Data Governance: Its Significance in MDM

data governance

Data Governance: Its Significance in MDM

Data Governance: Your MDM Plan Won’t Work Without It

At the heart of every successful master data management (MDM) strategy is master data. This strategy 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 is a provider of in-depth, vendor-neutral information about best practices in the management and stewardship of enterprise information. It has defined data governance as “a system of decision rights and accountabilities for information-related processes. These processes are executed according to agreed-upon models. These models 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. But it also aids in effective compliance with regulatory and information disclosure demands.

Why Companies Fail at Data Governance

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. These include:

  • 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 governing policies and related procedures must be defined and documented in a way that both technical and business stakeholders can easily understand. Moreover, these 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.