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How can Data Drive Good Governance? Ten Elements to the Ideal Data Ecosystem
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https://creativecommons.org/licenses/by-nc-nd/2.0/

Courtesy of Asian Development Bank / CC BY-NC 4.0

Why is transparency helpful for governance of major land-based investments? Last month, I had the chance to discuss that question with government officials and experts from a mix of lower to middle-income countries gathered for an executive development program of the Columbia Center for Sustainable Investment. The group had no trouble mapping out multiple benefits from reducing information asymmetries between stakeholders to informing accountability efforts within and outside of government. However, it was striking how much the conversation soon zeroed in on availability and use of data. 

That got us thinking – what might the ideal data ecosystem encompass? One that can underpin effective transparency and reinforce good governance, whether in land and agribusiness investments or more broadly. Here is the first cut list we generated.

Such a system would:

  1. Be designed in service of problem holders (including within government and industry as well as civil society.) Start with the problem users wish to solve (for example, to assess if the terms of a land deal will adequately benefit local communities) not the available data.

    Photo © Dominic Chavez/World Bank

  2. Ensure data producers work with domain experts and target users to provide ready access to relevant data i.e. more proactively match supply and demand – no more “build it and they will come” mentality (white elephant data platforms are already too numerous)
  3. Think beyond open data. Yes, open data is important, but there are also important uses of information that is not in the public domain (particularly within government), or is released only upon request through right to information frameworks. An effective ecosystem will harness all these data sources in support of good governance outcomes.
  4. Anticipate barriers to data use – maintain capacity to build data literacy where needed, adequately invest in infomediaries, design with an eye to the institutional incentives that will shape data production, sharing and use, and ensure access to information for those who don’t live in the digital sphere. Online is not everything.
  5. Support generation and use of data at multiple levels – local, national and global (recognition, for example, that a village leader near a new palm oil plantation may want different information from an official in the agricultural ministry and from the UN Food and Agriculture Organization, but all may need to call on information generated at different levels.)
  6. Benefit from sufficient back end infrastructure that is in service of users, but does not become an end goal in itself – this would include fit for purpose processes for effective data generation, processing and sharing, and standards that foster interoperability of datasets (Tim Davies offers useful guidance on ensuring design for networked usage that is not limited by stove piping of data).
  7. Build and maintain local capacity to service data needs – outside experts (too often from the North) cannot be the only source of expertise; local organizations need to be built that can support the ecosystem whether it be on data security or user-centered design.
  8. Support and integrate citizen generation of data – thinking of citizens not just as data consumers, but also producers and owners of data – in the case of land governance, examples include citizen-led water quality monitoring and citizen tracking of deforestation using drones.
  9. Have adequate safeguards for data generation, storage and use that minimize risks of data abuse, biases and privacy violations (critical for a sector such as land where information can be highly contested)
  10. Enable unanticipated uses – a presumption towards openness can encourage innovation; it is hard for data producers to anticipate what information different users might find useful and to what end. Don’t let uses be constrained by the imagination of data publishers.

What’s missing? How helpful is such a list? It is no substitute for a holistic framework for thinking about data use for accountability, such as the draft developed by Open Data Charter with TAI.  There is definitely some overlap.

For some participants in the Columbia program, the conversation was too divorced from reality to have immediate relevance. Even getting basic reliable information on investments is still a struggle whether in Tanzania or Cambodia. Nor are such issues limited to the land sector – the recent Africa Data Revolution Report reveals limitations in legal and policy frameworks, infrastructure, technology and interactions among stakeholders are cross-cutting and undermine the potential of data ecosystems across the continent. Africa is not alone in such struggles.

Incremental progress will be the reality, but it may help to have a vision of the ideal in mind.

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