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. |
Data timeliness
| Characteristic Name: | Data timeliness |
| Dimension: | Currency |
| Description: | Data which refers to time should be available for use within an acceptable time relative to its time of creation |
| Granularity: | Record |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to lack of data timeliness |
| The number of complaints received due to lack of data timeliness |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Recognise the activity/event that generates the time sensitive attribute values and specify rules to generate attribute values. | (1)Efficiency of production line 1) Line out quality check which signifies the end of manufacturing of a product in a lean manufacturing line. 2) The number of products which passed the line out quality checks per given time period is the efficiency of the line |
| Specify the valid time period for the values of attribute to be recorded | (1) The growth of the bacteria should be measured after 15 hours of culturing (2) Efficiency should be calculated and recorded once in every 10 minutes starting from the first 10th minute of an hour (six times per hour) |
| Specify the valid time period for the values of attribute to be used | (1) The exchange rate for the day is valid from 8 am to 8am the following day |
Validation Metric:
| How mature is the creation and implementation of the DQ rules to handle data timeliness |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| stable data such as birth dates have volatility equal to 0, as they do not vary at all. Conversely, stock quotes, a kind of frequently changing data, have a high degree of volatility due to the fact that they remain valid for very short time intervals. | C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006. |
| the quotation of a stock remains valid for only a few seconds irrespective of architectural choices | 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. |
| For example, patient census is needed daily to provide sufficient day-to-day operations staffing, such as nursing and food service. How- ever, annual or monthly patient census data are needed for the facilityís strategic planning. | B. Cassidy, et al., “Practice Brief: Data Quality Management Model” in Journal of AHIMA, 1998, 69(6). |
| consider a system where each user must change own password every 6 months. Those passwords without been updated during more than 6 months, are not valid in the system, and can be treated as absolute stale elements | O. Chayka, T. Palpanas, and P. Bouquet, “Defining and Measuring Data-Driven Quality Dimension of Staleness”, Trento: University of Trento, Technical Report # DISI-12-016, 2012. |
| Consider a database containing sales information for a division of a company. This database contains three years’ worth of data. However, the database is slow to become complete at the end of each month. Some units submit their information immediately, whereas others take several days to send in information. There are also a number of corrections and adjustments that flow in. Thus, for a period of time at the end of the accounting period, the content is incomplete. However, all of the data is correct when complete. If this database is to be used to compute sales bonuses that are due on the 15th of the following month, it is of poor data quality even though the data in it is always eventually accurate. The data is not timely enough for the intended use. However, if this database is to be used for historical trend analysis and to make decisions on altering territories, it is of excellent data quality as long as the user knows when all additions and changes are incorporated. Waiting for all of the data to get in is not a problem because its intended use is to make long-term decisions. | 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: |
|---|---|
| A measure of the degree to which data are current and available for use as specified and in the time frame in which they are expected. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| Domain Level: The data element represents the most current information resulting from the output of a business event. Entity Level: The entity represents the most current information resulting from the output of a business event. | 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. |
| The “age” of the data is correct for the Knowledge Worker’s purpose . Purposes such as inventory control for Just-in-Time Inventory require the most current data. Comparing sales trends for last period to period one-year ago requires sales data from respective periods. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| Determines the extent to which data is sufficiently up-to-date for the task at hand. For example, hats, mittens, and scarves are in stock by November. | G. GATLING, C. B., R. CHAMPLIN, H. STEFANI, G. WEIGEL 2007. Enterprise Information Management with SAP, Boston, Galileo Press Inc. |
| Timeliness of data refers to the extent to which data is collected within a reasonable time period from the activity or event and is available within a reasonable timeframe to be used for whatever purpose it is intended. Data should be made available at whatever frequency and within whatever timeframe is needed to support decision making. | 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. |
| The currency (age) of the data is appropriate to its use. | 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. |
| Timeliness can be defined in terms of currency (how recent data are). | SCANNAPIECO, M. & CATARCI, T. 2002. Data quality under a computer science perspective. Archivi & Computer, 2, 1-15. |
| 1) The age of an information object.
2) The amount of time the information remains valid in the context of a particular activity. |
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. |
| The age of the data is appropriate for the task at hand. | WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33. |