Methodology for Data Governance Skills Building

Step 1: Needs Assessment - Understanding the Challenges:

Through engaging discussions with stakeholders from different sectors, it is important to delve into the unique challenges and requirements they face in implementing data governance practices. By actively listening and gathering diverse perspectives, we gain a deep understanding of the gaps in existing data governance processes and identify areas for improvement.

Step 2: Curriculum Development - Equipping with Essential Knowledge:

By having insights from the needs assessment, it is important to develop a comprehensive curriculum. This curriculum acts as a roadmap, guiding participants through the concepts, principles, and best practices of data governance. It should be crafted to address the identified competency areas, ensuring a well-rounded learning experience.

Step 3: Training Delivery - Interactive Learning:

The program should embrace interactive learning methods to foster engagement and active participation. Through in-person workshops, webinars, online courses, and self-paced learning materials, the program should create a vibrant learning environment. Trainers, who are experienced data governance practitioners and subject matter experts, bring the curriculum to life. Group discussions, simulations, and hands-on exercises facilitate the application of knowledge, encouraging participants to think critically and creatively.

Step 4: Hands-on Experience - Bridging Theory and Practice:

Participants are given the opportunity to embark on real-world data governance projects, enabling them to experience the challenges and complexities firsthand. They gain access to relevant datasets, tools, and technologies, putting their skills to the test. Working individually or in groups, they navigate the intricacies of data management, analysis, and governance, developing the confidence and expertise needed for success.

Step 5: Evaluation and Improvement - Enhancing Impact:

Robust evaluation mechanisms are put in place to measure the effectiveness and impact of the training. Feedback from participants, trainers, and mentors is collected, serving as valuable insights for refining the curriculum and training materials. By staying responsive to emerging trends and evolving practices in data governance, we ensure that the program remains relevant and impactful.

Next steps for governing institutions on Data Governance

The technology developments in recent years are causing significant impact to the way we conduct business. As such, there is a need to make efforts in digitizing processes to increase the quality of work, in private and public sectors.

Drawing on the knowledge gained during the two-day training at the Data Governance Academy, our governing institutions have lots of room for improvement in data management. Although there is limited information on how the government institutions store and maintain their data, based on the public data delivered upon an official request, the data is not structured the same across institutions of the same level (local and national level), as each institution have established their own internal databases, and there is no Data Governance unit or department at any level to develop data policies. This is also linked to the lack of staff with Data Governance skills and knowledge amid their ranks, and most often the burden of data management falls under the IT staff, which are often overloaded with other technical tasks.

Therefore, there is a need for establishing a national level mechanism tasked with responsibility and authority to develop data management policies and standards for all public institutions, establish monitoring and coordination staff in each institution that will serve as a point of contact for data governance, and offer capacity building trainings and seminars for designated data governance staff in each institution. Thus, the focus should be on the following three competencies: Governance and institutions; Value; and Skills.

In conclusion, although the governance institutions have some data management in place, there is still room for improvement to enhance inter-institutional cooperation and information sharing.

Data Governance Skills Building Program Methodology 

• Needs Assessment - In order to understand audience knowledge and skills

• Objectives - Describing outcomes of the program that we want to undertake.

• Instructions- By engage participants/group of interests through various methods.

• Experts - Including experts for insights and contribute to this industry and bring successful case studies.

• Trainings - Conducting trainings for groups of interests.

• Evaluation - Assessing test to gather feedback

• Practical Application - Applying knowledge through exercises and case studies.

• Continuous Learning - Encouraging ongoing self-improvement programs/academies and opportunities. 

• Follow-up and Support - Offering post-program assistance

• Conclusion - By summarise the key aspects of the methodology and re-applying if needed.

The methodology for a program on Data Governance Skills Building

Outline of a recommended methodology:

• Needs Assessment: Identify the target audience and their existing knowledge and skills in data governance; Determine the specific goals and objectives of the program; Conduct a gap analysis to understand the areas where participants require improvement.

• Curriculum Design: Define the key topics and subtopics to be covered in the program; Create a structure for the curriculum, keeping in mind the complexity and interdependencies of the topics; Decide on the appropriate learning format, which could include lectures, workshops, case studies, group discussions, and practical exercises.

• Content Development: Develop comprehensive and engaging content for each topic, considering different learning styles and preferences of the participants/audience; Utilize a combination of theoretical concepts, best practices, and real-world examples to enhance understanding; Ensure the content is up-to-date with the latest trends, technologies, and regulations in data governance.

• Delivery and Implementation: Identify the most suitable delivery methods, such as in-person workshops, online platforms, or a combination of both; Provide clear instructions and guidelines to facilitators or instructors for delivering the program effectively; Ensure access to necessary resources, such as slide decks, handouts, reference materials, or online tools.

• Engagement and Interaction: Foster a collaborative learning environment that encourages active participation and interaction among participants; Incorporate group activities, discussions, and case studies to promote knowledge sharing and practical application.

• Evaluation and Feedback: Regularly assess participants' progress and understanding through assessments and evaluations; Collect feedback from participants to estimate the effectiveness of the program, identify areas for improvement, and improve the content or delivery methods as needed; Measure the impact of the program on participants' skills and their ability to apply data governance principles in real-world scenarios.

Why data governance skills matter  

Despite clear evidence of their importance, building people’s skills and knowledge is rarely at the forefront of discussions around data governance. Most discussions around data skills focus on increasing technical capacity and training data scientists or, alternatively, on improving general data literacy among individuals. Increasing technical skills and data literacy are important to bring about a fair data future, as the Data Values white paper “Reimagining Data and Power” highlights. However, increased technical skills and widespread data literacy are not sufficient on their own to achieve accountable data governance. When data governance skills are lacking and decision-makers do not understand the importance of governing data responsibly or of building and maintaining trust, data governance decisions are confined to compliance with existing laws. When data governance skills and knowledge are weak or absent, as is still the case in around 25 percent of countries, accountability is also weak or absent.

There is mounting evidence that weak data governance creates opportunities for exploitation. For instance, recent literature on data colonialism explores how organizations based in the Global North have been able to implement extractive data practices in Global South countries due to their perceived or real weakness of the legal frameworks. The Free Basics limited internet service that Meta (formerly Facebook) provides in developing markets, for instance, has been accused of harvesting huge amounts of metadata from its users and violating net neutrality rules, according to an investigation conducted by Global Voices in Colombia, Ghana, Kenya, Mexico, Pakistan, and the Philippines. China’s digital colonialism, which South African scholar Willem Gravett has defined as activities advocating for internet sovereignty (versus a global internet approach), is another prime example. Gravett argues that China encourages African governments to implement censorship in order to export authoritarian surveillance technologies and deploy AI and data mining techniques across the continent. This has been made possible by weak privacy and data protection legislation as well as lack of direct knowledge and experience of decision-makers, according to research by the Insikt Group.

As these researchers suggest, when data governance skills are lacking both among decision-makers and within the broader public, the only bulwark against misuse of data by governmental and nongovernmental organizations is laws and policies. Compliance with laws when they exist is important but does not meet the high standard of accountable data governance. And, when the necessary laws and policies are weak or absent, a lack of data governance skills prevents opportunities for checks and balances through participation to emerge.

All stakeholders have a role in governing data effectively

This participatory approach is the basis of what we call multistakeholder data governance. It is designed to culminate in shared principles and rules that foster engagement and buy-in. But the fairness, inclusivity, transparency and effectiveness of the process is critical in promoting trust and legitimacy, and therefore incentivizing participation in the data economy. Key stakeholders include governments, the private sector, civil society organizations (CSOs), academia, and individual producers and users of data. These actors may have competing incentives, play different or overlapping roles, [1]  and may have more or less power to influence the decision-making process. For example, governments take the lead on policy making and regulation, implementation and enforcement of rules, and fostering multilateral coordination and cooperation. CSOs, academia, and the private sector can contribute technical expertise and their perspective as end users to enrich the quality of policies, laws, and regulations. 

Effective multistakeholder data governance is challenging to implement in practice

A lack of transparency and openness of the proceedings, or barriers to participation, such as prohibitive membership fees, will impede participation and reduce trust in the process. These challenges are particularly felt by participants from low- and middle-income countries (LICs and LMICs), whose financial resources and technical capacity are usually not on par with those of higher-income countries. These challenges affect both the participatory nature of the process itself and the inclusiveness and quality of the outcome. Even where a level playing field exists, the effectiveness of the process can be limited if decision makers do not incorporate input from other stakeholders.

Governing Data for Development

Governments that can successfully harness the world’s ongoing digital transformation and the resulting proliferation of new datasets, data types, and data ecosystems can make better informed policy decisions and target their resources more efficiently and effectively. To achieve this goal, they must establish clear rules about how data is collected, analyzed, used, and shared in a way that protects citizens from abuse while supporting innovation, development, and inclusive growth. Today, there is growing interest among government officials to draw insights from data that are more granular and produced at a higher frequency than traditional official statistics, including administrative data and, increasingly, data collected by the private sector. While integrating data from private and public sources can help governments fill knowledge gaps, it also raises novel ethical, legal, and regulatory concerns for policymakers to contend with.

References: 

• https://www.data4sdgs.org/governing-data-benefit-people-decision-making-builds-trust-and-accountability

• https://blogs.worldbank.org/opendata/promoting-trust-data-through-multistakeholder-data-governance

• https://www.cgdev.org/working-group/governing-data-for-development

Mapping Data Governance 

Step 1: Emphasize the Importance of Data - Highlighting the significance of data is crucial to attracting widespread adaptation from institutions. Communicate the value of data as a strategic asset that drives decision-making, improves operational efficiency, and fosters innovation. Raise awareness among stakeholders about the risks associated with poor data quality and inadequate data protection. Clearly articulate the impact of data on business outcomes and customer trust, encouraging a data-centric mindset throughout the institution.

Step 2: Establish Hierarchy - Establish a clear data governance hierarchy to ensure accountability and effective management of data. Define roles and responsibilities within the institution, such as Data Stewards, Data Custodians, and Data Users. Data Stewards should oversee data governance, ensuring integrity, quality, and compliance. Data Custodians manage data storage, access, and security. Data Users leverage data for analysis, reporting, and decision-making. This hierarchical structure enables efficient decision-making, facilitates collaboration, and ensures data ownership across the institution.

Step 3: Implement Training Programs - Develop comprehensive training programs to enhance data literacy across the institution. Assess existing data literacy skills to identify gaps and areas for improvement. Design training materials that cover data management, data quality, data privacy, regulatory compliance, and data analysis techniques. Offer a combination of in-person and online training options to accommodate different learning preferences. Foster a data-driven culture by encouraging employees to rely on data insights when making decisions and recognize and reward data-driven behavior.

Step 4: Evaluation - Define key performance indicators (KPIs) to evaluate the effectiveness of the data governance program. Establish metrics such as data quality, compliance rates, data access controls, user satisfaction, and reduction in data-related incidents. Conduct regular assessments to measure progress against these KPIs, identify areas for improvement, and take corrective actions. Continuously monitor the institution's data governance maturity and proactively address emerging risks to ensure ongoing improvement and compliance.

Step 5: Implementation - Develop and implement comprehensive data governance policies and procedures that align with regulatory requirements and industry best practices. These policies should cover data classification, data security, data retention, data sharing, and data access. Establish data governance committees or councils comprising representatives from various departments to provide guidance, resolve data-related issues, and ensure cross-functional collaboration. Implement appropriate tools and technologies to support data governance activities, such as data cataloging, data lineage, and data quality management solutions.

Example of Data Governance Capacity Building Programs that are implemented worldwide