Top Data Governance Interview Questions
- 1. What do you understand by data governance?
- 2. What are the primary goals of data governance?
- 3. What are the 5 pillars of data governance?
- 4. What is the role of data stewards in data governance?
- 5. What is data lineage and why is it important?
- 6. Can you explain some of the techniques to ensure data governance practices are integrated into the organization’s software development lifecycle (SDLC)?
- 7. What are some of the main differences between data governance and data management?
- 8. How does data governance work with analytics solutions?
- 9. List some of the technologies that assist with data governance.
- 10. What are some of the tools available that can help automate parts of the data governance process?
- 11. How can organizations effectively enforce compliance of data sources with corporate policies?
- 12. How does data governance help organizations in a regulated industry, such as finance or healthcare?
- 13. Are there any risks related to a lack of data governance?
- 14. What are some of the steps you can take to build a data governance program from the ground up?
- 15. Should a data governance framework tackle data quality issues? Why and How?
- 16. What is the role of a Community of Practice in a data governance framework?
- 17. What are some of the KPIs to measure the effectiveness of the data governance program?
- 18. What challenges are associated with implementing an effective data governance program?
- 19. How can you ensure that the data governance program is sustainable?
- 20. What are some typical mistakes organizations make when implementing data governance policies?
- 21. Does data governance affect the customer experience? Explain.
- 22. What are some of the mature tools and products that can help organizations implement robust data governance?
- Related Reading
1. What do you understand by data governance?
Data governance is a set of policies and processes to ensure that an organization’s data is accurate, reliable, and secure. Data Governance ensures effective and efficient use of the organization’s data stores. It involves establishing standards for data collection and management, as well as access control to that data.
Data governance done properly can help organizations comply with relevant laws and regulations regarding the protection of sensitive personal or financial information such as HIPPA, GDPR, and SOX. Data Governance can be a part of a broader enterprise cloud governance framework.
2. What are the primary goals of data governance?
The primary goals of data governance are –
- Establish solid foundation for managing organization’s data from risks, ensure legal and regulatory compliance
- Create trusted data assets that the organization can rely on to gain insights and competitive advantage
- Provide the appropriate level of security for data access
- Support efficient and effective flow of data within the organization
3. What are the 5 pillars of data governance?
4. What is the role of data stewards in data governance?
Data stewards are typically responsible for managing specific data sets – ensure that the data set is accurate and secure and accessible to the appropriate resources within the organization. Data stewards accomplish this by setting policies, standards and processes for data collection, storage, retention. Data stewards are responsible for ensuring the enterprise data model is always kept up to date with any changes to the data sets they own.
5. What is data lineage and why is it important?
Data lineage refers to the traceability of data to its original source. This can be thought of as a map of how data has moved through different systems and transformation processes.
Data lineage provides an audit trail that data stewards can leverage to review whether the data collection policies and procedures are being followed and identify any potential risks.
Data lineage also allows organizations to track down and resolve sources of any data discrepancies or data drift.
Understanding the entire data landscape, including data movement, helps organizations take timely corrective actions.
6. Can you explain some of the techniques to ensure data governance practices are integrated into the organization’s software development lifecycle (SDLC)?
7. What are some of the main differences between data governance and data management?
The main difference between data governance and data management is that governance is an overarching strategy to build, monitor and effectively & securely leverage organizational data assets whereas data management involves more of a tactical set of tasks to improve and maintain high levels of data quality and availability over time.
Data governance sets policies, procedures and data stewards in place to ensure regulatory and legal compliance, standards for source data quality, common transformation patterns, security produces to ensure the relevant resources have access to relevant data sets, etc.
In contrast, data management involves activities such as data normalization, reducing redundancies (de-duplication), meta-data tagging, data source consolidation, integration with new sources and destinations, etc.
Data governance and management are distinct disciples yet closely related. Both play a critical role in an organization’s effective use of their data assets.
8. How does data governance work with analytics solutions?
Effective Data governance helps improve the accuracy, security and accessibility of analytics solutions. Analytics solutions typically require access to large amounts of enterprise data from many different sources and even from outside the organization.
Data governance helps the organization’s analytics strategy by setting appropriate policies and procedures in place for collecting, modeling and storing this data, ensuring reliability, accuracy, and lineage tracking. A good data governance strategy also has procedures in place to help ensure data security, access control and regulatory compliance.
In short, data governance allows organizations to make effective use of their investments in analytics solutions.
9. List some of the technologies that assist with data governance.
Some of the technologies that assist with data governance include:
- Metadata management tools: These tools help organize and tag data to ensure traceability, accuracy and integrity. For example – Apache Atlas, Informatica Metadata Manager
- Data Cataloging tools: Data catalogs that provide a comprehensive view of the available datasets in an organization. For example – Google Cloud Data Catalog, Amazon Glue Data Catalog, Oracle Data Catalog, Alation Enterprise Data Catalog
- Data quality monitoring: Data quality monitoring tools help assess data quality and help identify errors and inconsistencies. For example – Trifacta, Informatica Data Quality, Oracle Data Quality, Microsoft Azure Data Factory
- Data Profiling: Data profiling tools that analyze data to identify trends and outliers. For example – Google Cloud Datalab, AWS Glue DataBrew, Oracle Data Profiling and Discovery, Microsoft Azure Analysis Services
- Data masking: Data masking solutions, which anonymize or encrypt sensitive data to prevent unauthorized access. For example, Dynamic Data Masking in AWS Redshift, Immuta Platform, Tonic, etc.
- Business intelligence (BI): Business intelligence (BI) solutions, which provide insights into the available data sources and their usage across the organization. For example, Amazon Quicksight, Microsoft Power BI, Google BigQuery, Tableau, etc.
- Security tools: Security tools for monitoring and protecting data from unauthorized access.
10. What are some of the tools available that can help automate parts of the data governance process?
Some of the tools that can help automate data governance are – Collibra, Informatica Data Governance, Alation, Talend and open source Apache Atlas.
Collibra: With Collibra’s adaptive Data Governance framework, you can operationalize data governance workflows and processes to deliver trusted data for the people, by the people across your organization. (Read more about Collibra in our Collibra overview guide)
Informatica Data Governance: This software provides automated tools for data classification, metadata management, and data lineage tracking. It also has built-in security features to protect sensitive data from unauthorized access.
Alation: Alation is a data governance platform that helps organizations manage their data assets by providing automated tools for data discovery, classification, and metadata management. Alation also provides an audit trail and data lineage tracking to ensure data accuracy.
Talend Data Integrity & Governance: Talend makes it easier to discover, remediate, and share trusted data with simple tools to automate data processes, empower your team, and ensure data quality and governance. Talend Data Fabric simplifies data quality and compliance with built-in functionality for making sure your insights are trusted, governed, and actionable.
Apache Atlas: Atlas is an open-source, scalable and extensible set of core foundational governance services – enabling enterprises to effectively and efficiently meet their compliance requirements within Hadoop and allows integration with the whole enterprise data ecosystem.
Apache Atlas provides open metadata management and governance capabilities for organizations to build a catalog of their data assets, classify and govern these assets and provide collaboration capabilities around these data assets for data scientists, analysts and the data governance team.
Related Reading: Top Collibra Alternatives in 2023.
11. How can organizations effectively enforce compliance of data sources with corporate policies?
Organizations can effectively enforce compliance of data sources with corporate policies by implementing a robust data governance framework that encompasses the five pillars of data governance. The framework should include:
- Establishing clear policies and procedures for managing access to sensitive or confidential data.
- Ensuring that all users have appropriate authorization before accessing any data asset.
- Regularly auditing and monitoring data sources for compliance with corporate policies.
- Implementing automated tools to ensure accurate data classification and data lineage tracking.
- Educating the organization on best practices for data security and privacy.
By following these steps, organizations can effectively enforce compliance of their data sources with corporate policies.
12. How does data governance help organizations in a regulated industry, such as finance or healthcare?
Data governance is essential for organizations in a regulated industry, such as finance or healthcare, to ensure compliance with regulations. Data governance helps organizations by organizing and managing data assets according to corporate policies and more importantly, regulatory requirements. Such practices can include setting access controls, regular monitoring of data sources for accuracy, implementing automated tools to facilitate data classification and lineage tracking, and providing training and guidance on best practices for data security and privacy.
13. Are there any risks related to a lack of data governance?
Yes, there are numerous risks associated with a lack of data governance. Some of them are –
– Poor data accuracy and integrity due to inadequate control over data sources and data models, leading to incorrect decisions being made based on inaccurate information.
– Increased risk of unauthorized access to sensitive or confidential data due to lack of security measures in place.
– Increased costs associated with data integration and management due to lack of standardization.
– Loss of compliance with industry regulations due to incomplete or inaccurate documentation of data sources, policies, and processes.
– Reduced customer satisfaction due to poor quality data and incorrect decisions being made based on erroneous information.
– Loss of customer trust and brand reputation hit due to lack of data security and privacy measures.
14. What are some of the steps you can take to build a data governance program from the ground up?
You can kickstart a governance program by implementing the following six steps:
- Step 1 – Define Data Stewards
- Step 2 – Determine Data Governance Objectives
- Step 3 – Build a Data Governance Community of Practice
- Step 4 – Adopt a Common Data Model
- Step 5 – Set up Automation & Monitoring
- Step 6 – Establish a Feedback Loop
The article “Six Steps to Data Governance Implementation” covers these steps in detail.
15. Should a data governance framework tackle data quality issues? Why and How?
Absolutely. Maintaining data quality is an essential aspect of data governance and should be included in the framework. Data quality issues, such as duplicate entries and stale or incorrect data, can lead to inaccurate decisions being made. It is, therefore, important to ensure that the data quality controls are effective and always up-to-date.
Some of the steps that can help tackle data quality issues include:
- Establishing data stewards for data domains
- Setting up a process for validating and cleaning up existing data
- Regularly monitoring data sources for accuracy and integrity
- Establishing processes to detect, diagnose, and correct errors in the data
- Defining policies and procedures to ensure data completeness and accuracy
- Educating users on best practices for data entry and management.
16. What is the role of a Community of Practice in a data governance framework?
A Community of Practice (CoP) is an important element in a data governance framework. CoP is essentially a group of people with shared interests who can learn from each other and collaborate on projects. The CoP provides a platform for members to exchange knowledge, share experiences, discuss ideas and best practices around data governance, and help solve common challenges.
The data governance CoP can also serve as a conduit for feedback from all the stakeholders, allowing them to provide input on various aspects of data governance. This can help organizations stay make sure their data governance practices remain effective, relevant and aligned with the current needs of the business.
17. What are some of the KPIs to measure the effectiveness of the data governance program?
Some of the key performance indicators (KPIs) that can be used to measure the effectiveness of a data governance program include:
- Data Quality Score – A measure of how accurate and reliable the data is.
- Number & Frequency of Policy Violations – The number, type, and frequency of policy violations.
- MTTR for Data Issues – How quickly data issues are identified and addressed.
- User Satisfaction – A measure of how satisfied users are with the data governance program.
- Compliance Rate – A measure of how well the organization is adhering to regulations and guidelines.
- Cost Savings & ROI – A measure of how much money is saved and the return on investment.
- Data Governance Maturity – A measure of the level of data governance maturity within an organization.
18. What challenges are associated with implementing an effective data governance program?
Data governance is not an easy task, especially in large enterprises. Some of the main challenges associated with implementing an effective data governance program include:
- Establishing clear roles, responsibilities and expectations for data stewards and other stakeholders.
- Ensuring that all stakeholders follow the same set of standards and policies across the organization. The larger the organization, the more difficult this gets.
- Keeping up with changing regulations, compliance standards, or industry requirements.
- Staffing challenges, such as finding and keeping qualified data stewards.
- Overcoming cultural resistance to change and adoption of new technologies.
- Ensuring that data is kept secure while still making it accessible to those who need it.
19. How can you ensure that the data governance program is sustainable?
In order to ensure that the data governance program is sustainable, organizations should focus on developing an effective data governance framework and policies. It is also important to have clear roles and responsibilities for each stakeholder in the organization, as well as a process for ensuring compliance. Moreover, organizations should ensure that there are sufficient resources allocated to support the program. some of the steps that can be taken to ensure success and sustainability of governance programs are:
- Establish data stewards early in the process.
- Invest in tooling early on in the process.
- Follow the Six Steps to Data Governance Implementation.
- Establish CoP to gather feedback and adjust as necessary.
- The governance core committee must work with an agile mindset.
20. What are some typical mistakes organizations make when implementing data governance policies?
Some of the most common mistakes organizations make when implementing data governance policies include:
- Not establishing clear roles, responsibilities, and expectations for data stewards.
- Failing to create a comprehensive set of policies that covers all aspects of the organization’s data governance program.
- Not providing adequate training or resources to support the data governance program.
- Not maintaining an ongoing dialogue with stakeholders to ensure compliance and consistency.
- Not measuring the effectiveness of the data governance program or monitoring progress.
- Failing to keep up with changes in regulations or industry standards.
- Not having a system in place for tracking and resolving violations of policies and standards.
- Not adapting the data governance program to fit the changing needs of the organization.
21. Does data governance affect the customer experience? Explain.
Yes, data governance can have an impact on the customer experience. Data governance helps ensure that customer data is being collected, stored, and processed in a secure and compliant manner. It also helps to ensure that customer data is accurate, up-to-date, and relevant to the customer’s needs. Without proper governance, customer data is more likely to be mishandled or misrepresented, leading to a negative experience.
22. What are some of the mature tools and products that can help organizations implement robust data governance?
As of 2023, some of the popular tools that can help with robust governance implementation are – Collibra Platform, Axon (by Informatica), OvalEdge platform, Talend. This is just a short sampling of the many tools and services that address the data governance needs of organizations.