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What is data governance?
Every company needs a framework to ensure its data is accurate, secure, and effectively managed across all corporate operations.
This often requires the designation of people to plan and carry out a data governance program, incorporating best practices for storing and sharing this valuable asset.
Read on to explore the fundamental principles of data governance, how to implement a successful governance program, and how tools like Jira Service Management can automate the process.
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Data governance defined
Data governance is a comprehensive data management framework that ensures data is used correctly and effectively for as long as it belongs to an organization. It covers the entire data lifecycle, from collection and storage to usage and deletion.
Proper governance involves creating and implementing policies, procedures, and standards that maintain data quality, security, and compliance. It ensures that data is accurate, accessible, and protected while minimizing risks associated with data misuse or loss.
Good governance is essential for organizations that embrace a data-driven culture for strategic decision-making. This is because good governance ensures that data remains a valuable and trusted resource.
Why data governance matters
With the rise of big data, artificial intelligence, and evolving regulatory frameworks, businesses have come to manage vast amounts of information across various systems.
They must manage their data primarily to meet operational needs, all the while complying with complex regulations and best practices. Poor data governance can have severe consequences, including data breaches, loss of customer trust, and regulatory penalties.
Typical inefficiencies resulting from inadequate governance include data duplication, inconsistent reporting, and missed opportunities to use the data strategically.
The rapid adoption of AI-powered service management and machine learning technologies adds another layer of complexity to this scenario. These tools rely heavily on large, diverse datasets, and any lapses in data quality can lead to inaccurate predictions, biased outcomes, or security vulnerabilities.
A solid data governance framework will ensure that the data used in AI models is manageable and trustworthy.
Benefits of data governance
Organizations can maximize their value across various business functions by ensuring that data is properly governed.
For example, in HR operations only, data governance can map out HR automation best practices, such as the smooth execution of an onboarding workflow. With time, these practices extend to the entire HRSM department, supported by tools like Jira for HR service management.
If your framework clarifies data ownership, access, and usage, your teams will always have the correct data at the right time.
Here are the specific ways this kind of setup enhances productivity:
Improved data accuracy and consistency
Data governance ensures that data is standardized and accurate across systems, eliminating discrepancies arising from inconsistent data entry or outdated information. By establishing clear rules and processes for data collection, storage, and maintenance, governance frameworks ensure that data remains reliable and accessible for all stakeholders.
For example, consistent data governance helps prevent errors in financial reporting, as the same source of truth is used across departments. This leads to more accurate and timely financial statements.
Enhanced decision-making
Accurate, consistent, and timely data allows decision-makers to make confident choices, reducing their reliance on gut feelings or guesswork. With a clear, organized view of their data, businesses can spot trends, identify opportunities, and address challenges faster. Business agility is a prerequisite for staying competitive, and it is only possible if your high-quality data is readily available.
Compliance and risk reduction
Organizations across most industries must navigate various regulations, such as GDPR, HIPAA, and industry-specific standards that dictate how to manage data correctly. Effective data governance guides businesses through these controls and measures, protecting and securing data. For instance, GDPR requires businesses to handle personal data with transparency and accountability while also maintaining privacy. Companies risk hefty fines if they ignore these mandates.
Increased operational efficiency
Good data governance rests on clear rules that determine data access, quality, and usage. By following those rules, businesses can minimize errors, avoid duplicating efforts, and ensure that teams are working with the most accurate and up-to-date information.
For example, by eliminating unnecessary data silos, teams can easily locate and share data, reducing the time spent searching for or verifying information. Automated governance processes also reduce the manual effort involved in managing data, reducing the costs associated with data entry, maintenance, and auditing. This leads to faster workflows and more efficient use of resources across the organization.
Key principles of data governance
Effective governance frameworks must be designed and implemented with integrity, security, and accountability in mind. Those criteria set the foundation for data quality, compliance with relevant regulations, and transparency.
Data quality and integrity
For data to be effective, it must be complete, trustworthy, and consistent across systems. Data governance frameworks must establish processes to ensure that data is regularly cleaned, validated, and updated.
Without high-quality data, organizations risk making critical decisions in bad faith, which can lead to operational inefficiencies and missed business opportunities.
Data integrity is the starting point for good governance, because the other facets—compliance, security, analytics, and reporting—are only possible if based on reliable resources.
Downstream, business operations from product development to customer support depend entirely on trustworthy information.
Data privacy and security
As organizations collect and store vast amounts of information, particularly personal or confidential data, protecting against potential breaches or leaks is all the more important.
Key security measures include encryption, access controls, and audit logging. Encryption ensures that data is unreadable to unauthorized individuals, while access controls define who can view or modify specific data sets. Audit logs provide a trail of data usage history and modifications, offering accountability and traceability in case of security incidents.
Governance measures should also outline how to categorize the data based on its importance or sensitivity.
Accountability and ownership
Data owners and stewards must be designated to oversee the integrity, security, and accessibility of data and ensure that it is used according to organizational policies and legal requirements.
These roles should be formally assigned, with defined data quality, privacy, and security responsibilities. When stakeholders are fully aware of their duties, issues related to data mismanagement can be addressed rapidly.
Compliance with regulations
Organizations must align their data management practices with legal and regulatory requirements to minimize the risk of data breaches, unauthorized access, and penalties. This requires internal policies and procedures that ensure ongoing monitoring and enforcement of the broader rules. One example is GDPR, which mandates that organizations secure personal data and ensure individuals have control over their information.
Who is in charge of data governance?
Returning to the point about accountability and individually assigned roles, it’s worth noting that data governance must be collaborative.
Cross-functional collaboration is necessary because data flows across different departments and systems, including IT teams and business leaders. A concerted approach helps to avoid silos and miscommunication, which would hinder data access and affect quality.
Here’s how data stewards, data owners, and governance councils interact in this context:
Data Stewards are responsible for managing data quality and ensuring that data across systems is accurate, consistent, and reliable. They maintain data integrity by establishing data standards, performing data cleansing tasks, and ensuring that data governance policies are observed. Data stewards also act as the main point of contact for data usage and access, ensuring that data gets to the right stakeholders while remaining protected.
Data Owners are typically senior leaders or department heads who are responsible for specific datasets within the organization. They set the policies that guide data security and ensure data usage aligns with organizational and regulatory requirements. This means that data owners have ultimate accountability for ensuring that the data under their purview is managed correctly.
Governance Councils are cross-functional teams that provide oversight and strategic direction for data governance initiatives. These councils typically include representatives from various departments, such as IT, legal, compliance, finance, and operations. They ensure that data governance policies align with business objectives, comply with industry standards, and meet regulatory requirements. Governance councils are instrumental in resolving any disputes or challenges and ensure that all stakeholders are engaged and informed about data management practices.
How to implement a successful data governance program
By following a structured approach to data governance, the above teams can clearly communicate the plan to leadership, securing ongoing support. A good program also serves as a roadmap for adapting governance practices as the organization evolves.
Below are the key steps for managing data as a valuable asset:
1. Assess current data management practices
Begin by evaluating your organization’s existing data management policies, procedures, and systems. Identify any gaps or areas of inefficiency that need to be addressed. This assessment helps establish a baseline and highlights areas where improved governance can add value.
2. Define data governance policies and objectives
Develop clear policies that outline how data will be collected, managed, shared, and protected. These guidelines should include data quality standards, security protocols, privacy regulations, and compliance requirements. Set specific, measurable goals aligned with business objectives, such as improving decision-making or ensuring regulatory compliance.
3. Assign roles and responsibilities
Assign data ownership and stewardship roles to oversee the execution of governance policies. Ensure that these roles have well-defined responsibilities and a clear hierarchy of authority.
4. Select the right tools and technologies
Appropriate data governance tools, like Jira Service Management, should help automate processes like data cataloging, quality control, access management, and compliance monitoring. Look for solutions that integrate with existing systems, provide real-time data tracking, and offer strong security features. Many of these capabilities can be found in Jira Service Desk.
5. Establish a change management plan
Change management is the process of ensuring that your data governance program is embraced across the organization. Communicate the benefits of the program clearly to all stakeholders and provide regular updates on its progress. Address resistance by highlighting how governance will improve efficiency, decision-making, and compliance.
6. Focus on ongoing education and training
Even with the entire organization on board, data governance requires continuous education as data management practices, regulations, and technologies constantly evolve. Regular workshops or webinars will help keep staff updated on such changes. Offer training sessions to employees on governance policies, data security best practices, and the use of governance tools. This aspect of data governance sets you up for effective IT service management and contributes to employee experience management.
Best practices for managing data governance
Once a data governance program is in place, it should yield tangible benefits. So it’s during the planning, implementation, and updating of a program that you should tread carefully.
Here are some tips to keep the process on course:
Start small and scale gradually
Begin by focusing on a specific set of data assets or departments rather than trying to govern all data simultaneously. For example, you may prefer to start with high-priority areas such as customer data or financial records, which often have the most immediate business impact. A piecemeal rollout allows for a more manageable implementation and provides the opportunity to refine processes before expanding across the organization.
Align data governance with business goals
Data governance should not be seen as a standalone initiative, but rather as an integral part of achieving business objectives. Work closely with business leaders to identify pain points and define measurable goals that data governance can directly address. Typical goals could be improving customer insights, enhancing operational efficiency, or meeting regulatory requirements.
Measure the impact of data governance
Decide on the key performance indicators (KPIs) that will reflect the results of your data governance practices, such as data quality improvements, time saved through streamlined processes, or user adoption of governance tools. Analyze the KPIs regularly to track progress over time and adjust strategies as needed.
Balance policy, people, and technology
Policy-setting, staff response, and the tools you use are all variables that influence the effectiveness of your data governance program. So check that none of these components is a bottleneck within the overall program. Invest in training and resources to ensure teams understand how the governance policies and tools work. That’s when you’ll get feedback that may uncover potential issues.
Examples of data governance in action
Here are some hypothetical cases of how data governance can have an impact in different sectors:
Healthcare: Improving patient data management
A healthcare organization might implement data governance to manage patient records securely and efficiently, ensuring compliance with HIPAA. Defining access controls and creating a centralized data catalog could improve data accuracy and make patient records accessible in real time. Streamlining the system would reduce redundancies like data silos while improving data breach prevention and operational efficiency. This also supports efficient IT incident management.
Finance: Strengthening compliance and risk management
A financial institution might focus more on compliance with GDPR and Sarbanes-Oxley standards. It would also focus on data privacy, encryption, and auditing. Clear ownership roles across data stewards and owners would be a priority. The challenge lies in integrating governance practices with existing IT systems, but investing in integration tools would significantly improve audit processes.
Retail: Optimizing inventory and customer data
A global retailer would be more interested in inventory management and customer data accuracy. By implementing a unified data catalog, they would have consistent product availability and up-to-date customer data across all platforms. One challenge is regional inconsistencies in data entry, but standardized protocols resolve that issue. The result would be better inventory forecasting, reduced waste, and more effective marketing based on reliable customer insights.
Drive better data governance outcomes with Jira Service Management
Jira Service Management (JSM) offers powerful tools to streamline and automate data governance processes, ensuring compliance and efficiency at scale.
With its customizable workflows, JSM automates key tasks such as data cataloging, access control, and audit logging, reducing the risk of human error and ensuring that governance policies are consistently applied across your organization. Empower your teams with real-time tracking and reporting features to maintain high operational standards.
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