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1. About this case study
HXL (pronounced ‘hek-sil’) is a product and process innovation that aims to improve coordination across agencies responding in a humanitarian crisis, through a more efficient and effective system of collecting and sharing data. Led by the UN Office for the Coordination and Humanitarian Affairs (OCHA) with the support of a number of partners, HXL is a data standard and bundle of software tools branded as ‘a simple standard for messy data’. It aims to facilitate the exchange and merging of data across agencies to create a more complete and accurate operational picture of a crisis.
HXL itself has gone through a number of technical iterations. Two ideas were tested during the HIF grant period: Linked Open Data and Hashtags. Both have the same goal in mind (harmonisation of data to enable more comprehensive analysis) but focus on different degrees of change. With the first idea, Linked Open Data, the HXL team attempted to move the humanitarian community past its reliance on Excel spreadsheets to a system where the links between data are innate in how the data are presented. Linked Open Data is an extension of the worldwide web, whereby users are provided with a standardised way of expressing data so relationships between data points are clear. This allows computers to consistently report the meaning of hyperlinked data, which in turn facilitates cooperation. During the early implementation of Linked Open Data, the HXL team encountered challenges gaining wider uptake and took a step back to reassess the core problem.
This led to the development of a second idea: Hashtags. Hashtags require a smaller-scale change, focusing on creating commonality across spreadsheets without asking users to change their headers or titles or needing to agree on a common terminology. The advantage of the Hashtag solution is that it achieves harmonisation without requiring a significant change in practice among information management officers (IMOs). IMOs simply add a row of hashtags, very similar to those used in social media, to their datasets. In this way, software could then be developed to address the next level of data-related problems (e.g. cleaning data, merging data, facilitating analysis of data).
Overall, the innovation process has been successful in creating enhanced learning and evidence. Given the extremely iterative nature of the process and the fact that two ideas were brought through to the implementation stage, the diffusion stage had not fully commenced at the time of writing.
This case study was conducted on the basis of a review of key documents and interviews with 10 informants, including project partners and other stakeholders, over a period of three weeks in October and November 2015.