8

Table 3: Characteristics of a Proactive Organization

People

" Management understands and appreciates

the role of data governance and commits

personnel and resources

" Executive-level decision-makers begin to

view data as a strategic asset

" Data stewards emerge as the primary

implementers of data management strategy

and work directly with cross-functional

teams to enact data quality standards

Policies

" Real-time activities and preventive data

quality rules and processes emerge

" Data governance processes are built into

the foundation of CDI, PDM and other

solutions

" Data metrics are sometimes measured

against industry standards to provide

insight into areas needing improvement

" Goals shift from problem correction to

prevention

Technology

" A data stewardship group maintains

corporate data definitions and business

rules

" Service-oriented architecture becomes the

enterprise standard

" Ongoing data monitoring helps the company

maintain data integrity

" More real-time processing is available and

data quality functionality is shared across

different operation modes

Risk and Reward

" Risks: Medium to low. Risks are reduced by

providing better information to increase

the reliability of sound decision-making

" Reward: Medium to high. Data quality

improves, often in certain functional areas

and then in broader realms as more

employees join the early adopters

Advancing to the next stage

At the Proactive stage, organizations begin to unify the corporate view of a specific domain

(typically customers or products). The next phase creates a unified approach for all corporate

information, ultimately leading to the quality of information that can support the automation

of business processes.

To progress to the final stage Governed a company needs to assemble and integrate many

of the pieces already in place. A Center of Excellence (or similar framework) emerges to

organize the work of multiple data stewards within the enterprise. Business analysts start to

control the data management process, with IT playing a supporting role. And the master data

efforts provided by CDI and PDM initiatives provide the foundation for business process

automation, as the data is now robust and reliable enough to support high-end process

management.

The technology required to reach the final stage also centers on the ability to automate

business processes. The core components of MDM are in place, and organizations typically

need to concentrate on making master data a core component, regardless of the originating

application or data type. Through a high degree of data quality, the foundation for supporting

full BPM integration is now feasible.

The benefits

from the

Proactive stage

establish the

foundation for

MDM efforts

and business

process

automation.