towards sustainable data quality · maturity 1.data governance project launched ... measuring data...
TRANSCRIPT
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Towards Sustainable Data QualityData Management Maturity Assessments
Dr. Zbigniew „Zib” Korendo, CDQ AG
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After this session …
• … you can tell what role maturity assessments play in continuous improvement of data excellence management
• … you understand what insights and analysis results one can derive from maturity assessments
• … you know how to plan and conduct data maturity assessments
Learning objectivesContinuous improvement
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Assessment of data management performance must cover perspective of the entire end-to-end Data Supply Chain
Data Management
Governance FrameworkProcesses, Methods
Applications
Using
Function /
Process
Using
Function /
Process
CONSUMING
Functions /
Processes
Maintaining
Functions /
Processes
Maintaining
Functions /
Processes
SUPPLYING
Functions /
Processes
Data Quality provided
Data Quality required
DefiningData
QualityIoT
Digital
Automation
Compliance
GDPR
C&E
Accountable for Data Quality
DATA FITNESS FOR USE: the right quality at the right time at the right place
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Performance management addresses three areas
BUSINESS
STRATEGY
How well are we organized to manage data fitness for business use?
Time to process request for a new attribute
What is the actual quality of the data content, as per defined metrics and targets?
Delivery_Address completeness
e.g.
e.g.
Business processes
Data management PERFORMANCE AREAS
DRIVERS
DATA – Customer, Product,...
strategy governance
compliance processes monitoring
architecture
documents
training
What is the actual result of the business processes?
On-time delivery (OTD) indexe.g.
DRIVERS
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2
3
5
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8“How can we monitor transformation
progress and how does our speed andperformance compare to peers?”
Maturity assessment is an instrument for continuousimprovement of data management excellence
“How can we improve? What are the best practices?”
Data Management
Maturity
1.Data Governance project launched2.Global process harmonization project launched3.Data quality metrics launched4.Company reorganized5.Data Governance council launched
2017 2018 2019 2020
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GOALS ENABLERS RESULTS
BUSINESS VALUE
DATAMANAGEMENT CAPABILITIES
DATA STRATEGY
PEOPLE, ROLES & RESPONSIBILITIES
PROCESSES & METHODS
DATA LIFECYCLE
DATAAPPLICATIONS
DATA ARCHITECTURE
PERFORMANCE MANAGEMENT
BUSINESS CAPABILITIES
DATA EXCELLENCE
CDQ Data Excellence Model* defines key capability areas
* Free to use under the Creative Commons license
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Challenge: plan strategic roadmap and improvement measures
...but which areas should be addressed with the highest priority? Which capabilities need to be effective
when and in which sequence?
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The data management framework aims at providing managerial guidance for managing data in the digital age
The Data Excellence Model is a reference model for data management in the digital economy that
• offers support and guidance for practitioners in the implementation of data management
• Supports the transformation into a digital and data-driven company.
… to define and agree on basic terms
… to consistently communicate with stakeholders
… to understand the design areas & deliverables
… to build and share knowledge about good practices
… to assess the maturity of an organization
The Data Excellence Model supports data managers …
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Principal objectives of data maturity assessments
Assess the current status based on a holistic approach
Identification of strengths and capability gaps
Benchmark with others and consider their best practices
Plan actions to improve capabilities based on “what works”
Raise awareness in the company and strengthen data community
Improve communication between different stakeholders(Business, IT and Data Management functions)
Secure commitment and budget from decision-makers to drive corrective actions
Tool for a continuous improvement process for data quality mgmt.
Str
ate
gic
Goals
Tacti
cal
Goals
€
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Steps and deliverables of maturity assessment
2018 2019 2020
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1
Data governance
and ownership
Holistic view on data
EDM strategy
Activity
Optimize data
lifecycle processes
Performance and data
quality management
◼ Es ist keine Strategie für das Stammdatenmanagement definiert
◼ Ziele für das Stammdatenmanagement sind festgehalten
Ausgangssituation
◼ Entwicklung von strategischen Zielen für das Stammdatenmanagement
◼ Ableitung einer Strategie für das Stammdatenmanagement im Ganzen und für die
einzelnen Enabler-Dimensionen
◼ Dokumentation der Strategie in einem Strategiedokument
◼ Kommunikation der Strategie
◼ Erstellen einer Intranet-Präsenz für das Stammdatenmanagement
Zeitplanung
Massnahmen Zieldimension im Stammdatenmanagement
ResultsEnabler
Innovation and Learning
Cus-tomerResults
PeopleResults
Comp-liance
Results
Business Results
Controlling
Strategy
Organization & People
Applications
Data Architecture
Processes & Methods
2017
Q1 Q2 Q3 Q4
Kommunikation
Activity
Definition
MDM-Strategie
The questionnaire
contains 35 statements
in order to assess the
maturity level of data
management
R² = 0.7325
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 1 2 3 4 5 6 7 8 9 10 11 12
Re
ife
gra
d
Jahre seit der Einführung einer MDM Abteilung
Company Trend (target values)
5
4+
4
3+
3
2+
2
1
2010
2012
2017
2.4 2.4 2.4
2.6
2.3
2.1
2.72.6
1.92.0
3.2
1
2
3
4
5
To
tal
Go
als
Ena
ble
rs
Peo
ple
,
Role
s &
Respo
nsib
ilities
Pro
cesse
s &
Me
thods
Data
Lifecycl
e
Data
App
lica
tions
Data
Arc
hite
cture
Perf
orm
ance
Ma
nag
em
ent
Results
Contin
uou
s
Impro
vem
ent
Benchmarking of EDM Maturity Score
2017 Mean Top 25% Best in Class
Improvements and
innovation
Continuous
Improvement
Deriving
improvements of
data management
Data quality reporting ResultsSensitive data
reporting
Data management
value proposition
Business oriented
data quality metrics Measuring data quality
Measuring
sensitive data
Performance
ManagementPerformance metrics
Application
documentation
Tool integration
and usability
Application landscape
change management Applications
Unambiguous core
business objective
definition
Meta data
management
Data distribution and
integration
Data
architectureExternal data
integrationData classification
Requirements for data
lifecycle processes
Data lifecycle
definitions & guidelines Data lifecycle
Lean and efficient
processes
Processes and
methodsData governance
methods
Change
management
Data governance
processes
Top executive
support
Roles and
responsibilities
Employee data
management
awareness
Recognition Data management
trainings
People, roles
and responsibilities
Data management
strategy
Strategic project and
resource planning Business capabilities
Data-driven
business models Goals
Data management
capabilities
Interviews Benchmark RoadmapResults
“We need a marketing campaign for master data
management.”
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Each capability area is evaluated by a set of statements
Capability Area: People, Roles & Responsibilities
2.1Data management roles (for governance and data lifecycle processes), tasks, responsibilities and decision-making paths are documented, trained and executed by the role owners.
2.2(Top) executives show their support for data management through explicit actions, decisions and supportive statements.
2.3Employees understand the importance of data and data excellence. They are aware of the value and impact of data.
2.4Data management efforts to improve data management are actively promoted, appreciated and rewarded.
Capability Area: Continuous Improvement
9.1Improvement activities are derived and implemented, when data quality, performance, compliance, data privacy and data security targets are not met.
9.2All employees of the company have the possibility to initiate data management improvements and come up with innovations that generate added value from data.
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Guiding questions for Goals & StrategyWhat are the strengths and key areas for improvement?
BUSINESS VALUE
DATAMANAGEMENT CAPABILITIES
DATA STRATEGY
PEOPLE, ROLES & RESPONSIBILITIES
PROCESSES & METHODS
DATA LIFECYCLE
DATAAPPLICATIONS
DATA ARCHITECTURE
PERFORMANCE MANAGEMENT
BUSINESS CAPABILITIES
DATA EXCELLENCE
Is there a strategic planning and coordination of data
management initiatives and related activities in place?
Are the requirements for data management identified and are the data management capabilities
derived from business requirements?
Are the strategic data management objectives and values documented? Do they
refer back to the corporate strategy?
Is a portfolio of data management capabilities in
place?
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Guiding questions for People & ProcessesWhat are the strengths and key areas for improvement?
BUSINESS VALUE
DATAMANAGEMENT CAPABILITIES
DATA STRATEGY
PEOPLE, ROLES & RESPONSIBILITIES
PROCESSES & METHODS
DATA LIFECYCLE
DATAAPPLICATIONS
DATA ARCHITECTURE
PERFORMANCE MANAGEMENT
BUSINESS CAPABILITIES
DATA EXCELLENCE
Do all employees understand the importance of data?
Are definitions, documentationand guidelines for data
management processes are in place?
Are data management roles, tasks and responsibilities
documented, communicated and executed by the employees?
Are executives supportive of data management through
explicit actions and decisions?
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Data Lifecycle, Data Applications & ArchitectureWhat are the strengths and key areas for improvement?
BUSINESS VALUE
DATAMANAGEMENT CAPABILITIES
DATA STRATEGY
PEOPLE, ROLES & RESPONSIBILITIES
PROCESSES & METHODS
DATA LIFECYCLE
DATAAPPLICATIONS
DATA ARCHITECTURE
PERFORMANCE MANAGEMENT
BUSINESS CAPABILITIES
DATA EXCELLENCEAre the requirements for data
lifecycle processes derived from all relevant business processes and data users?
Are definitions and guidelines for the entire data lifecycle
processes in place?
Is sensitive data identified, classified and managed?
Do the applications facilitate and automate data
verification, maintenance, cleansing?
Are core business objectsdefined and well known
throughout the company?
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Performance Management, Results & Continuous ImprovementWhat are the strengths and key areas for improvement?
BUSINESS VALUE
DATAMANAGEMENT CAPABILITIES
DATA STRATEGY
PEOPLE, ROLES & RESPONSIBILITIES
PROCESSES & METHODS
DATA LIFECYCLE
DATAAPPLICATIONS
DATA ARCHITECTURE
PERFORMANCE MANAGEMENT
BUSINESS CAPABILITIES
DATA EXCELLENCE
Are the successesof data quality and of managing sensitive
data verified against targets and
communicated?
Is data quality measured by metrics?
Is the valuecontribution of data management to the
business reported and communicated?
Are improvement activities implemented, when data targets
are not met?
Is the performance and progress of data management measured?
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Quantitative result: overall maturity level of 3.1
3.1
2.72.9
3.3 3.3
2.5
3.4
2.3
3.03.2
1.00
2.00
3.00
4.00
5.00
Solid foundation for management of customer data and basic implementation progress. Medium improvement potential identifiable.
▪ Successful implementation in subdomain or pilot project
▪ Some indication of progress
2Certain
progress
▪ Excellent and comprehensive results in all areas of the topic
▪ High added value for users and stakeholders
5Fully
completed
▪ Initiatives have been imple-mented in almost all areas
▪ Clear evidence for successful implementation
4Significantprogress
▪ Basic approaches have been implemented in some areas
▪ Evidence of formalised and established procedures
3Average progress
▪ No initiatives are visible▪ Some valuable ideas,
however in general wishful thinking dominates
1Not yet started
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2.0 2.1 2.02.2
2.3
2.7
1.9
1.61.8
2.0
1
2
3
4
5
Strategic and tactical (governance) processes for STRUCTURES AND RULESare defined. Changemanagement - including communication plan –is in place.
2Certain
progress
5Fully
completed
4Significantprogress
3Average progress
1Not yet started
Data quality is subject of systematic corrective actions in response to identified data defects. Employees are involved in driving improvements of data management performance.
Common understanding and attributes of the key data objects are aligned with business stakeholders. Data flow and applications landscape is documented. Data quality is enabled bythe use of reference data.
Interpretation of maturity areas
Data Strategy is aligned with business strategy. Its implementation is ensured through coordinated activities. Scope, value proposition and target data-enabled insights/services are clear.
Business process requirements for CONTENT creation, updating and archiving of data is defined, along with detailed flow of activities and their owners. The date lifecycle process is found efficient by stakeholders.
Data governance is effective - data-related roles are specified by tasks, responsibilities and decision-making rights. The roles are staffed with people on adequate organization levels, whose motivation and skills are developed by structured trainings and top-down communication.
Data management is supported by applications for data governance, quality measurements, reporting, analytics, look-up as well as deduplication, cleansing and maintenance activities.
Data fitness for use is measured and reported with respect to the target business needs. Adequate attention is paid to sensitive data. Data value contribution to business strategic targets is actively communicated.
Data management is supported by applications for data governance, quality measurements, reporting, analytics, look-up as well as deduplication, cleansing and maintenance activities.
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Key takeaway: local “interpretations” and missing management support!
2.3
3.8
1
2
3
4
5
Matu
rit
y
sco
re
Need
fo
r
acti
on
Weaknesses
▪ Weak adherence to global regulations on local level
▪ High employee fluctuation
▪ Data management is not “in the heads” of top managers
▪ Data management as “side activity”
▪ Data management activities only partially appreciated and rewarded
Strengths
▪ Very knowledgeable and motivated employees as the basis for MDM
▪ Roles well-documented and (centrally) lived
▪ We have internal good practice for training and communication concept
Recommendations
▪ Review role model, its adaptability to local requirements and assignment process for responsibilities
▪ Detail and analyze pros and cons of regional hubs (instead of local responsibilities) for data entry and maintenance
▪ Revise communication and training approach, especially for regions with high employee change rate
▪ Empower and motivate employees for data management
• Integrate data management objectives into personal goals
• Identify, convince and adequately inform executive data management sponsor
“On board level, there are data management
ambassadors – and there are people who don’t care.”
“We need a
marketing campaign for master data
management.
”
“Master data are the DNA of our processes.”
Qualitative insights: understanding capability gaps
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Data quality reporting ResultsSensitive data
reporting Data management value proposition
Business oriented data quality metrics
Measuring data quality
Measuring sensitive data
Performance Management
Performance metrics
Application documentation
Tool integrationand usability
Application landscape change management
Applications
Unambiguous core business objective
definition
Meta data management
Data distribution and integration
Data architecture
Data classification
Requirements for data lifecycle processes
Data lifecycle definitions & guidelines
Data lifecycle Process efficiency
Processes and methods
Data governance methods & procedures
Change managementData governance
processes
Top executive support
Roles and responsibilities
Employee data management awareness
Recognition Data management
trainings
People, roles and responsibilities
Priority of corrective actions
Data management strategy
Strategic project and resource planning
Business capabilities Data-driven
business models GoalsData management
capabilities Portfolio of data and data mgmt services
Improvements and innovation
Continuous Improvement
Corrective actions for data quality
Data quality reporting
ResultsSensitive data
reporting Data management value proposition
Business oriented data quality metrics
Measuring data quality
Measuring sensitive data
Performance Management
Performance metrics
Application documentation
Tool integrationand usability
Application landscape change management
Applications
Common understanding
of core business objects
Metadata management
Data distribution and integration
Data architecture
External data integration
Requirements for data lifecycle processes
Data lifecycle definitions & guidelines
Data lifecycleLean and efficient
data processes
Processes and methods
Data governance methods
Change management
Data governance processes
Top executive support
Roles and responsibilities
Employee data management awareness
Recognition& personal targets
Data management trainings
People, roles and responsibilities
Data management strategy
Strategic project and resource planning
Business objectives
Data-drivenbusiness models Goals
Data management capabilities
Contribution to data products and services
* The lower Maturity level and the higher Urgency for Improvements – the higher Need for Action.
A
B
C
Priority* areas of improvement
A. Purposeful data strategy
B. Effective data governance
C. Proactive quality management
NEED for ACTION*
High need for action
Urgent need for action
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The clustered and prioritized recommendations defineactivity streams to improve data management capabilities
Data strategy and communication
Data Governance and Ownership
Data architecture and lifecycle processes
Performance and data quality management
Systematic improvements
Communication approach for data
strategy and business impact/results
Roles on-boarding and change
management plan
Refine training program for data
governance
Metadata governance process/tools
Validate data sensitivity requirements, incl. data model and quality rules
Revise the core data model, incl. obligation
and other business rules
Calibrate ownership allocation (processes,
rules, content)
Refine communication approach for data quality
transparency and corrective actions
Improve efficiency of corrective measures triggered
by DQ measurements
Introduce root cause analysis for proactive
improvements
Ensure business-oriented (fit for purpose)
data validation metrics
Establish regular performance measurements for data management processes
Continuous Improvement
Results
Performance Management
Data architecture
Applications
Data lifecycle
Processes and methods
People, roles and responsibilities
Goals
Facilitate common language and data literacy with business stakeholders
Revalidate data strategy against business strategy
Integrate enhanced data sources
Revisit GSD data interconnections with business applications
Ensure data-to-value awareness to sustain focus
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Data
Arc
hite
ctu
re
Data LifecylceProcesses &
Methods
Ap
plic
atio
n
s
Peo
ple
, ro
les a
nd
resp
on
sab
ilit
ies
Performance
Management
Go
als
ResultsContinuous
Improvement
Data Excellence
Five streams towards data excellence
Facilitate common language and data
literacy with business stakeholders
Revalidate data strategy against
business strategy
Roles on-boarding and change
management planCommunication approach for data strategy and
business impact/results
Refine training program for data
governanceCalibrate ownership
allocation (processes, rules,
content)
Integrate enhanced data sources
Validate data sensitivity requirements, incl. data model and quality rules
Revisit GSD data interconnections with business applications
Revise the core data model, incl. obligation
and other business rules
Metadata governance process/tools
Ensure business-oriented (fit for purpose)
data validation metrics
Ensure data-to-value awareness to sustain focus
Establish regular performance measurements
for data management processes
Improve efficiency of corrective measures
triggered by DQ measurements
Refine communication approach for data quality
transparency and corrective actions
Introduce root cause analysis for proactive
improvements
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Maturity targets – ambition levels must be realistic and purposeful
• ROI challenge:attaining the best in class (Level 5: 100%) requires major investment – is there a justifiable business case and priorities rationalizing the given maturity level?
• Feasibility challenge:attaining 100% maturity index within, say, 24 monthsmay be perceived as unrealistic ambition and will undermine motivation… and credibility
Follow peer benchmark indicators
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Benchmarking: how do you rate against industry benchmark?
2.62.4
3.0
3.2
2.6
2.8
3.1
2.22.1
2.8
1
2
3
4
5
Mean Best in Class Benchmark reference
The benchmark includes results from maturity assessment for the finance, customer data domain at:▪ Bacardi▪ Heineken▪ Kerry Food▪ Merck▪ Mondelez▪ Nestlé▪ Phillip Morris▪ Redbull▪ s.Oliver▪ Zespri▪ Swarovski▪ Novartis Pharma▪ AstraZeneca▪ Bayer HealthCare▪ TetraPak
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Assessment as progress tracking for MDM transformation
Maturity Assessment Profile (2017-2018) Planned vs. actual MDM maturity progress
R² = 0.7325
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 1 2 3 4 5 6 7 8 9 10 11 12
Matu
rity
score
Years since central MDM department has been established
Benchmark Companies Trend (target values)
My Company
5
4+
4
3+
3
2+
2
1
2017
2018
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Planned vs actual maturity progress 2015-2016
Audit 2016 Target 2018 Target 2020
Actual 2018
42.1%
5
4+
4
3+
3
2+
2
1
25
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Internal benchmarking exposes corporate gaps
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
People &
Resp.
Processes &
Methods
Data
Lifecycle
Data
Applications
Data
Architecture
Performance
Mgmt
North America
Asia Pacific
Europe
Global
Maturity score per Region
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Assessment approach
… …
Determine the as-is and prioritize
needs for action
The moderated self-assessment is applied to derive insights that are relevant to the predefined scope: organizational, functional and data class-specific.
The assessment applies a standardized questionnaire to evaluate the strengths, weaknesses and priorities of data management, based on interviewees feedback
Quantitative and qualitative information is used to derive capability gaps, used as a basis to formulate needs for action for your company
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Maturity evaluation and improvement process steps
Domain SteCo
„Reference Team”
Domain Manager
Interviewees
Self-assessment findings Priority analysis
Areas with high need for action
Approved actions for execution
Recommended corrective actions
lead to
are addressed by
are approved
Prioritized corrective actions
are discussed and prioritized
START
CONCLUSION
MATU
RIT
Y A
SSESSM
EN
TM
ATU
RIT
Y IM
PRO
VEM
EN
T
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Maturity assessments follow a well-defined process
7-10 Weeks
• Participants selection
• Customise questionnaire
• Communication plan
• Interviews execution
• Interviews documentation
• Takeaway confirmation
• Performance overview
• Maturity scoring
• Priority analysis
• Performance comparison
• List of recommended best practices
• List of corrective actions
• Roadmap
• Final report
Planning AssessmentQuantitative
AnalysisBench-
markingCorrective
actionsQualitative
Analysis
• Strenghts and weaknesses
• Statements
• Pain-points
• Insights / capability gaps
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Assessment scoring principles
Maturity Level
Urgency of Improvements
High urgency for improvements
Improvement activities required
Activities to maintain status-quo
Minor activities
No activities required
Fully completed
Significant progress
Regular progress
Minor progress
Not yet started
…+ unstructured input: comments, recommendations, requirements, etc.
1
2
3
4
5
1
2
3
4
5
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RIGHT participants – the key to meaningful takeaway!
• What to consider to select the RIGHT participants for maturity assessments?In general participants’ profile shall embrace three essential characteristics:
Be informed about the current business drivers and data-related requirements in their scope of business processes
Be engaged to drive improvements and prioritize actions
Be able to provide meaningful insights to identify business-relevant issues, capability gaps and define corresponding corrective actions
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Assessment scope must be clear
Sales Areas
Germany
Europe
International
Customer
Product
Finance
Data Domains Systems
CRM
PIM
SAP
OrganizationLevels/Entities
HQ functions
Country Orgs
Retail Network
Plants
Business Processes
Marketing & Sales
Aftermarket & Retail
FinanceGroup IT
R&D
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Participants are invited to identify capability gaps and share ideas
❑ Think of issues, show-stoppers, bottle-necks, missing capabilities, wishlists, etc.
❑ Stick to the defined scope: data domains, processes, organization levels,...
❑ Provide perception from perspective of their context - not „THE Reality”
Data quality issues may result from different root causes:
A. One thing is lack of definitions, rules, models, etc. (governance framework),
B. ...but quite another is lack of awareness (know-how)
C. ...let alone lack of actual use (implementation and enforcement)
If you could change three things to improve data management performance from perspective of your responsibility area...
...what would it be?
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Goals and provided insights of the maturity assessment
Assess current data management capabilities based on a standard methodology and
capture the perception and expectations
of key stakeholders
Data Governance Maturity Assessment
1 Goals
NA 1 2 3 4 5 NA 1 2 3 4 5
1.1
1.2
1.3
1.4
1.5
1.6
Key strengths
Key areas of improvement
Additional comments
Pil
ot
esta
bli
sh
ed
Basic
ap
pro
ach
esta
bli
sh
ed
Ap
pro
pri
ate
ly e
sta
bli
sh
ed
Fu
lly c
om
ple
ted
Urgency for
improvementsMaturity level
Hig
h u
rgen
cy f
or
imp
rovem
en
ts
A portfolio of data products and data governance services exists, is
documented and managed.
Internal and external business requirements, the corporate strategy
and corporate goals are captured in a structured way and build the
base for deriving and documenting requirements for data governance.
Based on the requirements for data governance the needed data
governance capabilities are derived and documented.
Strategic data governance objectives and values are documented.
Those back up the corporate strategy.
In order to establish new business models and support data-based
decisions, new channels to utilize data are continuously developed.
No
t ap
pli
cab
le
No
t ap
pli
cab
le
No
acti
vit
ies r
eq
uir
ed
Min
or
acti
vit
ies
Acti
vit
ies t
o m
ain
tain
sta
tus-q
uo
Imp
rovem
en
t acti
vit
ies r
eq
uir
ed
No
t yet
sta
rted
Strategic planning and coordination of data governance initiatives and
activities are in place. The planning considers availability of required
resources (time, staff and budget).
Identify areas of improvement and derive recommendations
for the future set-up of data governance based on team insights, industry good
practices and expert knowledge
Strategy and
communication
Data Governance and
Ownership
Optimize data lifecycle
processes
Performance and data
quality management
Holistic view on data
Improvements and
innovation
Continuous
Improvement
Deriving
improvements of
data management
Data quality reporting ResultsSensitive data
reporting
Data management
value proposition
Business oriented
data quality metrics Measuring data quality
Measuring
sensitive data
Performance
ManagementPerformance metrics
Application
documentation
Tool integration
and usability
Application landscape
change management Applications
Unambiguous core
business objective
definition
Meta data
management
Data distribution and
integration
Data
architectureExternal data
integrationData classification
Requirements for data
lifecycle processes
Data lifecycle
definitions & guidelines Data lifecycle
Lean and efficient
processes
Processes and
methodsData governance
methods
Change
management
Data governance
processes
Top executive
support
Roles and
responsibilities
Employee data
management
awareness
Recognition Data management
trainings
People, roles
and responsibilities
Data management
strategy
Strategic project and
resource planning Business capabilities
Data-driven
business models Goals
Data management
capabilities
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Goals and provided insights of the maturity assessment
Benchmark maturity assessment results against other
companies and learn from industry good practices
Share data managementknowledge and raise
awareness and improve communication within
your organization
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Closing recommendations
The more detailed interview documentation gets, the more reliable insights into capability gaps
and hence more solid qualitative analysis can be executed. Scores alone will not do the job in
planning the improvement measures.
Documentation
Group-interviews are possible, but beware not to mix participants from different hierarchy
levels. Typically this hinders the openess of evaluation feedback.Hierarchy
Be ready to challenge high scores. Clarify to the interviewee the scoring criteria and provide
capability evidence examples. Prompt to the interviewee to back-up his/her scoring claim.Challenge
The assessment is inherently subjective and may not reflect the „reality”. The risk is that
the scores will only reflect the level of awareness – be careful when identifying intervieweesSubjectivity
Questionnaire statements can get interpreted in different ways. Use capability evidence
examples to ensure homogenous understanding amongst the interviewees.Interpretation
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Right data quality at the the right timeand the right place
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Questions? Afterthoughts?I am looking forward to hearing from you!
Senior ConsultantHead of CDQ Academy
Dr. Zib Korendo
+ 48 606 620 550
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CDQ – Sharing Data Excellence since 2006Data Management knowledge from practitioners for practitioners
Sharing
Excellence
Sharing
Data
Data Quality
CDQ Data Sharing Community
CDQ Academy
CDQ Consulting
Competence Center Corporate Data Quality
Sharing
Knowledge
Sharing
Expertise
The root of the company since 2006: 20 companies are research partners in the Competence Center Corporate Data Quality (CC CDQ), exploring the frontiers of data quality management
As part of our Data Sharing community, companies receive 100% transparency on data quality and automated improvement of their customer and vendor data – “Data Quality as a Service”
Practitioners can learn about our best-practice models and approaches in data quality management and data gover-nance in the CDQ Academy
Companies are working directly with our data management experts, who support with strategic design or operational implementation of data management
CDQ is headquartered in St. Gallen, Switzerland and shares data excellence with customers in four ways
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The Data Excellence Model is licensed as a Creative Commons Attribute
• Please note that the Data Excellence Model as developed by the Competence CenterCorporate Data Quality (CC CDQ) is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This CC-BY-SA license states that you can use, copyand redistribute the Data Excellence Model, provided that you give appropriate credit (i.e.indicating the Competence Center Corporate Data Quality /CC CDQ as the author of themodel), provide a link to the license on your website, and indicate all changes you madeto the Model.
• You may adapt the Data Excellence Model to the requirements of your company by, forexample, renaming the design areas or changing the colors. If you do so, you mustpublish your version of the model under the same license the original Model is distributedunder.
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