Completeness of records
| Characteristic Name: | Completeness of records |
| Dimension: | Completeness |
| Description: | Every real world entity instance, that is relevant for the organization can be found in the data |
| Granularity: | Record |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to missing records |
| The number of complaints received due to missing records |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Implement a process level validation mechanism to avoid occurrence of missing records | (1) A buyer must record/verify an expense or asset in accordance with accepting/receiving any purchased items. (2)New application are stored in a temporary cabinet after entering into the system and they will be transferred to the file cabinet at the end of every week after the property manager cross check them with the system |
| Execute database commits upon transaction sequences in application programs and make sure all the transactions in the sequence successfully commit and generate the required records at the end of the sequence. | (1) In generating the MRP, the database operations will not be committed unless all materials in BOM is successfully executed for MRP |
| When distributed databases are used or online data collection devices are used, ensure the synchronisation/replication of records happen successfully without distortions and omissions. | (1) EFTPOS transactions are replicated with bank database and create the new balance B/F in the account |
| Implement periodic audit process for critical tangible objects that are recorded as data in database | (1) Annual audit for tangible assets in the organisation |
| Implement a validation mechanism in data transfers considering the business rules to monitor and ensure all records relevant to a event/transaction is transferred successfully. | (1) Rules to verify the number of records in the source file and destination file (2) All records relevant to a customer trip is transferred to the central database from online data stores |
| Maintain error logs for system transactions and regularly monitor them and perform relevant forensic activities to find missing records. | (1) A failed sales order creation |
Validation Metric:
| How mature is the process to prevent missing records |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| if Dept is a relation representing the employees of a given department, and one specific employee of the department is not represented as a tuple of Dept, then the tuple corresponding to the missing employee is in ref(Dept),and ref(Dept) differs from Dept in exactly that tuple. | C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006. |
| 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 database should contain all customers in North and South America, but it is known that the database reflects only a portion of the company’s customers. Coverage in this example is the percent- age of customers actually captured in the database compared to the population of all customers that should be in it. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| A record exists for every Real-World Object or Event the Enterprise needs to know about. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| Completeness of data refers to the extent to which the data collected matches the data set that was developed to describe a specific entity. Monitoring for incomplete lists of eligible records or missing data items will identify data quality problems. | 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. |
| Quality of having all data that existed in the possession of the sender at time the data message was created. | ISO 2012. ISO 8000-2 Data Quality-Part 2-Vocabulary. ISO. |
| Data is complete if no piece of information is missing – anti-example: "The Beatles were John Lennon, George Harrison and Ringo Starr" | KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published. |
| Every real-world phenomenon is represented. | 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. |
Meta-data compliance
| Characteristic Name: | Meta-data compliance |
| Dimension: | Validity |
| Description: | Data should comply with its metadata |
| Granularity: | Element |
| Implementation Type: | Rule-based approach |
| Characteristic Type: | Declarative |
Verification Metric:
| The number of meta-data violations 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: |
|---|---|
| Domains should be specified by considering all possible value patterns, cases and usage needs which is applicable for a data attribute. | (1) Eg: It is easy to maintain the order number as a numeric value since it can be easily incremented (10000, 10001, and 10002). But it can be also defined as alphanumeric in order to distinguish special cases (10000R is a return order of sales order 10000) |
| Maintain valid values/value ranges/Value lists for attributes. | (1) Gender can be M or F (2) Country is taken from the existing list of countries (3) Birth date cannot be a future value. (4) Salary range for level 4 employees must be between $40000-60000 |
| Usage of number ranges for critical data elements | (1) Sales orders 10000001 to 1999999 (2) Purchase orders 50000001 to 59999999 |
| Maintain the possible synonyms and abbreviations which could be accepted as valid values | (1) Post Box , PO BOX, BOX etc. |
| Explicitly mention what values, characters are not permitted in the attribute | (1) User Name can contain only A-Z (2) No blank spaces are allowed for credit card number |
| Explicitly mention the minimum /maximum number of characters, or any other requirements such as case sensitivity, that an attribute value should meet | (1) Password should contain minimum of 8 characters including one numeric and one capital |
| Maintain values based on specific formats as defined by the stakeholders, standards, best practices or agreements. | (1) Time should be in 24 hour clock (2) Date should be in DD/MM/YYYY |
| Appropriate measurement scale should be maintained against quantities and volumes | (1) Currency for Price values Kg/g/mg for weights litres for volumes (2) Data Dictionary , Data catalog |
| Documentation for Meta-Data is available online for the users | (1) Data Dictionary , Data catalog |
Validation Metric:
| How mature is the creation and implementation of the DQ rules to define meta-data |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| 1) A new year 9 teacher, Sally Hearn (without a middle name) is appointed therefore there are only two initials. A decision must be made as to how to represent two initials or the rule will fail and the database will reject the class identifier of “SH09”. It is decided that an additional character “Z” will be added to pad the letters to 3: “SZH09”, however this could break the accuracy rule. A better solution would be to amend the database to accept 2 or 3 initials and 1 or 2 numbers.
2) The age at entry to a UK primary & junior school is captured on the form for school applications. This is entered into a database and checked that it is between 4 and 11. If it were captured on the form as 14 or N/A it would be rejected as invalid. |
N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013. |
| the answer to the query “What is Mr. Wat- son% telephone number?” can bl: validated against the format for telephone numbers. Additionally, Wat- son’s address might be used to vallidate the area code and exchange | M. Brodie, “Data Quality in Information Systems”, North-Holland Publishing Company Information and Management 3, 1980, pp. 245-258. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| Determines the extent to which data conforms to a specified format. For example, the order date must be in the format YYYY/MM/DD. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| 1) Data element passes all edits for acceptability and is free from variation and contradiction based on the condition of another data element (a valid value combination).
2) The metadata of the data element clearly states or defines the purpose of the data element, or the values used in the data element can be understood by metadata or data inspection. The metadata of the entity clearly states or defines the purpose of the entity and its required attributes/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. |
| 1) A data value is a Valid Value or within a specified range of valid values for this data element.
2) Data values are consistent with the Attribute (Fact) definition. |
ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| This dimension refers to whether instances of data are represented in a format that is consistent with the domain of values and with other similar attribute values. For example, the display of time in a non-military (12-hour) format may be confusing if all other instances of times in the system are displayed in the 24-hour military format. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
| This dimension refers to whether instances of data are either store, exchanged, or presented in a format that is consistent with the domain of values, as well as consistent with other similar attribute values. Each column has numerous metadata attributes associated with it: its data type, precision, format patterns, use of a predefined enumeration of values, domain ranges, underlying storage formats, etc. | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
| Appropriate metadata is available to define, constrain, and document data. | 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. |
| Representation consistency refers to whether physical instances of data are in record with their formats. For example, an EMPLOYEE’s salary cannot be represented “$AXT,” as there is (or should be) no such element in S. One would often like to know whether a physical instance is the proper representation for the intended (correct) value. But in practice this is rarely possible, as the intended value is conceptual and not known. So one is left with the issue of whether the representation conflicts with S. | REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc. |