What is data governance?

Data governance is a vital method of managing data throughout its existence, from collection to utilization to final disposition, in ways that support, benefit, and protect the ever-evolving enterprise. The ability of organizations to acquire vast quantities of disparate data means that they also require principles to maximize usefulness, mitigate threats, and minimize costs associated with that information.  The stakes associated with effective data governance continue to rise due to reliance on quality data by technologies like Machine Learning and Artificial Intelligence (AI), as well as evolving digital transformations, as enterprises grow and scale. 

Regardless of industry, data governance impacts whether and how data supports the enterprise’s strategic initiatives, organizational goals, and stakeholders in areas like security, compliance, supply chain management, finance, procurement, marketing, sales, and many more. But what, exactly, is data governance? 

Data governance defined

Data governance is a collection of activities the enterprise conducts to ensure data is reliable, confidential, accessible, and functional. It comprises the procedures organizations must undertake, the processes they must enable, and the technology that supports their efforts. For successful data governance, the enterprise must develop data policies, internal requirements that specify how data is collected, managed, stored, and eliminated, and establish metrics that assess the effectiveness of its data governance program. 

Data governance specifies the types of data that must be managed, and which people, applications, and technologies can access this data. These policies are informed by numerous external and internal factors, including regulatory compliance requirements, industry standards, government agencies, and organizational goals and objectives. To establish data governance, organizations must answer questions like: 

• What data can be acted upon? 
• Who can act on this data? 
• What actions can these entities (employees, third parties, applications, devices) take? 
• Under what circumstances can these entities take these actions on this data? 
• What methods can be utilized to take these actions? 

Ensuring integrity, privacy, and security while enabling quality, availability, and usability of structured and unstructured data requires an effective decision-making process at the core of the enterprise’s data governance program. The organization must make many choices in terms of investment prioritization, resource allocation, and key performance indicators (KPIs) to properly utilize data to advance business initiatives. 

 

The enterprise must consider the entire life cycle of its data when it comes to data governance, with attention to big data, digital transformation, and scaling business intelligence as key drivers.

Additional goals for data governance include: 

• Planning for and managing increasing volumes of data 
• Facilitating data access to support workflows 
• Reducing data silos to increase efficiencies 
• Implementing policies for data from new sources, such as devices, Application Programming Interfaces, and the Internet of Things (IoT) 
• Improving the audit process while streamlining compliance

The organization will benefit from increasingly valuable insights as it continues to enhance data quality, security, and accessibility with robust data governance. 

Benefits of data governance

Data governance enables centralized, reliable visibility into the enterprise’s data. Other benefits of data governance include: 

• Better-quality data, user confidence in that data, and a shared understanding of that data through data governance that generates a single source of truth throughout the organization 
• Enhanced quality of decisions and outcomes for the enterprise, in a timelier fashion, with stakeholders able to access the data they need to capitalize on opportunities and build better relationships 
• Enriched data analytics, with greater opportunities for business intelligence, machine learning, and other advanced data initiatives  
• Decreased costs as data resources are more efficiently managed, with less waste from conclusions drawn from flawed or obsolete data, along with fewer silos 
• Improved regulatory compliance, not only from avoiding penalties and risks associated with non-compliance, but with better data management, easier audits, and proactive intelligence about upcoming regulatory demands 
• Mitigated cyber risk from reduced exposure to unauthorized users, insider threats, and data breaches 
• A positive reputation for proper handling of sensitive data within the enterprise’s industry and amongst shareholders, customers, and suppliers 

The enterprise flourishes when it has precise, dependable, and trustworthy data, which can be realized with effective data governance. 

Data governance in the cloud

As the enterprise continues to migrate to the cloud, questions surface about how data governance must evolve and scale. Considerations include: 

• Data migration: The migration itself requires a data governance process to enable efficiency and security 
• Data security: Safeguarding data from threat exposure, ensuring privacy and confidentiality obligations are satisfied, and communicating assurances to stakeholders 
• Data visibility: Enabling stakeholders to access and control data across the organization as needed at every level, from making strategic business decisions to streamlining workflows 
• Data sprawl: Benefiting from cloud flexibility while minimizing impact from fewer controls in a decentralized environment 
• Regulatory compliance: Ensuring that the cloud provider conforms to legal standards and requirements and supports the enterprise on specifications, such as where data must reside 

Why data governance is useful

For many reasons, data governance is necessary. But beyond meeting mandatory requirements, data governance is useful to the enterprise in several ways, including: 

• Insights from customer data for service, renewals, cross-selling, and upselling 
• Product data including user behavior and usage that supports product design updates 
• Sales and marketing information for enablement and optimization 
• Integrations that streamline workflows, enable efficiencies, and offer insights on how various functions interact with one another across the organization 
• Real-time data availability for quick tactical pivots when needed 
• A system for archiving and deleting data as appropriate that makes the best use of organizational resources

What data governance is not 

Since there are many processes, practices, and procedures associated with data governance, it’s easy to confuse it with other data-related concepts, such as data privacy, data quality, data storage, data stewardship, data management, and master data management.  

Data governance vs data privacy

Data privacy is a part of data governance, but governance encompasses more than privacy. Implementing processes around data privacy throughout the governance program enables the enterprise to identify what data they possess, where it’s kept, and how it is utilized. 

Data governance vs data quality

Data quality defines the extent to which data is correct, comprehensive, and reliable, based upon the requirements for the enterprise. Data governance is the practice of management, control, and policymaking over data resources.  

Data governance vs data storage

Organizations that use a central repository for data storage and establish mechanisms for retrieving and managing that data are on the path to effective data governance, though data storage by itself is not data governance.  

Data governance vs data stewardship

Data stewardship is the aspect of data governance that addresses procedures, but not strategy, roles, policies, or processes. Stewardship involves the interpretation and execution of policies and processes, as opposed to creating or developing them, to ensure that the data is accurate and accessible to the appropriate parties. Data governance enables the right people to be assigned the role of data stewards.  

Data governance vs data management

Data governance is the foundation of data management, which describes administration of the complete data life cycle requirements for the enterprise. Data management implements data governance policies to collect and utilize data to make business decisions.  

Data governance vs master data management

Although effective master data management (MDM) requires good data governance, MDM goes further than governance. It emphasizes identification of the enterprise’s key entities, such as suppliers, customers, and products, and increasing the value derived from that data. The data governance program describes each key entity and its associated data policies. 

Data governance tools

The enterprise has many things to consider when it comes to data governance tools that will drive the best data governance approach. They include: 

• Scalability that enables focus on strategy alignment and supports data governance initiatives across business units 
• Machine learning and artificial intelligence (AI) to enrich decision-making and hone application performance 
• Cloud-based platforms to avoid additional overhead for on-premises servers 
• Fast, economical integration into current platforms with data pipeline tracking 
• Discovery, reporting, and benchmarking features to collect and analyze organizational data 
• Data quality capabilities for verification, cleansing, and enhancement 
• Data control features for evaluation and maintenance 
• Data documentation including metadata development (source, date, type, tags) 
• Self-service data stewardship technology for data profiling and monitoring the execution of the organization’s data governance policies 
• Automated data retention, archiving, and deletion to manage risk and generate cost efficiencies 
• Content management capabilities to digitize documents and incorporate appropriate content into operations and systems 

Data governance goals

Data governance goals should be defined at all levels of the enterprise so every stakeholder and team member will understand how to do their part to achieve them. Goals might include: 

• Increased consistency in data utilized for decision-making 
• Maximized revenue generation and reduced costs  
• Established baselines to generate key metrics and support continuous improvement 
• Better data security and data quality via clear accountability 
• Clarity for all stakeholders on data distribution policies, as well as how data ownership impacts data value 
• Metadata management to enact control on the collection and use of data 
• Improved planning and efficacy for teams, with less friction and rework 
• Decreased risk of regulatory non-compliance and associated penalties and fines 

Data governance roles

As mentioned above, data governance involves the entire enterprise; however, as with any initiative, certain roles are critical to ensure proper execution, reporting, and refinement of the data governance program. Some of the most crucial roles include: 

• Master data governance managers: People in this role oversee the design, execution, and maintenance of master data control and governance throughout the enterprise. 
• Data stewards: Also known as data champions, data stewards ensure that data standards and policies are observed on a daily basis. They frequently serve as the recognized experts for data entities and attributes and recommend improvements to data governance processes.  
• Solution and data governance architects: These team members direct solution designs and applications. 
• Data custodians: Also known as data operators, data custodians enable the onboarding, maintenance, and sunsetting of data resources.  
• Data owners: Also known as data sponsors, data owners are empowered to make and implement decisions across the enterprise. They are ultimately responsible for data as a resource. 
• Data analysts: Team members in this role utilize analytics to identify trends and provide reporting. 
• Data strategists: These team members contribute by creating and implementing plans based on trends detected via analytics. 
• Compliance specialists: People serving in this role manage conformity to mandatory standards and regulations. 

A data governance committee that comprises some or all of these roles is often established for significant areas of the enterprise to manage standards and policies and address escalated concerns. 

Data governance frameworks

Data is a digital asset that determines the success of the enterprise, and appropriately utilizing this data depends on a proper data governance framework. The data governance framework must support the organization and its strategies, goals, and objectives, as well as compliance programs and industry protocols.  

The data governance framework should also monitor data standards, specify essential roles and responsibilities, and establish deliverables for all functions. The best operating model will enable the framework to be seamlessly integrated into daily business activities.  

The data governance framework should include: 

• A data model that defines data flow: inputs, storage considerations, and outputs 
• Guidelines, policies, practices, methods, and procedures that are applicable to the data model 
• The organizational structure and accountabilities  
• A full scope of the relevant data and expected outcomes once the data governance framework is enacted  
• A data categorization and distribution process with defined channels, especially for sensitive data 
• A measurement and reporting plan with metrics that generate useful insights that enable refinement of the data governance framework 

Data governance best practices

Data governance best practices include: 

• Develop a business case for data governance that can be shared to generate support for the program; explain the need (including regulatory compliance), benefits, and required resources, as well as the risks and potential costs associated with failing to implement the program.  
• Select an executive sponsor to evangelize for the data governance program and keep it on track; this individual will demonstrate the preferred approach to the program, manage team member responsibilities, and respond to procedural questions.  
• Avoid treating data governance as a project; it is an ever-evolving practice that requires the executive sponsor and ongoing resources to keep pace with rapidly changing market conditions and regulations.  
• As with any initiative, establish baselines and then set specific, measurable, actionable goals; share reporting with relevant stakeholders and strive for iterative, ongoing improvements.  
• Ensure documentation includes standardized terminology and socialize these definitions throughout the enterprise, so everyone knows and uses the same language around data governance. 
• Keep the lines of communication open – not only with internal team members, but with partners, third-party vendors, suppliers, and customers. Stakeholders are more likely to conform to data governance requirements when they understand and appreciate the program and have a resource for getting questions answered. 

Data governance accelerates growth of the enterprise 

The enterprise possesses enormous quantities of data about customers, clients, suppliers, team members, third-party vendors and contractors, and more. The importance of this information is equally vast when it is utilized to enhance knowledge of the organization, its industry and market, and its customers and prospects. Data governance enables this ability while ensuring the quality, reliability, access, privacy, and security of the data. 

Aligning data with its proper purpose gives the enterprise greater confidence in the quality of its business decisions. Being able to trust its data while meeting regulatory compliance requirements and minimizing data risk is an enormous asset to any organization in an environment where many of their competitors are struggling to do so. 

Learn more about how optimizing and leveraging data will support your enterprise in amplifying business value. Schedule a demo today! 

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