Interpretability

Interpretability

Compare with other Characteristic

Characteristic Name: Interpretability
Definition: Data should be interpretable
Dimension: Usability and Interpretability
Granularity: Information object
Characteristic Type: Usage
Implementation Form: Process-based approach

Verification Metric:

The number of tasks failed or under performed due to the lack of interpretability of data
The number of complaints received due to the lack of interpretability of data

Validation Metric:

To what extent required capabilities and skills have been implemented to improve the data usage of a task

BackgroundGuidelines

The original definitions given below formed the basis of the consolidated definition of the characteristic.

Definition: Source:
Comparability of data refers to the extent to which data is consistent between organisations and over time allowing comparisons to be made. This includes using equivalent reporting periods. HIQA 2011. International Review of Data Quality Health Information and Quality Authority (HIQA), Ireland. http://www.hiqa.ie/press-release/2011-04-28-international-review-data-quality. More from this source
Data is not ambiguous if it allows only one interpretation – anti-example: Song.composer = ‘Johann Strauss’ (father or son?). KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published. More from this source
Comparability aims at measuring the impact of differences in applied statistical concepts and measurement tools/procedures when statistics are compared between geographical areas, non-geographical domains, or over time. LYON, M. 2008. Assessing Data Quality ,
Monetary and Financial Statistics.
Bank of England. http://www.bankofengland.co.uk/
statistics/Documents/ms/articles/art1mar08.pdf. More from this source
The most important quality characteristic of a format is its appropriateness. One format is more appropriate than another if it is better suited to users’ needs. The appropriateness of the format depends upon two factors: user and medium used. Both are of crucial importance. The abilities of human users and computers to understand data in different formats are vastly different. For example, the human eye is not very good at interpreting some positional formats, such as bar codes, although optical scanning devices are. On the other hand, humans can assimilate much data from a graph, a format that is relatively hard for a computer to interpret. Appropriateness is related to the second quality dimension, interpretability. REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc. More from this source

The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation

Guidelines: Scenario:
Standardise the interpretation process by clearly stating the criteria for interpreting results so that an interpretation on one dataset is reproducible (1) 10% drop in production efficiency is a severe decline which needs quick remedial actions
Facilitate the interaction process based on users' task at hand (1) A traffic light system to indicate the efficiency of a production line to the workers, a detail efficiency report to the production manage, a concise efficiency report for production line supervisors
Design the structure of information in such a way that further format conversions are not necessary for interpretations. (1) A rating scale of (poor good excellent ) is better than (1,2,3) for rate a service level
Ensure that information is consistent between units of analysis (organisations, geographical areas, populations in concern etc.) and over time, allowing comparisons to be made. (1) Number of doctors per person is used to compare the health facilities between regions.
(2) Same populations are used over the time to analyse the epidemic growths over the tim
Use appropriate visualisation tools to facilitate interpretation of data through comparisons and contrasts (1) Usage of tree maps , Usage of bar charts, Usage of line graphs

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