data governance [English]
n. ~ A senior-level administrative structure, part of information governance, that establishes roles, responsibilities, decision-making processes, and policies and procedures, and that treats data as an enterprise resource, ensures its quality and appropriate use, and compliance with relevant laws and regulations, to support the organization's information needs.
Data governance ensures that an organization has the information of sufficient quality and integrity it needs to make decisions. The main goals and objectives of data governance include · Defining, approving, and communicating data strategies, policies, standards, architecture, procedures, and metrics. · Tracking and enforcing conformance to data policies, standards, architecture, and procedures. · Sponsoring, tracking, and overseeing the delivery of data management projects and services. · Managing and resolving data related issues. · Understanding and promoting the value of data assets (paraphrased, Oracle, 2011).
- DGPO 2014 (†541 ): A discipline that provides clear-cut policies; procedures; standards; roles; responsibilities; and accountabilities to ensure that data is well-managed as an enterprise resource.
- Ballard 2014 (†528 p. 26): The core disciplines of data governance cover data quality management, Information Lifecycle Management, and information security and privacy. (†843)
- Butler 2014 (†548 ): What is the difference between information governance and data governance? “Data governance is keeping garbage from getting in,” [Susan] White said. “Information governance is the decisions we make in using that data… For me, data governance keeps garbage from coming in, information governance is keeping garbage from coming out.” (†904)
- Dennedy, et al. 2014 (†568 p.53): Data governance is a strategic, “top-down” program for data management in which an organization’s leadership communicates the core value of data quality and integrity to stakeholders. It includes the development and enforcement of standards and procedures. It requires broad understanding of data entrusted to the organization, the value and use of data, upstream and downstream stakeholders, systems, and processes for all decisions and issue resolution. To be effective, data governance requires data stewardship and data stewards. It also requires executive sponsors and support. (†954)
- DGI Definition 2014 (†543 ): When you refer to governance, be careful! Depending on the context, “Data Governance” could refer to: · organizational bodies · rules (policies, standards, guidelines, business rules) · decision rights (how we “decide how to decide”) · accountabilities · enforcement methods for people and information systems as they perform information-related processes (†892)
- DGI Glossary 2014 (†544 ): Data Governance programs often support many types of compliance requirements: Regulatory compliance, contractual compliance, adherence to internal standards, policies, and architectures, and conformance to rules for data management, project management, and other disciplines. (†896)
- DGI Governance 2014 (†545 ): The terms data governance and data stewardship are sometimes used interchangeably, but there is actually a difference. Data governance brings together cross-functional teams to make interdependent rules or to resolve issues or to provide services to data stakeholders. These cross-functional teams – data stewards and/or data governors – generally come from the business side of operations. They set policy that IT and data groups will follow as they establish their architectures, implement their own best practices, and address requirements. Data governance can be considered the overall process of making this work. ¶ Data stewardship is concerned with taking care of data assets that do not belong to the stewards themselves. Data stewards represent the concerns of others. Some may represent the needs of the entire organization. Others may be tasked with representing a smaller constituency: a business unit, department, or even a set of data themselves. (†899)
- IBM Data Governance 2007 (†529 p. 3): Data governance is a quality control discipline for adding new rigor and discipline to the process of managing, using, improving and protecting organizational information. Effective data governance can enhance the quality, availability and integrity of a company’s data by fostering cross-organizational collaboration and structured policy-making. (†844)
- Ladley 2012 (†589 p.11): "Data governance is the organization and implementation of policies, procedures, structure, roles, and responsibilities which outline and enforce rules of engagement, decision rights, and accountabilities for the effective management of information assets." Regardless of style of definition, the bottom line is that data governance is the use of authority combined with policy to ensure the proper management of information assets. (†1198)
- Ladley 2012 (†589 p.11): Data governance is NOT a function performed by those who manage information. This means there must always be a separation of duties between those who manage and those who govern . . . . This is a key concept that business people understand, and IT staff often experience as a problem. For example, in business there are auditors and managers. Managers control, monitor, and ensure work gets done and rules and standards are adhered to. Auditors verify compliance to standards, and define and implement new controls and standards as required. This is exactly the same protocol that is required by data governance. The [data governance] “area” identifies required controls, policies, and processes, and develops rules. Information managers (essentially everyone else) adhere to the rules. (†1199)
- Ladley 2012 (†589 p.174): Data governance is a business program - [data governance] is never an IT program. It exists to provide the roles, rules, and controls for the data assets. It must be applied across the board to everyone in the organization. (†1200)
- Nadhan 2014 (†549 ): Data Governance is the mechanism by which we ensure that: the right corporate data is available to the right people, at the right time, in the right format, with the right context, through the right channels. Information governance is about ensuring the same for the knowledge gathered from this data. . . . ¶ Data Governance is about controlling and understanding the fundamental elements of data, so it can be processed effectively to generate information. Once multiple data elements have been coalesced to realize the Big Picture, different rules are likely to apply. Hence, Information Governance. (†905)
- Oracle Enterprise Information Management 2011 (†542 p. 3): Data governance is not meant to solve all business or IT problems in an organization. The main goals and objectives of data governance include the following. · To define, approve, and communicate data strategies, policies, standards, architecture, procedures, and metrics. · To track and enforce conformance to data policies, standards, architecture, and procedures. · To sponsor, track, and oversee the delivery of data management projects and services. · To manage and resolve data related issues. · To understand and promote the value of data assets. (†890)
- Oracle Enterprise Information Management 2011 (†542 p. 5): Some of the key deliverables for data governance include: ¶ Data policies are a collection of statements that describes the rules controlling the integrity, security, quality, and use of data during its lifecycle and state change. ¶ Data standards are more detailed rules on how to do it. Sample data standards include naming standards, data modeling standards, and other data architecture standards. ¶ Resolved issues . . . procedures of addressing data related issues including data quality issues, data naming and business rules conflicts, data security issues, and service level problems. ¶ data management projects and service development effort across the organization. As a result, it drives better data management projects to have higher success rate, deliver more value, and reduce time to deliver and cost to implement. ¶ Quality data and information . . . with improved quality, easier access, and managed and auditable security. Quality data and information, as a result, is the core deliverable of the data governance function. $para;Recognized data value . . . data as an asset. A key output of data governance is to valuate core enterprise data assets – what business processes they support, how critical are these processes, how critical are these data elements in support of these processes, what are the ramifications and risks to the organization if they are unavailable or incorrect. (†893)
- Plotkin 2013 (†588 Chapter One): "Data Governance is the exercise of decision making and authority for data-related matters. It’s 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." The key thing to take away from this definition is that the practice of Data Governance has more to do with establishing the roles and responsibilities about how people manage and make decisions about data than about the data itself. That is, Data Governance–and Data Stewardship–is all about making sure that people are properly organized and do the right things to make their data understood, trusted, of high quality, and, ultimately, suitable and usable for the enterprise’s purposes. (†1192)
- Reidenberg, et al. 2013 (†364 ): [School] districts must establish policies and implementation plans for the adoption of cloud services by teachers and staff including in-service training and easy mechanisms for teachers to adopt, and propose technologies for instructional use. Districts must address directly and publicly any policies on the use of student data for advertiser supported services. Districts should create data governance advisory councils for advice and industry should develop mechanisms to help districts vet privacy-safe services and technologies. Finally, larger districts and state departments of education must designate a Chief Privacy Officer to provide advice and assistance. (†362)
- Schmidt 2014 (†550 ): The kinds of policies that Data Governance cares about include: · Data transparency. What data do we have in the enterprise, where is it, and how is it secured. · Data lineage. What is the system of record for various types of data, how does it move between systems, and what transformations were applied in the process. · Data Quality. What rules can be applied systematically in the capture, monitoring, and measurement of data assets. · Service Levels. What are the required service levels for the timeliness of data delivery or synchronization between copies of the data · Data Security. How can data be kept secure regardless whether it is controlled by an application system, copied to a test or training database, or stored in the cloud. · Change Impact. What is the impact to existing and historical data and data processes if a given system change is implemented. · Data Ownership. Who is accountable for maintaining and operating the data stores, whether they are stand-alone copies or linked to a production application. (†906)
- Smallwood 2014 (†566 Chapter Two): Data governance involves processes and controls to ensure that information at the data level - raw alphanumeric characters that the organization is gathering and inputting - is true and accurate, and unique (not redundant). It involves data cleansing (or data scrubbing) to strip out corrupted, inaccurate, or extraneous data and de-duplication, to eliminate redundant occurrences of data. Data governance focuses on information quality from the ground up at the lowest or root level, so that subsequent reports, analyses, and conclusions are based on clean, reliable, trusted data (or records) in database tables. (†949)
- Stiglich 2014 (†546 ): When you hear some talk about Data Governance, it is hard to decipher whether they’re really talking about Data Governance or if they’re really talking about Data Management or some ambiguous conglomeration of the two.
The DAMA Dictionary of Data Management defines Data Governance as “The exercise of authority, control and shared decision making (planning, monitoring and enforcement) over the management of data assets.” DAMA has identified 10 major functions of Data Management in the DAMA-DMBOK (Data Management Body of Knowledge). Data Governance is identified as the core component of Data Management, tying together the other 9 disciplines, such as Data Architecture Management, Data Quality Management, Reference & Master Data Management, etc., as shown in Figure 1.
- Sucha 2014 (†567 p.26): Data governance is the organization and implementation of accountabilities for managing data (McGilvray 2008). Data governance includes the roles for managing data as well as the plans, policies, and procedures that control - in essence govern - data. (†952)
- Topi and Tucker 2014 (†551 p. 21-3): [Data governance] is part of an overall corporate governance strategy as an equal subdiscipline along side [information technology] governance. . . . ¶ [Data governance] should be clearly distinguished from data management and data quality management . . . . [Data governance] is the decision rights and policy making for corporate data, while data management is the tactical execution of those policies. (†909)
- Wikipedia (†387 s.v. "data governance"): 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. ¶·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. 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. (†835)
- Wikipedia (†387 s.v. "data governance"): 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 gain (†836)