Maturity Model
Adapted to
Enterprise Information Management
‘Descriptive Comments’
EIM 07 Data Warehouse and Business Intelligence Management
“Planning, implementation and control process to provide decision support
data, and to support knowledge workers engaged in reporting, query and
analysis.”
Maturity Assessment Sample
- DW/BI
0 - Non-Existent/Not
Defined 1 - Initial/Ad-hoc 2 - Repeatable 3 - Defined 4 - Managed 5 - Optimizing
There is no awareness that a data warehouse can provide business benefits. Reporting can be
characterized as
‘Operational Management’, emphasizing the daily operations of the organization.
Reporting can be characterized as ‘Spreadmarts’, with departmental data being extracted into spreadsheet applications.
The spreadsheets focus on operational reporting, with limited tactical reporting.
Data marts exist, and are used for operational and limited tactical reporting. Multiple Business Intelligence (BI) software suites are used for the same purposes.
An enterprise choice has been made for the BI Suite, but other suites continue to be used.
The departmental data marts are being retired. Multiple data warehouses exist, however an Enterprise Data Warehouse (EDW) also exists.
Two dimensional reporting and multi-dimensional cubes are deployed across the organization.
Operational, tactical and limited strategic reporting is done. Most reports are of lagging data, however some are predictive and have identified leading metrics.
Departmental data warehouses have been retired, and their data and reporting is done via the EDW.
One suite of Business Intelligence software is in use, and other suites have been replaced with the selected suite.
Advanced analytics and dashboards have been implemented. Strategic reporting is being facilitated by the EDW.
Matrix management of some DW/BI skilled
The EDW is mission critical, providing
information for operational, tactical and strategic decision making. The Help Desk provides first level support for EDW and BI questions. The BICC is second level support for EDW and BI questions.
Decision engines use the EDW data in their algorithms.
Strategic reports using leading measures are
EIM 07 Data Warehouse and Business Intelligence Management
“Planning, implementation and control process to provide decision support
data, and to support knowledge workers engaged in reporting, query and
analysis.”
Maturity Assessment - DW/BI
0 - Non-Existent/Not
Defined 1 - Initial/Ad-hoc 2 - Repeatable 3 - Defined 4 - Managed 5 - Optimizing
There is no awareness that a data warehouse can provide business benefits. Reporting can be
characterized as
‘Operational Management’, emphasizing the daily operations of the organization.
Reporting can be characterized as ‘Spreadmarts’, with departmental data being extracted into spreadsheet applications.
The spreadsheets focus on operational reporting, with limited tactical reporting.
Data marts exist, and are used for operational and limited tactical reporting. Multiple Business Intelligence (BI) software suites are used for the same purposes.
An enterprise choice has been made for the BI Suite, but other suites continue to be used.
The departmental data marts are being retired. Multiple data warehouses exist, however an Enterprise Data Warehouse (EDW) also exists.
Two dimensional reporting and multi-dimensional cubes are deployed across the organization.
Operational, tactical and limited strategic reporting is done. Most reports are of lagging data, however some are predictive and have identified leading metrics.
A Business Intelligence Competency Center (BICC) is under consideration.
Departmental data warehouses have been retired, and their data and reporting is done via the EDW.
One suite of Business Intelligence software is in use, and other suites have been replaced with the selected suite.
Advanced analytics and dashboards have been implemented. Strategic reporting is being facilitated by the EDW.
Matrix management of some DW/BI skilled resources exists. The technical resources in the BICC are under one manager.
The EDW is mission critical, providing
information for operational, tactical and strategic decision making. The Help Desk provides first level support for EDW and BI questions. The BICC is second level support for EDW and BI questions.
Decision engines use the EDW data in their algorithms.
Strategic reports using leading measures are continually being refined.
3 – Defined
Awareness & Mgmt Begins
The departmental data marts are
being retired. Multiple data
warehouses exist, however an
Enterprise Data Warehouse
(EDW) also exists.
Two dimensional reporting and
multi-dimensional cubes are
deployed across the organization.
Operational, tactical and limited
strategic reporting is done. Most
reports are of lagging data,
however some are predictive and
have identified leading metrics.
A Business Intelligence
Your Milestone in the Journey
Maturity Assessment
3 Weeks – Design the Maturity Assessment
4 Weeks – Interviews: 14 middle management, 12 ITD staff
– Write report
DAMA Functional Area Score
01 Data Governance
1.0
1.0
02 Data Architecture Management
2.5
2.5
03 Data Development
1.0
1.0
04 Data Operations Management
2.5
2.5
05 Data Security Management
1.0
1.0
06 Reference and Master Data Mgmt
2.5
2.5
07 DW/BI Management
1.0
1.0
08 Document and Content Mgmt
2.5
2.5
09 Meta-data Management
1.0
1.0
10 Data Quality Management
2.5
1.0
0.0 0.5 1.0 1.5 2.0 2.5 01 Data Governance 02 Data Architecture Management 03 Data Development 04 Data Operations Management 05 Data Security 07 DW/BI Management
08 Document and Content Mgmt
09 Meta-data Management
10 Data Quality Management
Your Milestone in the Journey
Maturity Assessment
0
1
2
3
4
5
Today
Unaware
Heroic
Efforts
Very Busy
Awareness
& Mgmt
Effective &
Efficient
Continuous
Improvement
Your
Average
Milestones in the Journey
1.
DAMA DM-BOK
2.
Marketing Campaign
3.
Maturity Assessment
Roadmap = Next Steps
Roadmap design was based on three things we knew...
1.
Strategic Objectives for EIM
•
Optimize the economics
•
Enhance the business return
•
Control and mitigate risks
•
Plan and manage a data governance framework
2.
Maturity Assessment
3.
EIM Projects – existing and in the pipeline
•
DW/BI project was getting ready to start
•
CRM project was getting ready to start
Technology & Skill Requirements
Project: DW/BI
•
Staff Skill sets – EIM-CC*:
•
DBA
•
Data Migration developers
•
BI developers
•
Predictive Analytics Specialist
•
Data Quality Analyst
•
Data Modeller
•
MDM Architect
•
Metadata Architect
•
Data Governance Coordinator
•
EIM Technology
•
DBMS
•
Data Migration software
•
BI software
•
Data Mining software
•
Data Quality software
•
Data Modelling software
•
Master Data Management
Technology & Skill Requirements
Project: CRM
•
Staff Skill sets – EIM-CC*:
•
DBA
•
CRM Developers
•
Predictive Analytics Specialist
•
Java / C# developers
•
Data Quality Analyst
•
Data Modeller
•
MDM Architect
•
Metadata Architect
•
Data Governance Coordinator
•
EIM Technology
•
DBMS
•
CRM Software
•
Data Mining software
•
Enterprise Service Bus
•
Data Quality software
•
Data Modelling software
•
Master Data Management
Prioritize The DAMA 10
For The Roadmap
Inventory Required
Who – What – When – Where – Why
Guidance Required
Understand what we own, guide current &
future initiatives – blueprint
Management Required
Customer info and codes are everywhere
Facilitation Required
Engage business in the management of their
data
ROI
Predictive Analytics delivers insight and
value
ROI
Enables better decision making
1.
Metadata Management
2.
Data Architecture Management
3.
Reference and Master Data
Management
4.
Data Governance
5.
DW/BI Management
6.
Data Quality Management
Prioritize The DAMA 10
For The Roadmap
Inventory Required
Who – What – When – Where – Why
Guidance Required
Understand what we own, guide current &
future initiatives – blueprint
Management Required
Customer info and codes and were
everywhere
Facilitation Required
Engage business in the management of their
data
ROI
Predictive Analytics delivers insight and
value
ROI
Enables better decision making
1.
Metadata Management
2.
Data Architecture Management
3.
Reference and Master Data
Management
4.
Data Governance
5.
DW/BI Management
6.
Data Quality Management
Data Development………..… Waiting For Data Architecture
Data Operations, Data Security,
Roadmap Pulls EIM all Together
EIM Functional Area
2010 - Q2
2010 - Q3
2010 - Q4
2011 - Q1
2011 - Q2
2011 - Q3
2011 - Q4
2012 - Q1
2012 - Q2
01 Data Governance
02 Data Architecture Management
03 Data Development
04 Data Operations Management
05 Data Security Management
06 Reference and Master Data Mgmt
07 DW/BI Management
08 Document and Content Mgmt
09 Meta-data Management
10 Data Quality Management
Roadmap
•
What
to do
•
When
to do it
Roadmap – We Like Plans
EIM Functional Area 2010 - Q2 2010 - Q3 2010 - Q4 2011 - Q1 2011 - Q2 2011 - Q3 2011 - Q4 2012 - Q1 2012 - Q2
01 Data Governance Hire Data Governance Analyst Develop structure Support DW/BI Support DW/BI Support MDM Support Metadata
02 Data Architecture Management
Hire Enterprise Data Architect Buy S/W Develop standards Subject Area 1 Customer Subject Area 2 Location Subject Area ### Subject Area ### Subject Area ### Subject Area ### 03 Data Development Hire Data Modellers Support DW/BI Support CRM
04 Data Operations Management
Develop DBA standards
05 Data Security Management External
review
06 Reference and Master Data Mgmt Hire MDM
Architect Architect Support DW/BI & CRM Implement MDM via CRM 07 DW/BI Management Hire Predictive Analytics Specialist Develop ETL & BI Standards Develop predictive models
08 Document and Content Mgmt Hire ECM
Specialist Develop First Taxonomy Integrate structured with unstructured Develop First Taxonomy 09 Meta-data Management Hire Metadata Architect Develop model Build ETL bridges Build ETL bridges and reports 10 Data Quality Management
Hire Data Quality
Support DW/BI &
Support
Predictive Support all LOB
Roadmap – Actual
EIM Functional Area 2010 - Q2 2010 - Q3 2010 - Q4 2011 - Q1 2011 - Q2 2011 - Q3 2011 - Q4 2012 - Q1 2012 - Q2
01 Data Governance Transfer and lose Data Governance Analyst Prepare Governance Structure 02 Data Architecture Management
03 Data Development
04 Data Operations Management 05 Data Security Management 06 Reference and Master Data Mgmt 07 DW/BI Management
08 Document and Content Mgmt 09 Meta-data Management 10 Data Quality Management
Data Governance & Stewardship
Proposed Structure
Executive Data Governance Committee
(IT Steering Committee)
Tactical Data Stewardship Committee
Steering & Work Committees – As Needed
Data
Development
Data
Operations
Data Security
Data
Architecture
Reference &
MDM
* CRM *
EDW
(DW & BI)
Document &
Content
Management
Meta-data
Data Quality
Executive Data
Governance
Champion
Chairman TDSC
Each
Committee
has A
TDSC
Member
as its
Chairman
Roadmap – Actual
EIM Functional Area 2010 - Q2 2010 - Q3 2010 - Q4 2011 - Q1 2011 - Q2 2011 - Q3 2011 - Q4 2012 - Q1 2012 - Q2
01 Data Governance Transfer and lose Data Governance Analyst Prepare Governance Structure
02 Data Architecture Management Recruit
Job offer and lose Enterprise
Data Architect
Recruit Recruit Recruit
Job offer to new candidate 03 Data Development
04 Data Operations Management 05 Data Security Management 06 Reference and Master Data Mgmt 07 DW/BI Management
08 Document and Content Mgmt 09 Meta-data Management 10 Data Quality Management
Roadmap – Actual
EIM Functional Area 2010 - Q2 2010 - Q3 2010 - Q4 2011 - Q1 2011 - Q2 2011 - Q3 2011 - Q4 2012 - Q1 2012 - Q2
01 Data Governance Transfer and lose Data Governance Analyst Prepare Governance Structure
02 Data Architecture Management Recruit
Job offer and lose Enterprise
Data Architect
Recruit Recruit Recruit
Job offer to new candidate
03 Data Development
04 Data Operations Management 05 Data Security Management
06 Reference and Master Data Mgmt
Transfer Senior Analyst Masters degree in Banking Train eLearningCurve Architect Analyse Design Support CRM 07 DW/BI Management
08 Document and Content Mgmt 09 Meta-data Management 10 Data Quality Management
06 Reference & MDM Startup
1.
Recruit externally, then transfer from within ITD
2.
eLearningCurve online education + certification
3.
Work with CRM project as it goes through the Initiation Phase
4.
A Favorite Book:
Enterprise Master Data Management:
An SOA Approach to Managing Core Information
Roadmap – Actual
EIM Functional Area 2010 - Q2 2010 - Q3 2010 - Q4 2011 - Q1 2011 - Q2 2011 - Q3 2011 - Q4 2012 - Q1 2012 - Q2
01 Data Governance Transfer and lose Data Governance Analyst Prepare Governance Structure
02 Data Architecture Management Recruit
Job offer and lose Enterprise
Data Architect
Recruit Recruit Recruit
Job offer to new candidate
03 Data Development
04 Data Operations Management 05 Data Security Management
06 Reference and Master Data Mgmt
Transfer Senior Analyst Masters degree in Banking Train eLearningCur ve Architect Analyse Design Support CRM
07 DW/BI Management Recruit Recruit
Hire Predictive Analytics Specialist Masters degree in Statistics 08 Document and Content Mgmt
09 Meta-data Management 10 Data Quality Management
07 DW/BI Startup - Predictive Analytics
1.
Recruit externally (Australia)
2.
Acquire software: SAS Data Miner
3.
Assist the EDW project’s data quality needs
4.
Seek & Find: Predictive Analytics opportunity in our customer
databases
Roadmap – Actual
EIM Functional Area 2010 - Q2 2010 - Q3 2010 - Q4 2011 - Q1 2011 - Q2 2011 - Q3 2011 - Q4 2012 - Q1 2012 - Q2
01 Data Governance Transfer and lose Data Governance Analyst Prepare Governance Structure
02 Data Architecture Management Recruit
Job offer and lose Enterprise
Data Architect
Recruit Recruit Recruit
Job offer to new candidate
03 Data Development
04 Data Operations Management 05 Data Security Management
06 Reference and Master Data Mgmt
Transfer Senior Analyst Masters degree in Banking Train eLearningCur ve Architect Analyse Design Support CRM
07 DW/BI Management Recruit Recruit
Hire Predictive Analytics Specialist Masters degree in Statistics 08 Document and Content Mgmt
09 Meta-data Management Transfer Senior Analyst Masters degree in Engineering Train eLearningCurve Develop model Build ETL bridges and reports 10 Data Quality Management
09 MetaData Management Startup
1.
Recruit externally, then transfer from within ITD
2.
eLearningCurve online education + certification
3.
System Architect for the Metadata Repository Data Model
4.
Began with bridges to DBMS dictionaries to capture table/column
metadata to support the EDW project
5.
A Favorite Book:
Building and Managing the Meta Data Repository:
A Full Lifecycle Guide
Roadmap – Actual
EIM Functional Area 2010 - Q2 2010 - Q3 2010 - Q4 2011 - Q1 2011 - Q2 2011 - Q3 2011 - Q4 2012 - Q1 2012 - Q2
01 Data Governance Transfer and lose Data Governance Analyst Prepare Governance Structure
02 Data Architecture Management Recruit
Job offer and lose Enterprise
Data Architect
Recruit Recruit Recruit
Job offer to new candidate
03 Data Development
04 Data Operations Management 05 Data Security Management
06 Reference and Master Data Mgmt
Transfer Senior Analyst Masters degree in Banking Train eLearningCur ve Architect Analyse Design Support CRM
07 DW/BI Management Recruit Recruit
Hire Predictive Analytics Specialist Masters degree in Statistics 08 Document and Content Mgmt
09 Meta-data Management Transfer Senior Analyst Masters degree in Engineering Train eLearningCur ve Develop model Build ETL bridges and reports
10 Data Quality Management Recruit
Predictive Analytics Specialist
Data Quality
Data Quality Management Unit
Business Case
1.
Six Functions
1. Profile, monitor and report on
data quality
4. Create service requests for ITD to change software in order to
correct the root cause of the problem
2. Correct data in situ if within the
DQMU mandate
5. Recommend changes to LOB business rules – i.e. processes
and procedures – to prevent data quality problems
Data Quality Management Unit
Business Case
1.
Six Functions
2.
Establish the Data Quality Management Unit, independent of
LOBs
GCOO
Head – Data Quality Management Unit
LOB Experts Data Quality Analyst EIM Data Governance
GCOO
Head – Data Quality Management Unit
LOB Experts Data Quality Analyst EIM Data Governance Operations
Or
1. Profile, monitor and report on
data quality
4. Create service requests for ITD to change software in order to
correct the root cause of the problem
2. Correct data in situ if within the
DQMU mandate
5. Recommend changes to LOB business rules – i.e. processes
and procedures – to prevent data quality problems
Key Questions
1.
What was the catalyst for change?
2.
Was a business case made and what was involved in that and what
level in the organization reviewed and approved it?
3.
How did NBAD get their EIM program going?
4.
How was the maturity assessment conducted (how long, to whom
and ease of administration)?
5.
Did the DAMA maturity assessment help develop the roadmap and
set priorities?
6.
Were there culture/adoption challenges and how were they
addressed?
Key Questions
1.
What was the catalyst for change?
•
Fast pace of change in business applications
•
Drowning in data but thirsting for information
•
DCIO “Agent of Change” was hired
2.
Was a business case made, what was involved, what level in the
organization reviewed and approved it?
•
Yes… ROI and risk is always examined
Key Questions
2.
Was a business case made…Job Scope said
•
Optimize the economics of corporate data management by ensuring
that existing practices are efficient and scalable
Business
Alignment &
Improvement
Key Questions
2.
Was a business case made…Job Scope said
•
Enhance the business return on existing data assets by ensuring that
data is kept in an accurate, coherent and accessible manner
•
Plan and manage a data governance framework in close co-ordination
with corporate governance unit (e.g. audit, compliance, risk
management, strategic planning, etc.).
IT
Efficiency
Business
Alignment &
Improvement
Risk
Management
Key Questions
2.
Was a business case made…Job Scope said
•
Control and mitigate risks in the areas of – data theft, data privacy,
data vandalism, data incoherence, data obsolescence, data loss, and
data inaccessibility
Business
Alignment &
Improvement
Key Questions
2.
Was a business case made…
•
Balance of all three
IT
Efficiency
Business
Alignment &
Improvement
Risk
Management
Key Questions
3.
How did NBAD get their EIM program going?
•
Hired me
4.
How was the maturity assessment conducted (how long, to whom
and ease of administration)?
•
4 weeks, two people
•
Interviews repeated themselves – same questions, same answers
•
Question
Question
Question
Question… Electronic Survey or an Interview?
Key Questions
5.
Did the DAMA maturity assessment help develop the roadmap and
set priorities?
•
Roadmap = Job Scope + Maturity Assessment + Projects
EIM Functional Area 2010 - Q2 2010 - Q3 2010 - Q4 2011 - Q1 2011 - Q2 2011 - Q3 2011 - Q4 2012 - Q1 2012 - Q2 01 Data Governance Transfer and lose Data Governance Analyst Prepare Governance Structure
02 Data Architecture Management Recruit
Job offer and
lose
Enterprise Data Architect
Recruit Recruit Recruit
Job offer to new candidate
03 Data Development
04 Data Operations Management 05 Data Security Management 06 Reference and Master Data Mgmt
Transfer Senior Analyst Masters degree in Banking Train eLearningCurve Architect Analyse Design Support CRM
07 DW/BI Management Recruit Recruit
Hire Predictive Analytics Specialist Masters degree in Statistics
08 Document and Content Mgmt 09 Meta-data Management Transfer Senior Analyst Masters degree in Engineering Train eLearningCurve Develop model Build ETL bridges and reports Predictive
