What are data governance frameworks?

Data governance frameworks are collections of rules, roles, and processes designed to help ensure that an organization is properly managing its data.

It’s no secret that for many modern businesses, success or failure is defined by how well they can collect, control, and command their data. But while data is a well-known essential across nearly every industry, understanding what data is—and how best to put it to use to advance company objectives—can still be a hurdle for many businesses. Data governance describes the processes associated with managing the security, integrity, availability, and usability of company data, ensuring that vital data is not only reliable, consistent, and well defined, but that it is also being put to effective use.

Data governance is, as the name suggests, a solution for helping organizations govern their data. But data governance cannot exist in a vacuum; for data governance to provide actual value, it relies on established structures, rules, and processes, in the form of data governance frameworks. These frameworks help streamline potentially complex governance tasks, scale core processes, and facilitate data-management collaboration, while also ensuring compliance with data-management standards and regulations. In other words, data governance frameworks help businesses define and document how data is being administered across the entire enterprise.

Data governance frameworks must be capable of facilitating collaboration throughout the organization. As such, vital data governance stakeholders can be found across nearly every level of the enterprise.

Data owners

Continuous monitoring is essentially 24/7 surveillance for network activity. When suspicious, non-compliant, or otherwise unauthorized activity occurs, continuous monitoring provides optimal transparency and gives organizations a chance to respond.

Data stewards

Data stewards are the champions of the data governance framework. Stewards help ensure that any daily data practices within the business are in line with established policies and standards, and that these practices are being followed. Data stewards generally either take the responsibility of caring for the data themselves, or take a more administrative approach by training and consulting others on how to manage data assets.

Data custodians

Data custodians are appointed to handle various technical aspects of the data governance framework. This may include onboarding and maintenance, as well as any data asset end-of-life updates.

Data governance committee

The data governance committee may be thought of as the ruling body of the data governance framework. Working together, this group of individuals will have significant say in establishing and approving data standards and policies and will also act as the authority when faced with emergent data issues. Larger enterprises may need to further delineate by creating subcommittees for different data domains within the business. Additionally, the aforementioned roles should ideally be supported by a dedicated data governance team.

Although different organizations may choose different titles or roles for their data governance teams, the basic structure generally includes the following members:

Architect

The data governance architect is responsible for supervising the design and implementation of data governance solutions.

Analyst

The data analyst reviews vital information and applies analytical models to identify trends and gather actionable insights related to data governance.

Strategist

The data strategist works closely with the data analyst, using data insights and forecasts to develop and execute data strategy.

Compliance specialist

The data compliance specialist monitors data usage within the governance framework to confirm that all required standards are being adhered to.

Manager

Data managers help direct data governance across all levels of the organization.

There are additional roles in the organization you should consider involving in the development of your data governance framework including the platform owner, enterprise architect, security administrator, audit/risk expert, and business process stakeholders.

Although data governance teams may play a critical role, there are also other methodologies for improving data governance frameworks. One such approach is by using a data governance maturity model.

A data governance maturity model is used to measure an organization's data governance framework and initiatives and communicate them in ways that may be understood by the entire company. By comparing their data governance framework against a maturity model, the organization can accurately gauge the effectiveness of their data management initiatives.

In mature data governance frameworks, all the necessary processes are securely in place to access, manage, and improve business functions using reliable data.

The basic maturity model for data governance frameworks is the progressive model, and consists of several phases that may be used to track the progress of their data governance initiatives towards true data maturity:

Phase 0: Unaware

In Phase 0, the organization is largely unaware of the initiative or the importance of the data in question; processes are almost entirely reactive and non-standardized. To move the initiative to the next phase, relevant goals must be established, and stakeholders and decision makers will need to be educated as to the importance of the data.

Phase 1: Aware

Entering Phase 1, the organization has begun to become aware of the importance of the data initiative. Existing data practices are well documented and pain points become more obvious, allowing for the initial development of data governance frameworks to address these failings.

Phase 2: Reactive

In this phase, the groundwork is being laid for future data governance. Information is being shared between internal teams, and an information management system is in place, though adoption is likely not yet universal. With more collaboration between teams, other issues become apparent.

Phase 3: Proactive

Data governance begins to play a role in essentially every project within the organization, and the information management system is widely used. A governance team is established, and information owners are assigned. A truly comprehensive data governance framework may now begin to take shape covering the entire organizational structure.

Phase 4: Managed

Data and data insights are now appropriately viewed as valuable company assets. Data management policies are well understood and adhered to throughout the enterprise, valuable information metrics are clearly defined, and data is correctly categorized. Organizations can now take a more granular look at any inefficiencies or data weaknesses and work together to implement improvements to the governance framework.

Phase 5: Effective

In the final phase, the organization reaches its goal of proper data management. The framework is integral to business decisions, and all levels of the business view data and data insights as essential assets for providing a competitive edge and ensuring future growth.

When it comes to maturity models, there is no perfect, plug-and-play solution that will be an exact match for every business. Instead, decision makers are encouraged to review multiple variations of maturity models, find something that is close to meeting their needs, and then tailor the model where necessary to make it fully compatible.

Ultimately, data governance exists to maximize the value and return of data by identifying and avoiding risks, promoting compliance, and securing potentially beneficial opportunities that might otherwise slip past. When assessing the effectiveness of data governance frameworks in meeting these goals, there are several factors of significant importance to keep in mind. These are known as the pillars of data governance readiness.

The four pillars of data governance readiness consist of the following:

Processes

Correct data processes help ensure that data is being properly managed and implemented, and that the data supporting and informing vital business functions is accurate and reliable.

Technology

Businesses need the right technology to support their data governance. This includes tools, programs, platforms, and technological expertise. Correctly implemented, data governance technology can be used to automate vital processes, accelerate time to value, and scale data governance solutions to meet increasing demand.

People

No data governance framework can remain effective without support from people at all levels in the organization. People work together to conceptualize and define processes and support strategic initiatives. Without commitment from the people that make up the enterprise, data governance will likely not meet expectations.

Contributors

Although technically also part of the ‘people’ pillar, contributors take the added responsibility of providing essential context and aligning initiatives with processes.

Data government frameworks operate based on seven key principles:

Value and outcomes

Ensuring that the data governance framework is in line with the established business goals, objectives, or outcomes. Effectively assessing value in data governance depends on reliable data analysis processes.

Accountability and roles

Defining roles related to decision making and promoting accountability for assigned responsibilities. Consider how you will authorize access to data at your organization and how you audit access to track changes to permissions.

Graphic lining out the frameworks of data governance.

Trust

Understanding the source, lineage, and history of data for more effective management of expectations and results. Without trustworthy sources of data, the framework has no solid foundation.

Transparency

Keeping data-governance analytics open and accessible. With clearly defined and followed decision-making processes there shouldn’t need to be any second guessing in terms of whether data is being ethically handled.

Risk and security

Securing the data governance framework against known threats and possible risks. Considering risk and security in data governance can mean the difference between creating a framework that supports important outcomes, and one that undermines them. 

It’s important to identify encryption requirements at your organization and how you plan to abide by them. If you are creating, storing, and/or accessing personally identifiable information (PII), how will you protect this data? You must secure PII at least to the same standards set by enterprise information security protocol. This is especially important in highly regulated industries, such as healthcare and finance. Some solution providers access a vast amount of information about your technology systems and assets. Unauthorized access to this data exposes your IT environment to potentially hostile actors. How will you secure this data in accordance with standards set by your IT information security team?

Education and training

Providing training for data owners and others who are responsible for building and working within the data governance framework. Effective data governance educational resources help keep all relevant personnel focused on the importance of data governance, while also reducing the likelihood of human-introduced errors.

Collaboration and culture

Building a culture where data governance is recognized as an essential advantage within the company. By creating a culture of data governance, organizations promote increased adoption of vital data governance practices.

An effective data governance framework brings with it many clear advantages, including increased protection against data breaches and cyber attacks, improved ROI on data analytics, reduced data management costs, and the democratization of data management responsibilities throughout the entire organization. That said, establishing a data governance framework also brings with it certain challenges:

Motivating employee adoption of data governance initiatives

Correct data processes help ensure that data is being properly managed and implemented, and that the data supporting and informing vital business functions is accurate and reliable.

Collaborating as part of larger IT governance policies

Data governance and data governance frameworks fall under the larger umbrella of IT governance and must work in collaboration with those policies.

Providing value without hindering other work

If data governance is difficult for users, detracts from other vital tasks or concerns, or is too rigid to be of use, then it won’t benefit the organization.

Choosing the correct tool stack

Data governance is heavily dependent upon support from technology and tools. Researching available options and making informed decisions regarding which technologies to use in managing the data governance strategy can be a complicated and time-consuming process.

To meet and conquer the challenges of building a data governance framework, successful organizations follow these best practices:

Being specific in assigning data governance roles

Data governance team members need to know not only what is expected of them and what their rights and responsibilities are, but should also be capable of operating cross functionally and willing and able to push new initiatives.

Mapping business goals and creating usable touchpoints

Early in the process, map out any goals the organization may have for data governance initiatives. Create a detailed roadmap that others will be able to use to break larger objectives into smaller, more manageable tasks.

Establishing and enforcing data formatting standards

Institute standards for data formatting so that every data set is easily usable and accessible at every stage. This includes standards for classifying and tagging metadata. Verify and enforce these standards using various tools.

Automating tasks and processes wherever possible

Automation is a vital aspect of data governance. Workflows, approvals, requests, and any other tasks that could conceivably and effectively be automated should be automated, to ensure accuracy and reduce the burden on team members.

Measuring data governance progress and effectiveness

Data governance frameworks exist to improve data quality and use. Identify and track the metrics that provide insight into progress towards these goals.

Given the importance of data in the modern digital world, data governance has become a prerequisite for business success. However, finding the right tool stack to empower your work force with effective data governance support can be extremely difficult. ServiceNow Governance, Risk, and Compliance (GRC) is helping companies of all kinds get more out of their data, with the Now Platform®.

The Now Platform is a cloud-based automation platform that easily integrates legacy software with modern systems to create seamless digital workflows, optimize vital processes, bridge silos, and enable innovation to create increased business value. The Now Platform seamlessly creates, stores, and processes eight different kinds of data: reporting, transactional, product-setup, common-service, foundational-platform, platform-configuration, platform-maintenance, and integrations data. ServiceNow data governance in the Now Platform defines how your data is owned, managed structured, and secured, so you can get the most out of the information that drives your business.

ServiceNow makes the world work better for everyone. ServiceNow allows companies of all sizes to seamlessly embed risk management, compliance activities, and intelligent automation into your digital business processes to continuously monitor and prioritize risk. ServiceNow Risk solutions help transform inefficient processes and data silos across your extended enterprise into an automated, integrated, and actionable risk program. You can improve risk-based decision making and increase performance across your organization and with vendors to manage the risk to your business in real time. And make risk-informed decisions in your daily work —without sacrificing budgets.

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