Assessment

Data Governance assessment

A Jumpstart to Data Maturity

How mature is your organization’s data management? Have you made strides, or are you just starting out? The Data Governance Assessment provides a quick overview of your organization’s data-driven operations in just 5 minutes.

Unlock the potential the potential of your data.

Data Governance Assessment

Discover the status of your data governance

Our quick assessment, based on the DAMA DMBOK framework for information and data management, provides a clear picture of your organization's data maturity.

In just 12 questions, you'll gain valuable insights into your data management practices and current data governance status.

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Cegeka Data Governance Assessment

In-depth analysis

For a deeper understanding of your organization’s data maturity and to elevate your data management practices, explore Cegeka's comprehensive Data Governance Assessment approach.

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Data Governance Assessment: A Kickstart for Data Maturity

Good data governance is crucial for establishing a reliable data foundation. Cegeka’s in-depth Data Governance Assessment evaluates your current status and provides actionable recommendations and a roadmap for improvement.

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Raimond Joosten | Data Solutions

Mini Assessment

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Question 1 How aware is your organization about Data Governance?

1. Low awareness: Minimal understanding of Data Governance concepts.

2. Growing awareness: Increasing recognition of Data Governance benefits.

3. Strategic-level awareness: Awareness at strategic level within the organization about the importance of Data Governance.

4. Organization-wide awareness: General awareness of the importance of Data Governance throughout the entire organization

5. Periodic evaluation awareness: Regular checks and training to improve awareness of Data Governance.

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Question 2 Does your organization have a data strategy aligned with the business strategy?

1. No data strategy: No strategy or plan for data management.

2. Basic data strategy: We have a basic data strategy with goals for future data management.

3. Shared data strategy: We have shared and communicated our basic data strategy with everyone in the organization.

4. Data strategy implementation: Our business strategy is partly based on the developed data strategy.

5. Data as a strategic basis: Data is central to business strategy formulation.

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Question 3 Is there a department or team within your organization responsible for data processing and analysis?

1. No team: There is no specific team assigned for data management.

2. Decentralized team: Multiple teams work independently across the organization on data management.

3. Centralized team: A specialized team trained for data management.

4. Organized team: The data team is well-organized; it documents its activities and communicates effectively with the rest of the organization.

5. Proactive team: The data team proactively uses data to advise the organization at various levels, providing valuable contributions.

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Question 4 Are roles and responsibilities for data-related processes defined?

1. Unclear roles: There is confusion about task distribution within our organization.

2. Basic roles: Some roles are defined, but specific tasks and responsibilities are unclear.

3. Defined roles: Most employees are aware of their tasks and responsibilities.

4. Streamlined roles: Roles and responsibilities are clearly defined, with structured processes for collaboration and communication.

5. Dynamic roles: Roles are clearly defined and focused on continuous improvement, promoting smooth communication and collaboration.

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Question 5 Has a data policy been established and made available within your organization?

1. No Data Policy: There is no data policy available within the company.

2. Shared Data Policy: The data policy is known and widely shared within the organization.

3. Adhered Data Policy: All teams follow the guidelines as described in the data policy.

4. Integrated Data Policy: Employees have fully incorporated the data policy into their daily activities.

5. Measurable Data Policy: Key performance indicators (KPIs) have been developed regarding the data policy.

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Question 6 Have data-related processes been implemented within your organization for retrieval, management, exchange, and maintenance?

1. No processes: There are no established processes for handling data.

2. External processes: Processes for data exchange with external parties have been established.

3. Troubleshooting processes: Specific processes for resolving data issues have been introduced.

4. Change processes: Advanced processes for managing data changes have been added.

5. Optimization processes: Dynamic processes for regular data cleaning and optimization have been included.

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Question 7 Are data management standards documented within your organization, and is compliance ensured?

1. No standards: We currently lack documented guidelines for our data management practices.

2. Basic standards: Some basic data management guidelines have been established but are not fully documented.

3. Documented standards: Our data management rules are documented, but compliance could be improved.

4. Comprehensive standards: We have extensive data management guidelines, including processes to ensure compliance.

5. Advanced standards: Our data management strategy includes strict adherence to guidelines, supported by techniques for continuous improvement.

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Question 8 How would you assess the maturity level of data quality management within your organization?

1. No measurement: We currently do not measure the quality of our data.

2. Fixed measurement: We measure data quality at fixed intervals and always with the same method.

3. Active quality measurement: We measure data quality and take active steps to improve it.

4. Data lifecycle measurement: We measure and manage data quality throughout its lifecycle to make improvements.

5. Strategic quality measurement: Our data quality measurement is fully integrated with our strategic goals and contributes to measurable business outcomes.

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Question 9 Are there data quality reports within your organization that track the origin and transformations of data?

1. No reports: There are no existing data quality reports.

2. Non-focused reports: We have reports, but they are not directly focused on data quality.

3. Ad-hoc reports: Data quality reports are available but are used on an ad-hoc basis.

4. Preventive reports: Our data quality reports allow us to analyze and proactively address data quality issues.

5. Continuous reports: We use a centralized system for continuous storage and analysis of data quality issues.

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Question 10 What methods has your organization implemented to monitor data quality?

1. No data quality methods: There are no methods or tools in use for monitoring data quality.

2. Data quality monitoring: Methods and tools are available to continuously monitor data quality.

3. Data quality reports: There are specifically defined reports that provide insight into data quality.

4. Automated data quality: Data quality is continuously measured using automated tools, summarizing findings using KPIs.

5. Data quality returns: The high quality of our data yields financial benefits, which we measure using financial KPIs.

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Question 11 What security measures has your organization implemented to ensure data security, prevent fraud, and maintain adequate privacy and access policies?

1. No security: We do not have security measures for our data.

2. Basic security: We distribute data-related tasks among multiple team members as a security measure against fraud and errors.

3. Increased security: We have implemented security measures to ensure that only authorized personnel have access.

4. Advanced security: We employ a process for assigning and securing data-related roles and authorizations.

5. Dynamic security: We employ a dynamic access policy that ensures data security and privacy.

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Question 12 Is the data architecture within your organization documented?

1. No documentation: We do not have documentation for the data architecture.

2. Basic documentation: Our data architecture has been modeled, documented, and made available.

3. Comprehensive documentation: The diversity of systems and interfaces has been modeled, documented, and made available.

4. Advanced documentation: We have detailed processes for automatically cleaning or removing data architecture when it is no longer needed.

5. Monitored documentation: We have processes for monitoring and measuring data architecture.

Level 1 - Initial: Your organization is at level 1 - data governance is minimal. Data governance plays a limited role, and the resulting data may not always be used effectively. There are no specific control measures in place to address this. Data is considered an IT issue.

Level 2 - Managed: Your organization is at level 2 - data governance is managed. You are capable of reactively addressing data problems as they arise. Data governance and process improvements related to data management are typically treated as projects, but they are not an active pillar of the organization's strategy and objectives.

Level 3 - Defined: Your organization is at level 3 - data governance is defined. At this level, proactive and standardized data governance measures are in place.

Level 4 - Quantitatively Managed: Your organization is at level 4 - data governance is quantitatively managed. At this stage,the data governance controls you have developed are fully integrated. In addition, data quality and data management processes are quantitatively managed using key performance indicators (KPIs) and monitored using a dashboard.

Level 5 - Optimized: Your organization is at level 5 - data governance is optimized. At this highest level, data governance is fully integrated across the organization. Strategy and vision are data-driven and directly linked to data management. Using methods like causal analysis, you continually seek improvements that contribute to long-term efficiency and effectiveness. The organization recognizes the value that data governance brings to its operations.

Read more about the data governance assessment

How to elevate your data governance

From Level 1 to 2:

To move from level 1 to level 2 (managed), your organization must recognize data as a valuable business asset. Data shifts from being an IT concern to a business-critical asset. When data management issues arise, the organization launches project-based initiatives to address them. This reactive approach means acting as problems occur, which contributes to more controlled and effective data management within the organization.

How to elevate your data governance

From Level 2 to 3:

To bring your organization to level 3 (defined), it is necessary to establish a dedicated team focused on quickly identifying and resolving data issues and developing basic data management guidelines. A enterprise-wide data governance policy with clear processes is implemented, supported by proactive data quality management that continuously monitors and improves data. Processes are standardized across departments for consistency and efficiency, and employees are trained in data governance. These practices are regularly evaluated and adjusted for maximum effectiveness.

How to elevate your data governance

From Level 3 to 4:

To achieve level 4 (quantitively managed), data governance controls are fully integrated, making them an integral part of all business processes and daily operations. Data quality and data management processes are quantitatively controlled through KPIs aligned with data governance objectives. Additionally, an advanced dashboard continuously monitors data performance and quality, providing real-time insights and enabling rapid responses to necessary adjustments. These steps mark the transition to an advanced, data-driven operation where data quality is continuously monitored and optimized.

How to elevate your data governance

From Level 4 to 5:

To reach level 5 (optimized), you need an optimized data governance policy. Here, data governance is embedded in the business strategy and vision, with data at the core of all decision-making processes. It also involves the use of advanced analytics, such as causal analysis, that continuously seek improvements that drive long-term efficiency and effectiveness.AI technologies play a key role in this process, automating decision-making processes and enabling the organization to respond dynamically to market changes and opportunities. The organization takes a proactive stance in shaping and influencing future trends and developments, using data governance as a strategic tool for successful operations.

At Cegeka, we're here to help you enhance your data governance. Contact our experts to learn more.

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Data Governance assessment

Discover the status of your data governance

In-depth analysis

Mini Assessment