Interpretability

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

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

GuidelinesExamplesDefinitons

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

Validation Metric:

How mature is the process to maintain the interpretability of data

These are examples of how the characteristic might occur in a database.

Example:	Source:
when an analyst has data with freshness metric equals to 0, does it mean to have fresh data at hand? What about freshness equals to 10 (suppose, we do not stick to the notion proposed in [23])? Is it even fresher? Similar issues may arise with the notion of age: e.g., with age A(e) = 0, we cannot undoubtedly speak about positive or negative data characteristic because of a semantic meaning of “age” that mostly corresponds to a neutral notion of “period of time”	O. Chayka, T. Palpanas, and P. Bouquet, “Defining and Measuring Data-Driven Quality Dimension of Staleness”, Trento: University of Trento, Technical Report # DISI-12-016, 2012.
Consider a database containing orders from customers. A practice for handling complaints and returns is to create an “adjustment” order for backing out the original order and then writing a new order for the corrected information if applicable. This procedure assigns new order numbers to the adjustment and replacement orders. For the accounting department, this is a high-quality database. All of the numbers come out in the wash. For a business analyst trying to determine trends in growth of orders by region, this is a poor-quality database. If the business analyst assumes that each order number represents a distinct order, his analysis will be all wrong. Someone needs to explain the practice and the methods necessary to unravel the data to get to the real numbers (if that is even possible after the fact).	J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.

Example:

Source:

when an analyst has data with freshness metric equals to 0, does it mean to have fresh data at hand? What about freshness equals to 10 (suppose, we do not stick to the notion proposed in [23])? Is it even fresher? Similar issues may arise with the notion of age: e.g., with age A(e) = 0, we cannot undoubtedly speak about positive or negative data characteristic because of a semantic meaning of “age” that mostly corresponds to a neutral notion of “period of time”

O. Chayka, T. Palpanas, and P. Bouquet, “Defining and Measuring Data-Driven Quality Dimension of Staleness”, Trento: University of Trento, Technical Report # DISI-12-016, 2012.

Consider a database containing orders from customers. A practice for handling complaints and returns is to create an “adjustment” order for backing out the original order and then writing a new order for the corrected information if applicable. This procedure assigns new order numbers to the adjustment and replacement orders. For the accounting department, this is a high-quality database. All of the numbers come out in the wash. For a business analyst trying to determine trends in growth of orders by region, this is a poor-quality database. If the business analyst assumes that each order number represents a distinct order, his analysis will be all wrong. Someone needs to explain the practice and the methods necessary to unravel the data to get to the real numbers (if that is even possible after the fact).

J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.

The Definitions are examples of the characteristic that appear in the sources provided.

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.
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.
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.
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.

Ease of data access

Characteristic Name:	Ease of data access
Dimension:	Availability and Accessability
Description:	Data should be easily accessible in a form that is suitable for its intended use.
Granularity:	Information object
Implementation Type:	Process-based approach
Characteristic Type:	Usage