Data governance

Data governance is an emerging discipline with an evolving definition. The discipline embodies a convergence of data quality, data management, data policies, business process management, and risk management surrounding the handling of data in an organization. Through data governance, organizations are looking to exercise positive control over the processes and methods used by their data stewards and data custodians to handle data.

Data governance is a set of processes that ensures that important data assets are formally managed throughout the enterprise. Data governance ensures that data can be trusted and that people can be made accountable for any adverse event that happens because of low data quality. It is about putting people in charge of fixing and preventing issues with data so that the enterprise can become more efficient. Data governance also describes an evolutionary process for a company, altering the company’s way of thinking and setting up the processes to handle information so that it may be utilized by the entire organization. It’s about using technology when necessary in many forms to help aid the process. When companies desire, or are required, to gain control of their data, they empower their people, set up processes and get help from technology to do it.^[1]

There are some commonly cited vendor definitions for data governance. Data governance is a quality control discipline for assessing, managing, using, improving, monitoring, maintaining, and protecting organizational information.^[2] It is 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.^[3]:

Overview

Data governance encompasses the people, processes, and information technology required to create a consistent and proper handling of an organization's data across the business enterprise. Goals may be defined at all levels of the enterprise and doing so may aid in acceptance of processes by those who will use them. Some goals include:

• Increasing consistency and confidence in decision making
• Decreasing the risk of regulatory fines
• Improving data security
• Maximizing the income generation potential of data
• Designating accountability for information quality
• Enable better planning by supervisory staff
• Minimizing or eliminating re-work
• Optimize staff effectiveness
• Establish process performance baselines to enable improvement efforts
• Acknowledge and hold all gains

These goals are realized by the implementation of Data governance programs, or initiatives using Change Management techniques.

Data governance drivers

While data governance initiatives can be driven by a desire to improve data quality, they are more often driven by C-Level leaders responding to external regulations. Examples of these regulations include Sarbanes-Oxley, Basel I, Basel II, HIPAA, and a number data privacy regulations. To achieve compliance with these regulations, business processes and controls require formal management processes to govern the data subject to these regulations. Successful programs identify drivers meaningful to both supervisory and executive leadership.

Common themes among the external regulations center on the need to manage risk. The risks can be financial misstatement, inadvertent release of sensitive data, or poor data quality for key decisions. Methods to manage these risks vary from industry to industry. Examples of commonly referenced best practices and guidelines include COBIT, ISO/IEC 38500, and others. The proliferation of regulations and standards creates challenges for data governance professionals, particularly when multiple regulations overlap the data being managed. Organizations often launch data governance initiatives to address these challenges.

Data governance initiatives

Data governance initiatives improve data quality by assigning a team responsible for data's accuracy, accessibility, consistency, and completeness, among other metrics. This team usually consists of executive leadership, project management, line-of-business managers, and data stewards. The team usually employs some form of methodology for tracking and improving enterprise data, such as Six Sigma, and tools for data mapping, profiling, cleansing, and monitoring data.

Data governance initiatives may be aimed at achieving a number of objectives including offering better visibility to internal and external customers (such as supply chain management), compliance with regulatory law, improving operations after rapid company growth or corporate mergers, or to aid the efficiency of enterprise knowledge workers by reducing confusion and error and increasing their scope of knowledge. Many data governance initiatives are also inspired by past attempts to fix information quality at the departmental level, leading to incongruent and redundant data quality processes. Most large companies have many applications and databases that can't easily share information. Therefore, knowledge workers within large organizations often don't have access to the information they need to best do their jobs. When they do have access to the data, the data quality may be poor. By setting up a data governance practice or Corporate Data Authority, these problems can be mitigated.

The structure of a data governance initiative will vary not only with the size of the organization, but with the desired objectives or the 'focus areas' ^[4] of the effort.

Implementation

Implementation of a Data Governance initiative may vary in scope as well as origin. Sometimes, an executive mandate will arise to initiate an enterprise wide effort, sometimes the mandate will be to create a pilot project or projects, limited in scope and objectives, aimed at either resolving existing issues or demonstrating value. Sometimes an initiative will originate lower down in the organization’s hierarchy, and will be deployed in a limited scope to demonstrate value to potential sponsors higher up in the organization.

Data governance tools

Leaders of successful data governance programs declared in December 2006 at the Data Governance Conference in Orlando, Fl, that data governance is between 80 and 95 percent communication.”^[5] That stated, it is a given that many of the objectives of a Data Governance program must be accomplished with appropriate tools. Many vendors are now positioning their products as Data Governance tools; due to the different focus areas of various data governance initiatives, any given tool may or may not be appropriate, in addition, many tools that are not marketed as governance tools address governance needs.^[6]

Data governance organizations

The IBM Data Governance Council ^[7]

The IBM Data Governance Council is an organization formed by IBM consisting of companies, institutions and technology solution providers with the stated objective to build consistency and quality control in governance, which will help companies better protect critical data."

The Data Governance and Stewardship Community of Practice (DGS-COP)^[8]

The Data Governance and Stewardship Community of Practice is a vendor-neutral organization open to practitioners, stakeholders and academics, as well as vendors and consultants. The DGS-COP offers a large collection of data governance artifacts to members including case studies, metrics, dashboards, and maturity models as well as on-line events.

Data Governance Conferences

Two major conferences are held annually, the Data Governance Conference, held in the USA^[9], and the Data Governance Conference Europe^[10], held in London, England.

Master Data Management & Data Governance Conferences^[11]

Six major conferences are held annually, London, San Francisco, Sydney and Toronto in the spring, and Madrid, Frankfurt, and New York City in the fall. 2009 was the 4th annual iteration with more than 2,000 attendees per year receiving their data governance and master data management updates via this 2-3 day event.

Data Governance Professionals Organization (DGPO)^[12]

The Data Governance Professionals Organization (DGPO) is a non-profit, vendor neutral, association of business, IT and data professionals dedicated to advancing the discipline of data governance. The objective of the DGPO is to provide a forum that fosters discussion and networking for members and to encourage, develop and advance the skills of members working in the data governance discipline.

See also

• Information technology governance
• Semantics of Business Vocabulary and Business Rules
• Master data management
• COBIT
• ISO/IEC 38500
• ISO/TC 215
• Operational risk management
• Basel II Accord
• HIPAA
• Sarbanes-Oxley Act
• Information technology controls
• Data Protection Directive (EU)

References

1. ^ Sarsfield, Steve (2009). "The Data Governance Imperative", IT Governance.
2. ^ "IBM Data Governance webpage". http://www-306.ibm.com/software/tivoli/governance/servicemanagement/data-governance.html. Retrieved 2008-07-09. 
3. ^ "Data Governance Institute Data Governance Framework". http://datagovernance.com/dgi_framework.pdf. 
4. ^ "Data Governance Focus Areas". http://datagovernance.com/fc_focus_areas_for_data_governance.html. 
5. ^ Hopwood, Peter (2008-06). "Data Governance: One Size Does Not Fit All". DM Review Magazine. Archived from the original on 2008-10-02. http://www.webcitation.org/5bGHaz1gA. Retrieved 2008-10-02. "At the inaugural Data Governance Conference in Orlando, Florida, in December 2006, leaders of successful data governance programs declared that in their experience, data governance is between 80 and 95 percent communication. Clearly, data governance is not a typical IT project." 
6. ^ "DataGovernanceSoftware.com". The Data Governance Institute. Archived from the original on 2008-10-02. http://www.webcitation.org/5bGI3dfHV. Retrieved 2008-10-02. 
7. ^ IBM Data Governance
8. ^ The Data Governance & Stewardship Community of Practice
9. ^ Data Governance Conference
10. ^ Data Governance Conference Europe
11. ^ MDM SUMMIT Conference
12. ^ Data Governance Professionals Organization