Sustainable Models

Sustainability means, of course, financial sustainability, but also many other factors that determine the ability of an data initiative to remain successful and continue to provide value or deliver impact, for the intended duration.

Consider some of the patterns I have encountered that are problematic in this regard.

Many data initiatives are intentionally designed to be short lived, relying on an initial infusion of resources for a pre-determined duration, usually 3 years or less. This is sometimes appropriate, and can help experiment with and derisk new approaches. But when applied as a default mode of intention, leads to problems for civil society as a whole.

Funders, including philanthropic funders, are rewarded for launches and announcements that sound exciting.

It leads to an emphasis on exciting “innovations” and disruptive approaches rather than strengh

Some of the reasons a data initiative may not sustain for its intended duration are:

• Funding runs out, and the team is unable to secure new funding.
• Team members leave the project. This type of instability can be caused by lack of career options for the team, poor or insufficient leadership, or simply because students graduate. Bus factor risk can exacerbate the impact of team turnover.
• Leaders lose interest. Inventive structures and motivation are skewed towards innovation and building, especially for leaders in academic institutions. Many leaders aren’t interested or well-suited to managing teams, sustaining operations.
• Takeover: The organization or project is absorbed, usually by a funder or sponsor. This can be subtle, as when board seats are captured to redirect resources.
• The absence or instability of legal structure can be the downfall of initiatives. For example, when the initiative has no clear owner, or when there are problems in attaining desired legal status and fiscal sponsorship.

There are good reasons for an initiative to shut down, of course. It may have achieved its stated purpose. Or it may be unsuccessful due to an incorrect theory of change, or have become irrelevant. Whatever the cause, there are important considerations at the end of a data initiative lifecycle.

Resilience

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