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. |
Business rules compliance
| Characteristic Name: | Business rules compliance |
| Dimension: | Validity |
| Description: | Data should comply with business rules |
| 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: |
|---|---|
| Identify data related business rules separately (business rules that determines the value of data elements and business rules that get executed depending on the values of data elements) and organise them into a separate executable data rules engine | (1) The system maintains price routines to handle price related data Element :Discount rate A price routine (procedure) can be maintained to calculate the discount rate considering the rules R1:All registered customers get a discount of 6% |
| Implement a stewardship structure for business rules (parallel to stewardship structure for data) and manage the changes to the rules properly | (1) Sales director is responsible for discounts and his approval is needed to change a discount rate. Only the sales manager can change the rules related to discounts. |
| Maintain an error log to identify the problems resulted in the data rules repository where the problematic data records can be identified precisely | (1) Rules engines |
| Continuously monitor the root causes for the errors recorded in the log and take preventive actions by amending the rules, fixing the technical defects in the system etc. | (1) Some trip data is missing for a particular journey in the go card system and as a result an unacceptable journey duration was resulted. New rules were implemented to process such data using a different criteria |
Validation Metric:
| How mature is the creation and implementation of the data related business rules |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| One common example in education is the student school of record. While most students do not change schools during an academic year, many do, especially in urban settings. Thus, the school at which students are tested may not be the school at which they received most of their instruction. Because school-level student achievement measures become increasingly invalid as the number of mobile students increases, many districts will hold schools accountable only for those students who were enrolled for a full academic year. In this case, student achievement measures for a given school lose validity as the percentage of mobile students increases. | J. G. Watson, S. B. Kraemer, and C. A. Thorn, “Data Quality Essentials. Guide to Implementation: Resources for Applied Practice”, August 2009. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| Determines the extent to which data is not missing important relationship linkages. For example, the launch date for a new product must be valid and must be the first week of any quarter, since all new products are launched in the first week of each quarter. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| 1) Data values conform to the Specified Business Rules.
2) A derived or calculated data value is Produced Correctly according to a specified Calculation Formula or set of Derivation Rules. |
ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |