Value consistency

Characteristic Name:	Value consistency
Dimension:	Consistency
Description:	Data values are consistent and do not provide conflicting or heterogeneous instances
Granularity:	Element
Implementation Type:	Rule-based approach
Characteristic Type:	Declarative

Verification Metric:

The number of inconsistent data values reported in an attribute per thousand records

GuidelinesExamplesDefinitons

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

Guidelines:	Scenario:
For critical data elements provide standard classifications (values lists) for data entry interfaces and restrict arbitrary values across the system	(1) Country, city are taken from a standard list. (2) Generally accepted industry classifications are used to analyse customers industry wise (Education, Banking & Finance, Medical, Manufacturing…….
When data elements are combined for specific identification/management/accounting purposes, standardise such combinations and use them across the system.	(1) Customer and sales order are combined for identification purposes (2) Costs of wastage are associated with individual orders they are incurred and managed.
Define data attributes in such a way that data values are atomic and hence consistency can be maintained for any form of aggregation or consolidation	Name is divided into first name Middle name and Last Name
Maintain consistency in using unit of measures across different tables and different data bases	Sales price is in $ in Sales table and Accounts receivable ledger

Validation Metric:

How mature is the creation and implementation of the DQ rules to maintain value consistency

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

Example:	Source:
School admin: a student’s date of birth has the same value and format in the school register as that stored within the Student database.	N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013.
For example, data are inconsistent when it is documented that a male patient has had a hysterectomy.	B. Cassidy, et al., “Practice Brief: Data Quality Management Model” in Journal of AHIMA, 1998, 69(6).
the name of the city and the postal code should be consistent. This can be enabled by entering just the postal code and ﬁlling in the name of the city systematically through the use of referential integrity with a postal code table	Y. Lee, et al., “Journey to Data Quality”, Massachusetts Institute of Technology, 2006.
the data values ST Louis and Saint Louis may both refer to the same city. However, the recordings are inconsistent, and thus at least one of them is inaccurate.	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:
Domain Level: The data values persist from a particular data element of the data source to another data element in a second data source. Consistency can also reflect the regular use of standardized values, articularly in descriptive elements. Entity Level: The entity’s domains and domain values either persist intact or can be logically linked from one data source to another data source. Consistency can also reflect the regular use of standardized values particularly in descriptive domains.	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.
Determines the extent to which distinct data instances provide nonconflicting information about the same underlying data object. For example, the salary range for level 4 employees must be between $40,000 and $65,000.	G. GATLING, C. B., R. CHAMPLIN, H. STEFANI, G. WEIGEL 2007. Enterprise Information Management with SAP, Boston, Galileo Press Inc.
Data is consistent if it doesn’t convey heterogeneity, neither in contents nor in form – anti examples: Order.Payment. Type = ‘Check’; Order. Payment. CreditCard_Nr = 4252… (inconsistency in contents); Order.requested_by: ‘European Central Bank’;Order.delivered_to: ‘ECB’ (inconsistency in form,because in the first case the customer is identified by the full name, while in the second case the customer’s acronym is used).	KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published.
Consistency can be curiously simple or dangerously complex. In its most basic form, consistency refers to data values in one data set being consistent with values in another data set. Two data values drawn from separate data sets may be consistent with each other, yet both can be incorrect.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
In its most basic form, consistency refers to data values in one data set being consistent with values in another data set. A strict definition of consistency specifies that two data values drawn from separate data sets must not conflict with each other, although consistency does not necessarily imply correctness.	LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation.
Consistency, in popular usage, means that two or more things do not conflict with one another. This usage extends reasonably well to data values, although a bit of added discipline is desired.	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.