Navigating the Organization to Ensure Data Governance - Data Governance: Creating Value from Information Assets - page 22

he DataFlux data governance maturity model takes an enterprise per-
spective, which moves through four phases of maturity: undisciplined
(think locally, act locally), reactive (think globally, act locally), proactive
(think globally, act collectively), and, finally, governed (think globally, act
globally). The DataFlux maturity model describes each phase along the
dimensions of people, policies, technology, and risk. As the organization
moves along the phases of data governance maturity, the value derives
from the information and knowledge assets increasing and the risks asso-
ciated with bad data decreasing.
EWSolutions presents five phases of data governance maturity, pro-
gressing from informal processes (reactive), emerging processes (initial
approaches to data stewardship and governance), engineered processes
(with standard processes), controlled processes (with established measur-
able process goals) to optimized processes (with qualitative and qualitative
understanding used for continuous process improvement).
G a r t ner 's EIM (enterprise information management) maturity model
views managing information as a strategic asset. The maturity model
moves through five levels and action items are provided for each level
of maturity. The levels of maturity include: Unaware (where strategic
decisions are made without adequate information), Aware (where there
is development in understanding of the value of information), Reactive
(where business understands the value of information), Proactive (where
information is viewed as necessary for improving performance), Managed
(where the enterprise understands information is critical) to, finally,
Effective (where information value is harvested throughout the informa-
tion supply chain). Gartner's EIM discipline has five major goals: (1) data
integration across the IT portfolio, (2) unified content, (3) integrated
master data domains, (4) seamless information flows, and (5) metadata
management and semantic reconciliation.
IBM presents a five-level path in its Data Governance Council's matu-
rity model that offers a steady, measurable progression to the final state of
fully mature processes. At maturity level 1 (initial), processes are unpre-
dictable, poorly controlled, and reactive. At maturity level 2 (managed),
processes are characterized for projects and are manageable. At maturity
level 3 (defined), processes are characterized for the organization and are
proactive. At maturity level 4 (quantitatively managed), processes are
quantitatively measured and controlled. At maturity level 5 (optimizing),
focus is on continuous process improvement. The Data Governance