What do you do if your response to a data governance failure is exacerbating the problem?

Data governance is the practice of ensuring the quality, security, and usability of data across an organization. It involves defining roles, policies, standards, and processes for data management and stewardship. But what happens when data governance fails? How do you deal with the consequences and prevent further damage? In this article, we will explore some common scenarios of data governance failure and how to respond effectively without making things worse.

1 Scenario 1: Data breach

A data breach is a serious incident that exposes sensitive or confidential data to unauthorized access or use, which can be caused by malicious attacks, human errors, or system vulnerabilities. It can have legal, financial, and reputational impacts for your organization and your customers. To respond to a data breach, it is necessary to identify the scope and source of the breach and contain it as soon as possible. Notifying the affected parties and authorities, as well as complying with any regulations or obligations is also essential. Furthermore, an investigation of the root cause and extent of the damage should be conducted, with the findings and actions documented. Finally, corrective and preventive measures should be implemented in order to improve data security and prevent future breaches.

Add your perspective

Khalid B.

Enterprise Architect | Data & AI Expert | Tech Lead | Telco & OSS/BSS | Gen AI & Synthetic Data Advocate | Let’s connect to explore how I can help !
Report contribution
After experiencing a data breach, it's crucial to take corrective and preventive measures to enhance data governance framework and especially the data security to prevent future incidents. It's important to be honest and transparent with stakeholders since many breaches occur due to human errors (8 out of 10). Start by collecting only necessary data and handling it responsibly. Clearly define who needs access to what data and for how long, and regularly review and update access permissions to minimize risks. Finally, during project's test & dev phases, avoid using or sharing real data with you IT team members or partners. Instead, use synthetic data (AI generated), which mimics real data without compromising sensitive information.

Like

Load more contributions

2 Scenario 2: Data quality issues

Data quality issues can significantly impact the accuracy, completeness, consistency, timeliness, or relevance of data and consequently affect the reliability and validity of data analysis and decision making. To respond to data quality issues, you should assess the impact and urgency of the issues and prioritize them accordingly. Furthermore, it’s important to identify the root causes and the data sources and processes that are affected. Additionally, you should cleanse and correct the data and validate the results. Finally, you need to implement data quality controls and monitoring to ensure data quality standards and expectations are met.

Add your perspective

Load more contributions

3 Scenario 3: Data compliance violations

Data compliance violations are breaches of the laws, regulations, or policies that govern the collection, use, storage, or sharing of data. These violations can occur due to ignorance, negligence, or misconduct and can expose your organization to legal risks, fines, sanctions, or lawsuits. To respond effectively to data compliance violations, you must review the relevant requirements and assess the severity and scope of the violations. Additionally, you should report the violations to the appropriate authorities and stakeholders and cooperate with any investigations or audits. Furthermore, you must remediate the violations and restore compliance as soon as possible. Lastly, you should implement data compliance training and awareness programs and enforce data compliance policies and procedures.

Add your perspective

Load more contributions

4 Scenario 4: Data silos

Data silos are isolated or disconnected data sets or systems that can prevent data sharing and collaboration across an organization. They can arise from organizational culture, structure, or technology and have the potential to hinder data visibility, accessibility, and usability. To combat data silos, it is essential to identify the data silos and their impacts and challenges for your organization and goals. Communicating the benefits of data integration and collaboration can help foster a data-driven culture. Additionally, implementing data integration solutions and platforms that enable data connectivity and interoperability is essential. Finally, establishing data governance roles and responsibilities, as well as promoting data stewardship and ownership, can help ensure successful integration of data silos.

Add your perspective

Load more contributions

5 Scenario 5: Data misuse or abuse

Data misuse or abuse is the intentional or unintentional use of data for unethical, inappropriate, or harmful purposes. It can be caused by lack of data literacy, awareness, or ethics and can damage the trust and reputation of your organization and your data, as well as violate the rights and interests of your data subjects. To respond to data misuse or abuse, you need to detect and stop it and assess the consequences and implications. You should also educate and inform data users and consumers about the proper and responsible use of data, enforce data policies and rules, apply data access controls and audits, protect data privacy and consent, and address any complaints or concerns.

Add your perspective

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

Add your perspective

What do you do if your response to a data governance failure is exacerbating the problem?

1

2

3

4

5

6

1 Scenario 1: Data breach

2 Scenario 2: Data quality issues

3 Scenario 3: Data compliance violations

4 Scenario 4: Data silos

5 Scenario 5: Data misuse or abuse

6 Here’s what else to consider

Data Governance

Rate this article

Thanks for your feedback

More articles on Data Governance

More relevant reading

What do you do if your response to a data governance failure is exacerbating the problem?

1

2

3

4

5

6

1 Scenario 1: Data breach

2 Scenario 2: Data quality issues

3 Scenario 3: Data compliance violations

4 Scenario 4: Data silos

5 Scenario 5: Data misuse or abuse

6 Here’s what else to consider

Data Governance

Rate this article

Thanks for your feedback

Explore Other Skills