At the heart of every successful master data management (MDM)
strategy is master data that is complete and accurate at all times. But, the
optimum quality and consistency of master data can only be secured if
comprehensive data governance plays an integral role in its creation,
collection, storage, handling, and administration.
The Data Governance Institute, a provider of in-depth,
vendor-neutral information about best practices in the management and stewardship
of enterprise information, has defined data governance as "a system of
decision rights and accountabilities for information-related processes,
executed according to agreed-upon models which describe who can take what
actions with what information, and when, under what circumstances, using what
methods.”
And, the experts all agree that MDM initiatives that lack formal
data governance policies have a higher likelihood of failure. Why? Because data governance not only helps
to ensure the integrity of the master data that stakeholders use to formulate
important business plans and make critical day-to-day business decisions, it
aids in effective compliance with regulatory and information disclosure
demands.
However, Gartner predicts that 90 percent of organizations will
not succeed at their first attempts at data governance. This failure can be caused by a variety
of common factors, including:
- Too much reliance on IT. According to Ventana Research’s
Mark Smith, responsibility for data quality is not just IT’s job. It is up to information consumers
within functional business units – who have insight into the context in
which master data is used – to help administer these assets.
- No clear documentation. Data governance policies and
related procedures must be defined and documented in a way that both
technical and business stakeholders can easily understand, and must be
readily accessible to all those who generate or interact with master
data.
- Poor enforcement. Data governance
processes that are loosely enforced – or not enforced at all – are not
likely to be adhered to.
Documentation must not only account for what the rules and
guidelines are, but what the possible penalties will be if they are not
properly followed.
In some scenarios, bad or invalid master data may be worse than no
master data at all. In order to
preserve the correctness and consistency of master data across an organization,
companies must implement a formalized data governance program that includes
strict “checks and balances” that are overseen by a council of key stakeholders
from both the IT team, and various business units. Only then can master data be optimized to ensure accuracy,
comprehensiveness, and most importantly, relevance to all those who rely on it
to support core business activities.
To learn more about best practices in data governance and master
data management, visit the Croyten Web site at www.croyten.com.