To enhance the use of data for decision-making, it is important to develop not only individual data skills but also institutions that have embarked on a journey to data maturity. This note provides some guidance on the key components of an institutional data strategy and a summary of the core components of sound data governance within organisations.

Supporting Government of Rwanda (GoR) institutions in developing and implementing practical, sustainable data and analytics strategies is a priority activity for phase 2 of the Rwanda Economy Digitalisation Programme (RED2). However, most of the principles in this note have wider application beyond Rwanda.

What should a good data strategy consider?

An important consideration is ensuring that the strategy is practical and sustainable without being overambitious. The strategy should prioritise data governance, data protection and privacy, and capacity development.

A realistic data strategy should not set objectives that are impossible to reach in the short term and should also demonstrate awareness that certain aspects of the strategy may be adjusted, or additional objectives may emerge, as capabilities are improved, technology advances or organisational mandates are adjusted.

Here are seven key components of a sound data strategy (this list is not exhaustive):

1. Define the objective of becoming a data-driven organisation in the context of the organisation’s mission, vision and strategy. In other words, attempt to express the organisation’s ’raison d’être’, its reason for existence, through the responsible use of data.
2. Define key analytical objectives – these should be expressed in meaningful terms to address mandates, key performance indicators, and other desired outcomes. Plan for machine learning (ML) and artificial intelligence (AI) integration. this should be expressed in meaningful terms to address mandates, key performance indicators and other desired outcomes.
3. Determine what data is needed and where it will come from. Remember to consider both current and future data requirements, data that may need to be sourced or data systems that may need to be established to generate the data that is needed, in addition to currently available data.
4. Lead with data governance as it is central to the strategic use of data to achieve organisational objectives. Where applicable you should consider incorporating ethical use and governance of AI and ML in the broader data governance framework. See the expanded section on data governance
5. Pay special attention to data protection and privacy. While many may argue that data protection and privacy are components of data governance, because of the wide range of legal and compliance factors related to the protection and ethical use of personal information, we recommend that a data privacy programme, which intersects with the data governance programme, is managed under the oversight of the organisation’s legal, risk and compliance functions.
6. Define how the data will be managed. Develop supporting strategies that consider:
   • Data management disciplines including the design of data systems and databases, data integration, database administration, etc.
   • Security and information risk management. A comprehensive strategy is needed for cybersecurity and information security. It is wise to consider which internal resources and skills are needed, the appropriate technologies, and the extent to which you will need to consult or rely on third-party specialists.
   • Infrastructure management. Consider the appropriate IT and network infrastructure for your needs including cloud services and external providers.
   • IT services management. This could include day-to-day technology support, the ability to log service requests and incidents, appropriate human and technology resources, standards used (such as ITIL or the Information Technology Infrastructure Library) and measurement of service quality.
7. Skills and capacity building needs. Identify and develop critical skills including the skills necessary to develop analytical insights and add value to the data the organisation holds. Consider roles such as data engineers, data integration specialists, data developers, data architects, data analysts, data wranglers, data scientists and, where appropriate, AI engineers.

By focusing on the outlined components—ranging from defining objectives and data governance to addressing data protection and capacity building—organisations can create a solid foundation for data maturity. This approach not only supports immediate goals but also prepares institutions to adapt to future advancements in technology and evolving mandates.

A well-crafted data strategy empowers organisations to use data responsibly and effectively, ensuring that they remain agile and resilient in an increasingly data-centric world

If you would like to learn more about our capacity development initiatives, please contact Angelos Munezero.