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. |
Objectivity
| Characteristic Name: | Objectivity |
| Dimension: | Reliability and Credibility |
| Description: | Data are unbiased and impartial |
| Granularity: | Information object |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to biased and partial data |
| The number of complaints received due to biased or partial data |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Identify all the factors that make a particular data/information biased for the intended use and take preventive actions to eliminate them | (1) A written questionnaire is better than a face to face interviews in getting sensitive personal data |
| Design and execute preventive actions for all possible information distortions (malfunctioning or personal biases) which may cause by information /data collectors | Perform a duel coder approach to code qualitative data. |
| Design and execute preventive actions for all possible information distortions (malfunctioning or personal biases) which may cause by information /data transmitters | (1) After a survey is performed, each participant is contacted individually by a party (other than the person who conducted the survey) and randomly verify if the participants real responses have been marked properly. |
Validation Metric:
| How mature is the process to prevent biased and partial data |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| Consider an inventory database that contains part numbers, warehouse locations, quantity on hand, and other information. However, it does not contain source information (where the parts came from). If a part is supplied by multiple suppliers, once the parts are received and put on the shelf there is no indication of which supplier the parts came from. The information in the database is always accurate and current. For normal inventory transactions and deci- sion making, the database is certainly of high quality. If a supplier reports that one of their shipments contained defective parts, this database is of no help in identifying whether they have any of those parts or not. The database is of poor quality because it does not contain a relevant element of information. Without that information, the database is poor data quality for the intended use. | 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: |
|---|---|
| The degree to which Information is presented without bias, enabling the Knowledge Worker to understand the meaning and significance without misinterpretation. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| Is the information free of distortion, bias, or error? | EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer. |
| 1) Data are unbiased and impartial
2) Objectivity is the extent to which data are unbiased (unprejudiced) and impartial. |
WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33. |