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. |
Statistical validity
| Characteristic Name: | Statistical validity |
| Dimension: | Validity |
| Description: | Computed data must be statistically valid |
| Granularity: | Information object |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to lack of statistical validity in data |
| The number of complaints received due to lack of statistical validity of data |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Establish the population of interest unambiguously with appropriate justification (maintain documentation) | (1) Both credit customers and cash customers are considered for a survey on customer satisfaction. |
| Establish an appropriate sampling method with appropriate justification | (1) Stratified sampling is used to investigate drug preference of the medical officers |
| Establish statistical validity of samples -avoid over coverage and under coverage (maintain documentation) | (1) Samples are taken from all income levels in a survey on vaccination |
| Maintain consistency of samples in case longitudinal analysis is performed. (Maintain documentation) | (1) Same population is used over the time to collect epidemic data for a longitudinal analysis |
| Ensure that valid statistical methods are used to enable valid inferences about data, valid comparisons of parameters and generalise the findings. | (1) Poisson distribution is used to make inferences since data generating events are occurred in a fixed interval of time and/or space |
| Ensure that the acceptable variations for estimated parameters are established with appropriate justifications | (1) 95% confidence interval is used in estimating the mean value |
| Ensure that appropriate imputation measures are taken to nullify the impact of problems relating to outliers, data collection and data collection procedures and the edit rules are defined and maintained. | (1) Incomplete responses are removed from the final data sample |
Validation Metric:
| How mature is the process to maintain statistical validity of data |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| if a column should contain at least one occurrence of all 50 states, but the column contains only 43 states, then the population is incomplete. | Y. Lee, et al., “Journey to Data Quality”, Massachusetts Institute of Technology, 2006. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| Coherence of data refers to the internal consistency of the data. Coherence can be evaluated by determining if there is coherence between different data items for the same point in time, coherence between the same data items for different points in time or coherence between organisations or internationally. Coherence is promoted through the use of standard data concepts, classifications and target populations. | 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. |
| 1) Accuracy in the general statistical sense denotes the closeness of computations or estimates to the exact or true values.
2) Coherence of statistics is their adequacy to be reliably combined in different ways and for various uses. |
LYON, M. 2008. Assessing Data Quality , Monetary and Financial Statistics. Bank of England. http://www.bankofengland.co.uk/ statistics/Documents/ms/articles/art1mar08.pdf. |