Precision
| Characteristic Name: | Precision |
| Dimension: | Accuracy |
| Description: | Attribute values should be accurate as per linguistics and granularity |
| Granularity: | Element |
| Implementation Type: | Rule-based approach |
| Characteristic Type: | Declarative |
Verification Metric:
| The number of tasks failed or under performed due to lack of data precision |
| The number of complaints received due to lack of data precision |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Ensure the data values are correct to the right level of detail or granularity | (1) Price to the penny or weight to the nearest tenth of a gram. (2) precision of the values of an attribute according to some general-purpose IS-A ontology such as WordNet |
| Ensure that data is legitimate or valid according to some stable reference source like dictionary/thesaurus/code. | (1) Spellings and syntax of a description is correct as per the dictionary/thesaurus/Code (e.g. NYSIIS Code) (2) Address is consistent with global address book |
| Ensure that the user interfaces provide the precision required by the task | (1) if the domain is infinite (the rational numbers, for example), then no string format of finite length can represent all possible values. |
| Ensure the data values are lexically, syntactically and semantically correct | (1) “Germany is an African country” (semantically wrong); Book.title: ‘De la Mancha Don Quixote’ (syntactically wrong); UK’s Prime Minister: ‘Toni Blair’ (lexically wrong) |
Validation Metric:
| How mature is the creation and implementation of the DQ rules to maintain data precesion |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| if v = Jack,even if v = John, v is considered syntactically correct, as Jack is an admissible value in the domain of persons’ names | C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| Data values are correct to the right level of detail or granularity, such as price to the penny or weight to the nearest tenth of a gram. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| Data is correct if it conveys a lexically, syntactically and semantically correct statement – e.g.,the following pieces of information are not correct:“Germany is an African country” (semantically wrong);Book.title: ‘De la Mancha Don Quixote’ (syntactically wrong); UK’s Prime Minister: ‘Toni Blair’ (lexically wrong). | KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published. |
| The set S should be sufficiently precise to distinguish among elements in the domain that must be distinguished by users. This dimension makes clear why icons and colors are of limited use when domains are large. But problems can and do arise for the other formats as well, because many formats are not one-to-one functions. For example, if the domain is infinite (the rational numbers, for example), then no string format of finite length can represent all possible values. The trick is to provide the precision to meet user needs. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
| Is the information to the point, void of unnecessary elements? | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
| The degree of precision of the presentation of an attribute’s value should reasonably match the degree of precision of the value being displayed. The user should be able to see any value the attributer may take and also be able to distinguish different values. | REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc. |
| The granularity or precision of the model or content values of an information object according to some general-purpose IS-A ontology such as WordNet. | STVILIA, B., GASSER, L., TWIDALE, M. B. & SMITH, L. C. 2007. A framework for information quality assessment. Journal of the American Society for Information Science and Technology, 58, 1720-1733. |
Data timeliness
| Characteristic Name: | Data timeliness |
| Dimension: | Currency |
| Description: | Data which refers to time should be available for use within an acceptable time relative to its time of creation |
| Granularity: | Record |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to lack of data timeliness |
| The number of complaints received due to lack of data timeliness |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Recognise the activity/event that generates the time sensitive attribute values and specify rules to generate attribute values. | (1)Efficiency of production line 1) Line out quality check which signifies the end of manufacturing of a product in a lean manufacturing line. 2) The number of products which passed the line out quality checks per given time period is the efficiency of the line |
| Specify the valid time period for the values of attribute to be recorded | (1) The growth of the bacteria should be measured after 15 hours of culturing (2) Efficiency should be calculated and recorded once in every 10 minutes starting from the first 10th minute of an hour (six times per hour) |
| Specify the valid time period for the values of attribute to be used | (1) The exchange rate for the day is valid from 8 am to 8am the following day |
Validation Metric:
| How mature is the creation and implementation of the DQ rules to handle data timeliness |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| stable data such as birth dates have volatility equal to 0, as they do not vary at all. Conversely, stock quotes, a kind of frequently changing data, have a high degree of volatility due to the fact that they remain valid for very short time intervals. | C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006. |
| the quotation of a stock remains valid for only a few seconds irrespective of architectural choices | 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. |
| For example, patient census is needed daily to provide sufficient day-to-day operations staffing, such as nursing and food service. How- ever, annual or monthly patient census data are needed for the facilityís strategic planning. | B. Cassidy, et al., “Practice Brief: Data Quality Management Model” in Journal of AHIMA, 1998, 69(6). |
| consider a system where each user must change own password every 6 months. Those passwords without been updated during more than 6 months, are not valid in the system, and can be treated as absolute stale elements | 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 sales information for a division of a company. This database contains three years’ worth of data. However, the database is slow to become complete at the end of each month. Some units submit their information immediately, whereas others take several days to send in information. There are also a number of corrections and adjustments that flow in. Thus, for a period of time at the end of the accounting period, the content is incomplete. However, all of the data is correct when complete. If this database is to be used to compute sales bonuses that are due on the 15th of the following month, it is of poor data quality even though the data in it is always eventually accurate. The data is not timely enough for the intended use. However, if this database is to be used for historical trend analysis and to make decisions on altering territories, it is of excellent data quality as long as the user knows when all additions and changes are incorporated. Waiting for all of the data to get in is not a problem because its intended use is to make long-term decisions. | 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: |
|---|---|
| A measure of the degree to which data are current and available for use as specified and in the time frame in which they are expected. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| Domain Level: The data element represents the most current information resulting from the output of a business event. Entity Level: The entity represents the most current information resulting from the output of a business event. | B. BYRNE, J. K., D. MCCARTY, G. SAUTER, H. SMITH, P WORCESTER 2008. The information perspective of SOA design Part 6:The value of applying the data quality analysis pattern in SOA. IBM corporation. |
| The “age” of the data is correct for the Knowledge Worker’s purpose . Purposes such as inventory control for Just-in-Time Inventory require the most current data. Comparing sales trends for last period to period one-year ago requires sales data from respective periods. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| Determines the extent to which data is sufficiently up-to-date for the task at hand. For example, hats, mittens, and scarves are in stock by November. | G. GATLING, C. B., R. CHAMPLIN, H. STEFANI, G. WEIGEL 2007. Enterprise Information Management with SAP, Boston, Galileo Press Inc. |
| Timeliness of data refers to the extent to which data is collected within a reasonable time period from the activity or event and is available within a reasonable timeframe to be used for whatever purpose it is intended. Data should be made available at whatever frequency and within whatever timeframe is needed to support decision making. | 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. |
| The currency (age) of the data is appropriate to its use. | PRICE, R. J. & SHANKS, G. Empirical refinement of a semiotic information quality framework. System Sciences, 2005. HICSS'05. Proceedings of the 38th Annual Hawaii International Conference on, 2005. IEEE, 216a-216a. |
| Timeliness can be defined in terms of currency (how recent data are). | SCANNAPIECO, M. & CATARCI, T. 2002. Data quality under a computer science perspective. Archivi & Computer, 2, 1-15. |
| 1) The age of an information object.
2) The amount of time the information remains valid in the context of a particular activity. |
STVILIA, B., GASSER, L., TWIDALE, M. B. & SMITH, L. C. 2007. A framework for information quality assessment. Journal of the American Society for Information Science and Technology, 58, 1720-1733. |
| The age of the data is appropriate for the task at hand. | WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33. |