This Slideshare presentation is a partial preview of the full business document. To view and download the full document, please go here https://flevy.com/browse/business-document/information-quality-maturity-matrix-3629 DOCUMENT DESCRIPTION With the ever increasing growth of information within and around business activities, the importance in the quality of data, as managed by enterprise systems, is heightened. Enterprises need to review the current situation for controlling the quality of data. Core to any study is the identification of the maturity that has been achieved within and across the eco-system. This maturity matrix helps identify the various facets that need to be considered and evaluated.

1. Enterprise Information Quality
Maturity Matrix
R. Bradley Consulting
November 2018

2. 3
Enterprise Information Quality Maturity Matrix
Usage Advice
With the stakeholders ask them to position into matrix where they believe the
maturity of the organisation is for the various elements both now (indicated by the
Red lines) and future given the need if an ideal solution were available. This
provides a clear guidance on the maturity of services the strategic solution needs to
provide and the gaps in the current. You can tune the elements and categories
based on the individual use if needed.
Maturity INITIAL REACTIVE MEASURED MANAGED OPTIMIZING
Governance No governance structure or
defined ownership models
Some data quality standards
used at a system level, but
applied to the outputs
Data governance
structure and tools in
place, but still mainly
reactive to problems
application by application
Data governance
structure in place and
proactively monitoring
data quality across
applications.
Data Governance is cross-
organizational and data
quality is prioritized by
business need.
Architecture No business process to
identify common data
models and integrate data
between systems
Initial attempts at
developing a single master
data repository
Data quality tools applied
within the Information
Architecture but mainly
to outputs.
Quality tools applied to
the inputs to the Data
Architecture.
Seamless integration of
systems using master
data sets.
Management Some application
configuration, but not
centrally co-ordinated.
Resources assigned to
manage data quality in main
applications using
introduced tool sets.
Data quality managed
centrally by comparison
with specifications and
initial data models.
Master Data models
established.
Master data sets are
managed within the
information architecture
itself
Identification Replication of reference data
in individual systems.
Centralized
indexing/referencing
developed
Centralized
administration of Data
Specification and
indexing.
Master data made
available and managed
within the information
architecture.
Migration of legacy
systems complete and
app/dev utilises master
data models.
Integration “Stovepipe” systems aligned
to business function
Exploration of system
rationalization and
development off cross-
functional systems
Feedback from single
master repository into
contributing systems.
Administration of Data
Specification takes place
in the background within
the information arch.
Systems incorporate data
quality standards and
business rules.
Business Process
Management
No synchronization of data in
different systems
Conceptual Business
Processes identified for the
capture, use and analysis of
data
Defined business
processes for the analysis
and use of data.
Defined business
processes for data are
incorporated into the
information architecture.
Business processes drive
application development
Assurance Lots of data but generally
information at a business
level is conflicting, confusing
and Business data is seen as
incomplete, inaccurate and
unreliable.
Lots of data, but “islands of
coherence” exist. Individual
audit trails exist for
production of key individual
items
Consistent approach to
referencing and recording
all information products
introduced. Data Quality
is seen as acceptable.
All information
productions are recorded
centrally in a defined
information library (DIL).
The inter-dependencies
are clearly understood,
Data Quality is seen as
Good.
Data Quality is business as
usual. Business level
information is consistent
and aligned with
operational information
(“one version of the
truth”)
Maturity INITIAL REACTIVE MEASURED MANAGED OPTIMIZING
Governance No governance structure or
defined ownership models
Some data quality standards
used at a system level, but
applied to the outputs
Data governance
structure and tools in
place, but still mainly
reactive to problems
application by application
Data governance
structure in place and
proactively monitoring
data quality across
applications.
Data Governance is cross-
organizational and data
quality is prioritized by
business need.
Architecture No business process to
identify common data
models and integrate data
between systems
Initial attempts at
developing a single master
data repository
Data quality tools applied
within the Information
Architecture but mainly
to outputs.
Quality tools applied to
the inputs to the Data
Architecture.
Seamless integration of
systems using master
data sets.
Management Some application
configuration, but not
centrally co-ordinated.
Resources assigned to
manage data quality in main
applications using
introduced tool sets.
Data quality managed
centrally by comparison
with specifications and
initial data models.
Master Data models
established.
Master data sets are
managed within the
information architecture
itself
Identification Replication of reference data
in individual systems.
Centralized
indexing/referencing
developed
Centralized
administration of Data
Specification and
indexing.
Master data made
available and managed
within the information
architecture.
Migration of legacy
systems complete and
app/dev utilises master
data models.
Integration “Stovepipe” systems aligned
to business function
Exploration of system
rationalization and
development off cross-
functional systems
Feedback from single
master repository into
contributing systems.
Administration of Data
Specification takes place
in the background within
the information arch.
Systems incorporate data
quality standards and
business rules.
Business Process
Management
No synchronization of data in
different systems
Conceptual Business
Processes identified for the
capture, use and analysis of
data
Defined business
processes for the analysis
and use of data.
Defined business
processes for data are
incorporated into the
information architecture.
Business processes drive
application development
Assurance Lots of data but generally
information at a business
level is conflicting, confusing
and Business data is seen as
incomplete, inaccurate and
unreliable.
Lots of data, but “islands of
coherence” exist. Individual
audit trails exist for
production of key individual
items
Consistent approach to
referencing and recording
all information products
introduced. Data Quality
is seen as acceptable.
All information
productions are recorded
centrally in a defined
information library (DIL).
The inter-dependencies
are clearly understood,
Data Quality is seen as
Good.
Data Quality is business as
usual. Business level
information is consistent
and aligned with
operational information
(“one version of the
truth”)
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http://flevy.com/browse/document/information-quality-maturity-matrix-3629

3. 1
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