Compare Page

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 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.