You're facing limited resources for data governance tasks. How do you decide what takes priority?

In my experience, prioritization always starts with finding the right alignment with the overall business strategy and business priority. Data Governance should be seen as a continuous journey rather than a one stop destination, reason being, the threats are increasing everyday and its always a race to stay ahead. Having said that, few things always take precedence, Eg. Regulator needs according to the Law of Land. It is also important to create a framework that allows you to adapt to these changes in a quick and agile way. Having a robust and agile framework also allows you to react quickly to any change coming in

Prioritize by focusing on these 4 criteria: 1st - Business impact - Identify tasks that align most closely with critical business goals and objectives. 2nd - Risk mitigation - Address areas where poor data governance poses the highest risks, such as compliance and data security. 3rd - Quick wins - Target tasks that can deliver visible results quickly, boosting stakeholder confidence and securing further support. 4th – Scalability - Prioritize initiatives that lay the groundwork for future scalability and efficiency, ensuring long-term value.

To prioritize data governance tasks with limited resources, assess business impact and focus on tasks that align with strategic objectives. Prioritize high-risk data, ensuring security and compliance of sensitive information. Evaluate key data usage and address stakeholder concerns. Identify quick wins for immediate value and conduct cost-benefit analyses for tasks. Ensure compliance with regulatory standards and prioritize data quality improvements. Establish a robust governance framework and consider resource availability and expertise. This structured approach ensures critical tasks are prioritized effectively.

By redefining responsibilities and strategically increasing quality controls, it is possible to reduce resource consumption while improving the relevance and efficiency of data exploitation. This requires active involvement from business units and judicious use of technological tools to support these efforts.

As governance tends to be top heavy in organisations, limitation on budget could improve lean project outcomes. Streamlining the decision making process in a flat structure is a way to save, but this requires the cost of time for training. Before focussing on risk, the structure of the organisation should become more agile such that any future implementations happen efficaciously.

Follow your Data Management Strategy and focus on the enterprise roadmap. Pick the most important task to focus on and invest your resources to complete a smaller but important scope. Do not try to boil the ocean. This is used more often than necessary but it is a common practice in Data Governance.

The data governance is a long term thing, not a short term project. It is important to keep the data governance as the same step with your company digitalize progress. Understanding your company’s digitalize strategy and roadmap first, don’t let the data governance is ahead than your company’s digital maturity. Otherwise, the data governance will be a policy task without the business real request to support. Develop some workshop or training to clarify the relationship between your company and industrial digital trend and data governance and management. When the important of data governance and management is be aware by the manager lever by the real business practices, the resource will not be a blocked thing.

Concordo plenamente com essa abordagem. Avaliar os riscos é essencial para uma gestão eficaz de dados, especialmente quando os recursos são limitados. Ao identificar quais ativos de dados são mais críticos e quais ameaças são mais prováveis ou prejudiciais, você pode priorizar a proteção dessas informações mais valiosas e garantir que os esforços e investimentos em governança de dados sejam direcionados de maneira eficiente. Essa estratégia não só ajuda a minimizar o impacto de possíveis compromissos, mas também contribui para a manutenção da confiança e da conformidade, que são fundamentais para a integridade e reputação da organização.

Start by identifying and assessing the risks associated with your data assets. Consider the potential consequences of data breaches, data loss, or non-compliance. Tasks that mitigate high-risk scenarios should be prioritized to protect the organization from significant harm.

When resources are limited, prioritize compliance with regulations that pose the highest risks to your business, like GDPR or industry-specific laws. Focus on those with the heaviest penalties. We've seen recent cases in which global companies have been fined millions of dollars for violations related to the GDPR - which serves as a major wake-up call.

Sua abordagem é muito adequada. Em um cenário de recursos limitados, focar na conformidade com as regulamentações que apresentam as maiores penalidades ou riscos para a continuidade dos negócios é uma estratégia prática e eficiente. Regulamentos como o GDPR, que impõem sanções severas por não conformidade, merecem atenção especial. Além disso, é crucial atender aos regulamentos específicos do setor que podem afetar a operação da empresa. Ao garantir que as leis mais rigorosas sejam cumpridas, você minimiza riscos legais e financeiros, ao mesmo tempo em que fortalece a posição da empresa em termos de conformidade e reputação.

Everything in an organisation should be prioritised based on value. Therefore, quantifying the business benefit of any initiative ahead is key, using a pre-established methodology. Governance tasks, as defined here, should rank very high on the priority list, with value being measured through cost avoidance mainly, as others mentioned.

Compliance with regulations and industry standards should be a top priority. Non-compliance can result in severe penalties, legal issues, and reputational damage. Identify the key regulatory requirements applicable to your organization and prioritize tasks that ensure adherence.

Non adherence to regulatory requirements can result in penalties from the regulators. So these should take priority over other internal requirements.

Compliance with regulatory requirements is non-negotiable, particularly in the face of increasing data privacy and security regulations globally. When resources are limited, focus on governance tasks that ensure your organization meets these legal requirements. Implementing data classification, retention policies, and maintaining audit trails for sensitive data should be prioritized. Compliance not only helps avoid penalties but also builds trust with stakeholders and customers.

Compliance would be my top priority but at same time try and understand the business impact and value that could be created by assuring the quality of the data meets regulations.

Evaluate the impact of data governance tasks on the overall business. Prioritize tasks that support key business objectives, enhance decision-making, or improve operational efficiency. Aligning data governance with business goals ensures that resources are used effectively to drive value.

Focus on the business value and outcomes (which are being enabled through the data governance program) and work with the key stakeholders, product owner(s) to prioritise scope and backlog for the team(s). This will inform where to invest resource and effort. Ultimately, let the business drive the prioritisation from which data governance resources and work can be aligned.

With limited resources and limited bandwidth, identifying the CDE’s (Critical Data Elements) is an old school but still relevant way to focus on governing those data assets that are the most impactful to the business: financially, compliance, exposure, and competitiveness. You don’t need to boil, or govern, the ocean.

Aligning data governance with business objectives is essential. With limited resources, prioritize tasks that have the most significant impact on the business. Whether it’s improving data quality for customer analytics or ensuring accuracy in financial reporting, focus on governance tasks that drive business value. Collaboration with business leaders is crucial to identify initiatives that will deliver the greatest return on investment (ROI).

Focusing on data quality is very important, especially when dealing with limited resources in data governance implementation. High-quality data provides the cornerstone for accurate insights and decision-making, increasing efficiency and lowering error-related costs. Prioritising data quality ensures that, even with limited resources, the data used is trustworthy, consistent, and actionable, maximising the value of data projects. Furthermore, it increases stakeholder confidence, improves compliance, and optimises operational performance, making it the most important step towards successful data governance.

High data quality is essential for reliable analytics and decision-making. Focus on tasks that address data quality issues such as accuracy, consistency, and completeness. Improved data quality leads to better business outcomes and increased trust in data assets.

Data quality is crucial in data governance because it directly impacts the accuracy, reliability, and trustworthiness of the data used for decision-making, analysis, and reporting. Informed decision-making_: Accurate data enables informed business decisions, reduces risks, and improves outcomes. Data integrity_: Good data quality ensures data is reliable, consistent, and free from errors. Customer trust_: High-quality data helps build customer trust. Competitive advantage_: Organizations with high-quality data gain a competitive edge by making better decisions and driving innovation. Data governance_: High-quality data is essential for effective data governance, ensuring accountability, transparency, and Compliance.

High-quality data is the foundation for effective decision-making and operational efficiency. Poor data quality can lead to errors, inefficiencies, and loss of trust. When resources are limited, prioritize tasks that address the most pressing data quality issues. Whether it's data cleansing, validation, or standardization, ensuring that critical data sets are accurate and reliable should be a top priority.

Successful data governance requires effective stakeholder engagement. Identify key stakeholders within the organization, such as data owners, stewards, and business leaders, and prioritize tasks that have strong stakeholder support. Engaging stakeholders ensures alignment with organizational needs and fosters collaboration across departments, which is essential for successful governance implementation.

Engaging stakeholders is crucial for successful data governance. Identify key stakeholders and prioritize tasks that involve their input and support. Effective stakeholder engagement ensures alignment, secures buy-in, and fosters a culture of data governance within the organization.

The following are some best practices for stakeholder engagement in data governance: 1. *Identify and categorize stakeholders*: Determine who has a vested interest in the data and group them by their level of interest and influence. 2. *Establish a governance framework*: Define roles, responsibilities, and decision-making processes for data management and use. 3. *Communicate effectively*: Provide regular updates, reports, and feedback to stakeholders on data governance initiatives and progress. 4. *Involve stakeholders in decision-making*: Encourage participation in policy development, data quality initiatives, and other data governance activities.

Involve key stakeholders in the decision-making process to ensure that the client's perspective is seriously considered. According to DAMA-DMBOK, data management should involve effective communication and expectation management with all concerned parties.

Data governance is an ongoing process that requires continuous improvement. Prioritize tasks that establish a foundation for future enhancements and scalability. This includes developing data governance policies, implementing training programs, and leveraging automation where possible.

Continuous improvement is important to focus on because Data Governance is never finished. You will always need to prioritize tasks based on the value they provide and how critical the data asset is. As long as you have buy-in across the organization, you should be able to continuously improve your data and get closer to having a single source of truth for your whole ecosystem.

With limited resources and limited bandwidth and/or budget, automate where possible and drive to self service. Data governance sometimes unnecessarily becomes a bottleneck - understanding what truly needs a data steward’s review and what can be managed by business stakeholders with the proper guidance will alleviate at least some of the pressure on the governance organization.

Data governance typically includes: 1. Data quality management 2. Data security and privacy 3. Data architecture and infrastructure 4. Data management policies and procedures 5. Data standards and metadata management 6. Data access and authorization 7. Data retention and disposition 8. Data backup and recovery 9. Compliance and risk management 10. Data governance framework and policies Effective data governance helps organizations: 1. Improve data quality and accuracy 2. Enhance data security and privacy 3. Increase data accessibility and usability 4. Support better decision-making 5. Reduce data-related risks and costs 6. Ensure compliance with regulations and standards 7. Improve collaboration and data sharing 8.data-driven innovation

Consider regularly reviewing priorities and adjust as needed to ensure alignment with changing business needs and resource availability.

Implement a systems approach when prioritizing data governance approaches. Don't focus only on your most commonly used data types, but also consider upstream datasets and metrics that may be necessary for work later. For example, data governance policies about patient utilization data won't be effective if the utilization data aren't available because the patient database is too unreliable to index. Remember that some of your most basic and fundamental data types undergird all the relational databases they touch and, while they may be the least substantively interesting, they are often the best place to start.

Business Impact. We need to gage what is is the criticalness of the data and its related operations. What are the risk spectrum from highest to lowest.

All these points remind me to the two primary ways of approaching Data Governance: offensive vs defensive. The Offensive one is all about a Proactive Strategy. This approach prioritizes data as a strategic asset to drive business value; Innovation and Growth, It emphasizes using data to create new products, services, and opportunities. Defensive Data Governance focus on Risk Management prioritizing the protection of data, ensuring compliance with regulations and minimizing risks associated with data breaches or misuse. There is a third way, my take, Integrative Data Governance, that embraces a Holistic Framework with a Balanced Prioritization in a Collaborative Culture that fosters Strategic Alignment by Secure Open Data Platforms.