partial data can still be useful

• quality data that well represents intersubjective knowledges/truths as truthy as is achievable/respects an individual’s life as lived and is building towards functionally useful aims
• these kinds of data are useful even if (and sometimes especially so) they are partial, multi-modal or otherwise ‘messy’

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messy data is at risk of, but isn’t inherently, poor quality data

• in clinical data, other ‘mess’ examples given: ‘incomplete or duplicated patient records; omission of expected clinical assessment information; incorrect variable classification; inconsistent formatting; insufficient identification labels; inconsistencies between variables; incorrect coding, data entry errors; and/or multiple variables presenting in a single column.’
• poor quality data, less useful (less ‘explanatory power’) - ‘inefficient, inaccurate, invalid, unreliable, incomplete, irrelevant, too broad/ detailed, and/or out-of-date’

embracing messy data

• transfer of memory from the physical/mental to the digital LOSES information, a lossy process - this should be embraced and worked around rather than treating partial data as full data when this would be inaccurate, non-representational, harmful omission
• reduction of the qualitative to the quantitative
• Jones (2022) discusses the analysis of ‘messy’ data of vulnerable patient groups in naturalistic (NHS) settings and emphasises the importance of ‘good quality’ data over complete or ‘clean’ data in making clinical decisions with reasonable certainty (Jones, 2022)
  • Formalised records have been found to be often misleading, ineffective and harmful for TGDI patients (Singer, 2015), (Alpert et al., 2023), (Kirkland, 2021)
• an external record as somewhere between an index and direct recall
  • not an experience in totality but still useful alone. partial info, re-enlivened by proper context/surroundings/etc, experiential knowledge of specifics partially transferred when you have the requisite lived experience
  • “the records require an active input of my internal memory in order to be more precisely deciphered.” (Smolicki, 2017)

Mentions

• “creating and categorizing data in the face of uncertainty and complexity” ->

References

• Alpert, A.B., Mehringer, J.E., Orta, S.J., Redwood, E., Hernandez, T., Rivers, L., Manzano, C., Ruddick, R., Adams, S., Cerulli, C., Operario, D. and Griggs, J.J. (2023a) ‘Experiences of Transgender People Reviewing Their Electronic Health Records, a Qualitative Study’, Journal of General Internal Medicine, 38(4), pp. 970–977. doi: 10.1007/s11606-022-07671-6.
• Jones, A.M. (2022) ‘Improving Healthcare Access and Engagement with Vulnerable Groups Through the Transformation of Complex and Messy Naturalistic Clinical Data’ University of Brighton.
• Kirkland, A. (2021) ‘Dropdown rights: Categorizing transgender discrimination in healthcare technologies’, Social science & medicine, 289, pp. 114348. doi: 10.1016/j.socscimed.2021.114348.
• Singer (2015)