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Fit for purpose: engineering principles for selecting an appropriate type of data exchange standard

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Abstract

Data standards are a powerful, real-world tool for enterprise interoperability, yet there exists no well-grounded methodology for selecting among alternative standards approaches. We focus on a specific sub-problem within a community’s data sharing challenge and identify four major standards-based approaches to that task. We present characteristics of a data sharing community that one should consider in selecting a standards approach—such as relative power, motivation level, and technical sophistication of different participants—and illustrate with real-world examples. These characteristics and other factors are then analyzed to develop decision rules for selecting among the four approaches. Independent of the data exchange problem, we suggest two general practices in choosing a standards approach: (1) vertical decomposition of interoperability issues, in order to define a narrow, formal, tractable problem, and (2) option-exclusion rules, as they are much simpler than stating optimal-choice rules.

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Notes

  1. The list is not exhaustive, but seems to cover the cases we have seen in practice.

  2. It is not sufficient that the source collects information that is vaguely similar, and given the same name. For example, doctors might report DrugDosage or Exercise as quantity prescribed, while safety researchers want to know the actual amount consumed or performed. As another example, car manufacturers advertise list price, but economists and shopping sites want to know actual sale prices.

  3. A data exchange standard might make certain data mandatory—but this compels sources that do currently lack that information to reject the standard, losing its benefits for data they do have. Incentives and decisions to capture more information should be separate from the exchange standard.

  4. http://www.ihie.com/.

  5. http://www.asias.aero/.

  6. http://niem.gov. NIEM is widely used for data exchanges across agencies of the U.S. government.

  7. For example, for coding a patient’s problems, some ICD-9 values lack distinctions (was an arm injury to left or right arm?) needed for ICD 10 and vice versa. As another example, transforms may be impossible for certain financial data produced by public companies using the Generally Accepted Accounting Principles (GAAP) Taxonomy using the eXtensible Business Reporting Language (XBRL) (Zhu and Wu 2011a).

  8. The Enriched idea also makes sense with flexible encoding, but is omitted in order to avoid obscuring the main arguments.

  9. The story is symmetric for the sub-transform from message schema to consumer. Note that matchings are directional; in addition to “same as”, one can have “is usable for”, a kind of generalization.

  10. The tool limitations described in the previous section still apply. In fact, mature tools comparable to InfoSphere seem some distance off; the ontology community has not adapted the subtle transform semantics pioneered in (Fagin et al. 2003) when multiple producer objects map to multiple consumer objects.

  11. http://www.fhims.org/.

  12. https://www.niem.gov/news/Pages/uml-profile.aspx.

  13. http://www.hl7.org/implement/standards/rim.cfm.

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Acknowledgments

We thank Kim Warren and the MITRE Innovation Program for funding this effort. We also thank Rob McCready for helpful comments on health IT data standards.

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Authors and Affiliations

  1. The MITRE Corporation, Bedford, MA, USA

    Arnon Rosenthal

  2. The MITRE Corporation, McLean, VA, USA

    Len Seligman, M. David Allen & Adriane Chapman

  3. University of Massachusetts Lowell, Lowell, MA, USA

    Hongwei Zhu

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  1. Arnon Rosenthal
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  2. Len Seligman
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  3. M. David Allen
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  4. Adriane Chapman
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  5. Hongwei Zhu
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Correspondence to Arnon Rosenthal.

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Rosenthal, A., Seligman, L., Allen, M.D. et al. Fit for purpose: engineering principles for selecting an appropriate type of data exchange standard. Inf Syst E-Bus Manage 12, 495–515 (2014). https://doi.org/10.1007/s10257-014-0238-3

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