Usefulness and relevance
| Characteristic Name: | Usefulness and relevance |
| Dimension: | Usability and Interpretability |
| Description: | The data is useful and relevant for the task at hand |
| Granularity: | Information object |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to the lack of usefulness and relevance of data |
| The number of complaints received due to the lack of usefulness and relevance of data |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Define the content of the information object based on the user requirements (as required by the task at hand) and also considering all other compliance requirements so that the information is relevant and legitimate | (1) Customer invoice should contain information for the customer to understand his liability and for the delivery person to understand the point of delivery and the tax department to verify the applicable tax amount. |
| Regularly monitor the changes to the internal operational environment ( business process changes etc) and find out what are the new information requirements emerge due to the changes, and provide for them by amending the information structures | (1) Time stamp became an important attribute for GRNs (goods receipts notes) when Lean manufacturing started as all raw materials are expected to receive by six hours before production (GRN-record, and the time stamp -attribute) |
| Regularly monitor the changes in the external environment find out the new information requirements emerge due to such changes and provide for such data needs | (1) Competitors' rates have become important to price the existing products during the recession period since the traditional costing method does not give a competitive price. |
| Regularly check with knowledge workers to find out how their operations/decisions can be performed better with new data available to them and provide for such data in the information system | (1) An hourly working progress report is useful in identifying the bottlenecks in production lines and balance the lines |
| Monitor and measure the user satisfaction about the information provided | (1) User satisfaction survey |
Validation Metric:
| How mature is the process to maintain usefulness and relevance of data |
These are examples of how the characteristic might occur in a database.
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| 1) The Characteristic in which the Information is the right kind of Information that adds value to the task at hand, such as to perform a process or make a decision.
2) Knowledge Workers have all the Facts they need to perform their processes or make their decisions. |
ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| 1) Can the information process be adapted by the information consumer?
2)Can the information be directly applied? Is it useful? 3) Does the information provision correspond to the user’s needs and habits? |
EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer. |
| Relevance of data refers to the extent to which the data meets the needs of users. Information needs may change and is important that reviews take place to ensure data collected is still relevant for decision makers. | 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. |
| Relevance is the degree to which statistics meet current and potential users’ needs. It refers to whether all statistics that are needed are produced and the extent to which concepts used (definitions, classifications etc.) | LYON, M. 2008. Assessing Data Quality , Monetary and Financial Statistics. Bank of England. http://www.bankofengland.co.uk/ statistics/Documents/ms/articles/art1mar08.pdf. |
| The data includes all of the types of information important for 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. |
| 1) Intrinsic: The extent to which the information is new or informative in the context of a particular activity or community.
2) Relational Contextual:The amount of information contained in an information object. At the content level, it is measured as a ratio of the size of the informative content (measured in word terms that are stemmed and stopped) to the overall size of an information object. At the schema number of elements in the object level it is measured as a ratio of the number of unique elements over the total. 3) The extent to which information is applicable in a given activity. 4) The extent to which the model or schema and content of an information object are expressed by conventional, typified terms and forms according to some general-purpose reference source. |
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. |
| 1) Data are applicable and useful for the task at hand.
2) The quantity or volume of available data is appropriate. 3) Data are of sufficient depth, breath and scope 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. |