A Steady States Data Flow Model to Support Administrative Data Sourcing - Sweeney, Kevin J (International Journal of Population Data Science, 2018)
Administrative data represents the future sourcing status quo for government social sector agencies, increasingly replacing more expensive, labour-intensive and less timely survey collection. But an administrative data model brings challenges, based in the inescapable fact that such data has been collected by a different organisation, for a different purpose. The resultant cost to unpack undesirable qualities in the data can be significant and absorb inordinate levels of energy better spent deriving actionable insights. If the administrative data is integrated with other data, these costs are multiplied. New Zealand’ national statistical agency has developed a new style of data governance framework that includes a steady state model (SSM) for mapping data flow. As innovation, the SSM offers an effective way to articulate administrative data issues, supporting improved supplier-user coordination. As such it can potentially reduce data handling costs, bolstering the value proposition for administrative data generally. By providing a means of reflecting user organisation data lifecycle perspectives within the supplier’ business process environment, the SSM opens an engagement channel that bridges what can otherwise represent insurmountable communication barriers. And since both user and supplier are able to maintain their native frame of reference, the resulting engagement fosters collaboration while facilitating what is more likely to represent a mutually amenable outcome.