The 4 Reasons Data Governance Fails

SOLUTION - Information Management


Kelly Guillory, Principal - Noah Consulting

The DMBOK from DAMA defines data governance as, “the exercise of authority, control, and shared decision making over management of data assets.” (i) The business community recognizes that data has become a corporate asset, and without good quality data there would be no way of managing and measuring the state of the business. Data governance is the foundation for sustainable data quality and ensuring that the right processes, people, and technology are in place. It is a key part of most data management projects, such as master data management and data virtualization projects, to name a few. Sustainable data quality provides huge business value given that: 

  1. Quality data is accepted and trusted, because there are consistent definitions and use which shortens the decision making cycle. 
  2. Quality data comes from consistent systems of record that have been authorized as the single truth. 

Sustainable data quality also drives IT enablement of business value by identifying DQ issues (hey, we have a problem), enabling DQ via technology (here is how we are going to fix the issue), and sustaining DQ by monitoring current state (how are we doing at keeping it clean). 

Since data governance is a new activity or methodology for most organizations, there is a need to identify the criteria for data governance. Once the criteria have been agreed upon, the business must take each data domain and score them against those dimensions. Scoring is a subjective exercise used to identify strengths and weaknesses in an organization’s data governance methodology. When data governance is not properly controlled, it may succumb to its own set of pitfalls. 

In order to define success, we must also define failure. In what ways can data governance break down and fail the company, rendering it counterproductive? By answering this question, Noah has developed four standard dimensions for scoring. These dimensions give the business the confidence that the data is being governed. These dimensions include Standardization, Data Quality, Process, and Roles & Responsibilities. But, when these dimensions are lacking, data governance fails. 

No Standardization 

Standardization is derived from the word “standard,” with the meaning of “something considered by an authority or by general consent as a basis of comparison; an approved model.” (ii) The following measures are used to assess the level of standardization: 

  1. Systems of Records (SoRs) are identified in place for each data domain. The business must identify what SOR means to them. 
  2. SoR standards are established and documented. 
  3. Data models are architected and documented for the SoRs. 
  4. Data security has been identified and managed. 

Poor Data Quality 

Data quality is a key component to the sustainment of data governance. The following measures must be addressed around data quality to assess the state of the organizations data quality plan: 

  1. The business processes is ready for auditing. 
  2. Data rules are tied to the business processes. 
  3. Data is being actively monitored against the standards set forth in the data rules. 
  4. The quality metrics are being communicated to the interested parties. 

Lack of Processes 

The process dimension deals with the level to which processes have been developed to oversee authoring and maintenance of the data. The following measures must be taken to determine the extent to which processes are being followed: 

  1. Master Data is being managed. 
  2. Standard business processes is identified for each data domain. 
  3. Business rules and data rules are being applied to data. 

No Defined Roles & Responsibilities 

Having the other three dimensions are very important in providing a comfort level to the business that their data is being governed; however, not having identified roles and responsibilities will inhibit the other three dimensions from actually happening. Ensuring the following measures is key to having the necessary people in place to perform the other three dimensions:  

  1. Data producers and consumers are identified. 
  2. Data stewards are identified, trained, and their role communicated across the organization. 
  3. Data analysts and data custodians are engaged. 

In establishing better overall data management process, procedures, and policies to adhere to business and data quality rules, data governance provides the business the following value: 

Quality: Improve the trust, consistency, relevance, and timeliness of data that is used on a daily basis to support business decisions. 
Process: Establish business rules and data rules to improve data flow and manage the business and data lifecycle. 
Standardization: Identify the single sources of truth, SoRs, to support each data domain 

Data governance is vital to the overall health of any data management program; its business value is a key factor for success. Without all four dimensions, business value will be low and the project may be deemed a failure. However, when properly implemented, data governance provides the opportunity to enhance business value and provides the business the ability to operate with increased productivity and organizational efficiency.

i data governance. Mosley, Mark. The DAMA Dictionary of Data Management, 1st Edition. Bradley Beach, N.J.: Technics Publications. 2008.

ii standard. Unabridged. Random House, Inc. (accessed: June 14, 2013).


Kelly Guillory is a Principal Technical Architect in the upstream oil & gas Information management space with over 16 years of IT experience spanning multiple industries from trading, upstream oil & gas, financial services, healthcare, and public sector. She has extensive knowledge in architecting and building data integration, data quality, and data warehousing solutions. Over the past four years, she has built several master data management solutions around the well domain using the foundation of PPDM. Prior to joining Noah Consulting, Kelly was working as an independent consultant as a Lead ETL architect for several clients. Kelly has a bachelor’s degree in accounting and accounting information systems from Virginia Polytechnic Institute & State University.

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