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.

Data freshness

Characteristic Name:	Data freshness
Dimension:	Currency
Description:	Data which is subjected to changes over the time should be fresh and up-to-date with respect to its intended use.
Granularity:	Element
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due to lack of data freshness

The number of complaints received due to lack of data freshness

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
Identify the natural factors which creates a particular data obsolete	(1) A seasonal change may impact the customer's food preferences. (2) Customers who are students may change their addresses frequently.
Considering the above factors plan for data refreshing activities by specify the frequency of refreshing the data elements and adhere to the plan.	(1) Customer contact information should be refreshed annually.
Identify the master data that may change over the time but may be used in longitudinal analysis.	(1) Name of customer in 2001 is ABC (PLC) Ltd, after a merger in 2006 its name is XYZ (PLC). This customer is an ongoing customer in the customer master file
For such master data maintain longitudinal versions with time a stamp in such a way they can be linked in longitudinal analysis	(1) 2001-2005: ABC (PLC) (2) 2006-20012: XYZ (PLC)

Validation Metric:

How mature is the process for ensuring data freshness

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

Example:	Source:
let us consider two databases, say A and B, that contain the same data. If at time t a user updates data in database A and another user reads the same data from database B at time t' (t < t' ), the latter will read incorrect data. If t and f are included within the time interval between two subsequent data realignments	C. Cappiello, C. Francalanci, and B. Pernici, “Time-Related Factors of Data Quality in Multichannel Information System” in Journal of Management Information Systems, Vol. 20, No. 3, M.E. Sharpe, Inc., 2004, pp.71-91.
currency indicates how stale is the account balance presented to the user with respect to the real balance at the bank database.	V. Peralta, “Data Freshness and Data Accuracy: A State of The Art”, Instituto de Computacion, Facultad de Ingenieria, Universidad de la Republica, Uruguay, Tech. Rep. TR0613, 2006.
Consider an air traﬃc control center which receives data from several controller stations. To regulate air traﬃc, the traﬃc control center has to cope with uncertain data.Thus, the decision process must balance the delaying receiving more accurate data of airplane positions and the critical period of time in which an“eﬀective” decision must be made to regulate traﬃc;	B. Pernici, “Advanced Information Systems Engineering” in proc. The 22nd International Conference, CAiSE, Hammamet, Tunisia, June 2010.

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

Definition:	Source:
A measure of the rate of negative change to the data.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
Is the information upto-date and not obsolete?	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
Data is accurate if it is up to date – anti example: “Current president of the USA: Bill Clinton”.	KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published.
Currency refers to the degree to which information is current with the world that it models. Currency can measure how up to date information is and whether is it correct despite possible time-related changes. Timeliness refers to the time.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
Currency refers to the degree to which information is current with the world that it models. Currency can measure how “up-to-date” information is, and whether it is correct despite possible time-related changes. Data currency may be measured as a function of the expected frequency rate at which different data elements are expected to be refreshed, as well as verifying that the data is up to date. For example, one might assert that the contact information for each customer must be current, indicating a requirement to maintain the most recent values associated with the individual’s contact data.	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
A datum value is up-to-date if it is correct in spite of a possible discrepancy caused by time related change to the correct values; a datum is outdate at time t if it is incorrect at t but was correct at some time preceding t. currency refers to a degree to which a datum in question is up-to-date.	REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc.