Data governance

Short description: Capability that enables an organization to ensure high data quality

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Data governance is a term used on both a macro and a micro level. The former is a political concept and forms part of international relations and Internet governance; the latter is a data management concept and forms part of corporate/organizational data governance.

Data governance involves delegating authority over data and exercising that authority through decision-making processes.[1] It plays a crucial role in enhancing the value of data assets.[2]

## Contents

* 1 Macro level
* 2 Data governance (Data Management)
* 3 Data governance drivers
* 4 Data governance initiatives (Dimensions)
* 5 Implementation
* 6 Data governance tools
* 7 See also
* 8 References
* 9 Further reading
* 10 External links

## Macro level

Data governance at the macro level involves regulating cross-border data flows among countries, which is more precisely termed international data governance. This field formed in the early 2000s[3] and consists of "norms, principles and rules governing various types of data."[4]

There have been several international groups established by research organizations that aim to grant access to their data. These groups that enable an exchange of data are, as a result, exposed to domestic and international legal interpretations that ultimately decide how data is used. However, as of 2023, there are no international laws or agreements specifically focused on data protection.[5]

## Data governance (Data Management)

Data governance is the set of principles, policies, and processes that guide the effective and responsible use of data within an organization. It creates a framework for decision making, accountability, and oversight across the data lifecycle, from creation and storage to sharing and disposal.

Data governance works closely with data management, which provides the practical methods to carry out governance objectives. These methods include data quality assurance, metadata management, master data management, security controls, and compliance monitoring.

Together, governance and management aim to maximize the value of data as a strategic asset, reduce risks from misuse or inaccuracy, and ensure compliance with regulatory, ethical, and business requirements. The importance of this discipline has grown with the rise of big data, cloud computing, and artificial intelligence, where consistent standards and stewardship are essential for privacy protection, interoperability, and informed decision making.[6]

## Data governance drivers

While data governance initiatives can be driven by a desire to improve data quality, they are often driven by C-level leaders responding to external regulations. In a recent report conducted by the CIO WaterCooler community, 54% stated the key driver was efficiencies in processes; 39% - regulatory requirements; and only 7% customer service.[7] Examples of these regulations include Sarbanes–Oxley Act, Basel I, Basel II, HIPAA, GDPR, cGMP,[8] and a number of 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.[9] 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 (Dimensions)

Data governance initiatives improve the quality of data by assigning a team responsible for data's accuracy, completeness, consistency, timeliness, validity, and uniqueness.[10] 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 not easily share information. Therefore, knowledge workers within large organizations often do not have access to the data 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 (individual or area responsible for determining how to proceed, in the best interest of the business, when a data issue arises), these problems can be mitigated.

## 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. The initial scope of an implementation can vary greatly as well, from review of a one-off IT system to a cross-organization initiative.

## Data governance tools

Leaders of successful data governance programs declared at the Data Governance Conference in Orlando, FL, in December 2006 that data governance is about 80 to 95 percent communication.[11] 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, a given tool may or may not be appropriate. Additionally, many tools that are not marketed as governance tools address governance needs and demands.

## See also

* Asset Description Metadata Schema
* Basel II
* Business semantics management
* COBIT
* Corporate governance of information technology
* Data Protection Directive (EU)
* Data sovereignty
* Health Insurance Portability and Accountability Act
* Information architecture
* Information governance
* Information technology controls
* ISO/IEC 38500
* ISO/TC 215
* List of datasets for machine-learning research
* Master data management
* Operational risk management
* Sarbanes–Oxley Act
* Semantics of Business Vocabulary and Business Rules
* Simulation governance
* Universal Data Element Framework

## References

1. ↑ Janssen, Marijn; Brous, Paul; Estevez, Elsa; Barbosa, Luis S.; Janowski, Tomasz (2020-07-01). "Data governance: Organizing data for trustworthy Artificial Intelligence". Government Information Quarterly 37 (3). doi:10.1016/j.giq.2020.101493. ISSN 0740-624X. https://www.sciencedirect.com/science/article/pii/S0740624X20302719.
2. ↑ Abraham, Rene; Schneider, Johannes; vom Brocke, Jan (2019-12-01). "Data governance: A conceptual framework, structured review, and research agenda". International Journal of Information Management 49: 424–438. doi:10.1016/j.ijinfomgt.2019.07.008. ISSN 0268-4012. https://www.sciencedirect.com/science/article/pii/S0268401219300787.
3. ↑ "The Evolution of Data Governance". DATAVERSITY. 18 May 2020. https://www.dataversity.net/the-evolution-of-data-governance/.
4. ↑ "FAQ". Digital Trade and Data Governance Hub. https://datagovhub.elliott.gwu.edu/faq/.
5. ↑ Bernier, Alexander; Molnár-Gábor, Fruzina; Knoppers, Bartha Maria (2022). "The international data governance landscape". Journal of Law and the Biosciences (Oxford University Press) 9 (1). doi:10.1093/jlb/lsac005. PMID 35382430.
6. ↑ Sarsfield, Steve (2009). The Data Governance Imperative. IT Governance Publishing. ISBN 978-1-84928-110-2.
7. ↑ Warburton, Daniel (2017-03-15). "The Data Governance Report 2017 – Your Copy". https://www.ciowatercooler.co.uk/the-data-governance-report-2017-your-copy/.
8. ↑ "eCFR — Code of Federal Regulations". https://www.ecfr.gov/current/title-21.
9. ↑ "Rimes Data Governance Handbook". 2013-10-16. https://www.rimes.com/rimes-data-governance-handbook.
10. ↑ Dai, Wei; Wardlaw, Isaac (2016). "Data Profiling Technology of Data Governance Regarding Big Data: Review and Rethinking". Information Technology, New Generations. Advances in Intelligent Systems and Computing. 448. pp. 439–450. doi:10.1007/978-3-319-32467-8_39. ISBN 978-3-319-32466-1.
11. ↑ Hopwood, Peter (June 2008). "Data Governance: One Size Does Not Fit All". DM Review Magazine. http://www.dmreview.com/issues/2007_48/10001356-1.html. "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."

## Further reading

* Sargiotis, Dimitrios (2024). DATA GOVERNANCE: A Guide. SPRINGER INTERNATIONAL. doi:10.1007/978-3-031-67268-2. ISBN 978-3-031-67267-5. OCLC 1442731250.
* Reichental, Jonathan (2023). Data Governance for Dummies. Hoboken, NJ: John Wiley & Sons, Inc. ISBN 978-1-119-90677-3. OCLC 1356475580.
* Ladley, John (2019). Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program (2nd ed.). Academic Press. ISBN 978-0-12-815831-9.
* Seiner, Robert S. (2014). Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success. Technics Publications. ISBN 978-1-63462-044-4.
* Plotkin, David (2013). Data Stewardship: An Actionable Guide to Effective Data Management and Data Governance. Morgan Kaufmann. ISBN 978-0-12-410389-4.
* Soares, Sunil (2014). The Chief Data Officer Handbook for Data Governance. MC Press. ISBN 978-1-58347-417-4.
* Madsen, Laura B. (2021). Disrupting Data Governance. Technics Publications. ISBN 978-1-63462-887-7.
* Eryurek, Evren; Gilad, Uri; Lakshmanan, Valliappa; Kibunguchy-Grant, Anita; Ashdown, Jessi (2021). Data Governance: The Definitive Guide. O'Reilly Media. ISBN 978-1-4920-6348-3.
* Sarsfield, Steve (2009). The Data Governance Imperative: A Business Strategy for Corporate Data. IT Governance Publishing. ISBN 978-1-84928-012-9.
* Stedman, Craig. "What is data governance and why does it matter?". https://www.techtarget.com/searchdatamanagement/definition/data-governance#:~:text=A data governance framework consists,how the program will work..

## External links

* DAMA International

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Data

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