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. |
Standards and regulatory compliance
| Characteristic Name: | Standards and regulatory compliance |
| Dimension: | Validity |
| Description: | All data processing activities should comply with the policies, procedures, standards, industry benchmark practices and all regulatory requirements that the organization is bound by |
| Granularity: | Information object |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due non adherence of standards and regulations |
| The number of complaints received due to non adherence to standards and regulations |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Identify the policies, procedures, standards, benchmark practices and any regulatory requirements that an Information object is bound by | (1) Each person's compensation criteria must be determined in accordance with the Annuities Based on Retired or Retainer Pay law. |
| Ensure that all data processing activities are well defined and documented based on the policies, procedures, standards, benchmarks and regulatory requirements. | (1) Process of making a damage estimate is well defined based on industry benchmarks |
| Ensure that the application programs cater for standards and regulatory compliance | (1) A software program to make damage estimates which includes all benchmark data |
| Regularly monitor the data processing activities and identify the problems and inefficiencies so that the corrective and preventive actions can be taken. | (1) Frequent delays in time sheet approvals results in delayed payments |
| Signs should be standardised and universally used | (1) In the line efficiency report, low efficiency lines are indicated using a RED light while a green light indicates high efficiency |
| Relevant standard, procedures, policies and regulations should be communicated to the users effectively | (1) Providing a guidelines for signs |
| Ensure that proper conversion tables are maintained and used in converting attribute vales to different measurement bases. | (1) Metric conversion tables are used to convert lbs to kgs. |
Validation Metric:
| How mature is the process maintain the adherence to standards and regulations |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| 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 Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| A measure of the existence, completeness, quality, and documentation of data standards, data models, business rules, metadata, and reference data. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| The data element has a commonly agreed upon enterprise business definition and calculations. | 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. |
| SIGNS AND OTHER Information-Bearing Mechanisms like Traffic Signals should be standardized and universally used across the broadest audience possible. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| Validity of data refers to data that has been collected in accordance with any rules or definitions that are applicable for that data. This will enable benchmarking between organisations and over time. | 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. |