Accuracy to reference source
| Characteristic Name: | Accuracy to reference source |
| Dimension: | Accuracy |
| Description: | Data should agree with an identified source |
| Granularity: | Element |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to lack of accuracy to reference sources |
| The number of complaints received due to lack of accuracy to reference sources |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Establish the source for a data attribute and maintain facilities to access the correct source. | (1) Actual Cost of raw material is taken from Supplier invoices and not from quotation. (2) Interest rates are taken from daily central bank statistics which is available in the finance system online. |
| Establish the data capturing points in the business process without leading to any ambiguity and enforce process level validation mechanisms to ensure the process is being followed. | (1) Personal drug utilisation data is captured at POS units at pharmacies and ALL pharmacies in the country are connected to a central system (All pharmacy data is considered). (2) In a barcode scanning system in a production system, finished products cannot be scanned into quality checked products (Finished, Quality checked are the two data capturing points here) |
| Implement effective techniques and efficient technological solutions (devices) in collecting data which minimise data errors and omissions in data capturing. | (1) Barcode scanning is used to enter sales of products. (2) Invoices are scanned into the system and price is automatically recognised. (3) Standard forms are used to collect patient data. |
| If data is collected and transferred batch wise, establish the frequencies of data transfers/uploads considering the nature of the data and business needs. | (1) All drug utilisation data collected in the pharmacies are transferred to the central system at the end of every month. (2) Production efficiency data is transferred to monitoring systems every 30 minutes |
| Implement an effective and efficient data transferring technology which do not cause distortions or omissions to data | (1) Data migration tools |
| Define and implement appropriate input validation rules to notify the data collector/operator about the erroneous values being entered, avoid erroneous values being entered into database or erroneous values are flagged for clear identification | (1) Telephone number does not accept non numeric characters |
| Implement flexible data capturing interfaces to accommodate important but out of the way data. | (1) A field exists to record special comments in a goods receipts note (GRN) |
| Implement and enforce standardised data capturing procedures/ best practices through the system in collecting data. | (1) Standard wait times are used in taking blood samples of a patient. (e.g.: one hour after meal) |
| Establish mitigation mechanisms to handle measurement errors and ensure that acceptable error tolerance levels are established | (1) calibrate the equipments on a routine basis |
| Identify barriers for data collection or barriers for data providers and take appropriate actions to remove them | (1) Maintain a log file of response failures of a web based survey and then eliminate the root causes. |
| Identify the practices which encourage data providers | (1) Reward survey participants |
| Conduct regular training programs for data capturing/entering staff and educate them on possible data capturing problems and how to overcome data entry errors depending on the context | (1) Do not restart the Scanner when it is hung up while scanning (2) Repeat a telephone number in a different pattern to validate it from the source e.g. : 045 220 371 9 , in validating repeat it as 04 52 20 37 19 |
Validation Metric:
| How mature is the process for ensuring accuracy for reference sources |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| In this scenario, the parent, a US Citizen, applying to a European school completes the Date of Birth (D.O.B) on the application form in the US date format, MM/DD/YYYY rather than the European DD/MM/YYYY format, causing the representation of days and months to be reversed. | N. Askham, et al., “The Six Primary Dimensions for Data Quality Assessment: Defining Data Quality Dimensions”, DAMA UK Working Group, 2013. |
| , let us consider two
databases, say A and B, that contain the same data. If at time t a user updates data in database A and another user reads the same data from database B at time t' (t < t' ), the latter will read incorrect data. If t and f are included within the time interval between two subsequent data realignments |
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. |
| Consider an air traffic control center which receives data from several controller stations. To regulate air traffic, the traffic control center has to cope with uncertain data.Thus, the decision process must balance the delaying receiving more accurate data of airplane positions and the critical period of time in which an“effective” decision must be made to regulate traffic; | B. Pernici, “Advanced Information Systems Engineering” in proc. The 22nd International Conference, CAiSE, Hammamet, Tunisia, June 2010. |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| A measure of the correctness of the content of the data (which requires an authoritative source of reference to be identified and accessible). | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| The data agrees with an original, corroborative source record of data, such as a notarized birth certificate, document, or unaltered electronic data received from a party outside the control of the organization that is demonstrated to be a reliable source. | ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing. |
| 1) Accuracy of data refers to how closely the data correctly captures what it was designed to capture. Verification of accuracy involves comparing the collected data to an external reference source that is known to be valid. Capturing data as close as possible to the point of activity contributes to accuracy. The need for accuracy must be balanced with the importance of the decisions that will be made based on the data and the cost and effort associated with data collection. If data accuracy is compromised in any way then this information should be made known to the data users.
2) Reliability of data refers to the extent to which data is collected consistently over time and by different organisations either manually or electronically. |
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. |
| Data accuracy refers to the degree with which data values agree with an identified source of correct information. There are different sources of correct information: database of record, a similar, corroborative set of data values from another table, dynamically computed values, the result of a manual workflow, or irate customers. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
| Data accuracy refers to the degree with which data correctly represents the “real-life” objects they are intended to model. In many cases, accuracy is measured by how the values agree with an identified source of correct information (such as reference data). There are different sources of correct information: a database of record, a similar corroborative set of data values from another table, dynamically computed values, or perhaps the result of a manual process. | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
| Accuracy of datum |
REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc. |
| Degree of correctness of a value when comparing with a reference one | 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 extent to which data are correct reliable and certified free of error. | WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33. |
Continuity of data access
| Characteristic Name: | Continuity of data access |
| Dimension: | Availability and Accessability |
| Description: | The technology infrastructure should not prohibit the speed and continuity of access to the data for the users |
| Granularity: | Information object |
| Implementation Type: | Process-bases approacd |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to the lack of continuity in data access |
| The number of complaints received due to lack of continuity in data access |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Convenient and efficient platform should be made available to access data depending on the task at hand | (1) For a sales person, a web based interface run on a smart device is more suitable to quickly access data |
| Speed of the data retrieval should be acceptable for users working pace | (1) For an online customer care executive, speedy retrieval of information is necessary since the customer cannot be kept waiting (2) With the growth of the database reports become slower (Anti example) |
| Continuous and unobstructed connectivity should be ensured for data retrievals | (1) Connection lost while accessing reports (Anti example) |
| Proper concurrency control has been implemented | (1) Controlling access to data by locks |
| Technological changes in the infrastructure/system should be handled in such a way that they should not make data inaccessible | (1) New version of the software does not provide access to " X out orders" since the new version does not allow the function "X out" |
Validation Metric:
| How mature is the process of maintaining an infrastructure for data access |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| 1) For example, recording the age and race in medical records may be appropriate.
However, it may be illegal to collect this information in human resources departments. 2) For example, the best and easiest method to obtain demographic information may be to obtain it from an existing system. Another method may be to assign data collection by the expertise of each team member. For example, the admission staff collects demographic data, the nursing staff collects symptoms, and the HIM staff assigns codes. Team members should be assigned accordingly. |
B. Cassidy, et al., “Practice Brief: Data Quality Management Model” in Journal of AHIMA, 1998, 69(6). |
The Definitions are examples of the characteristic that appear in the sources provided.
| Definition: | Source: |
|---|---|
| 1) Is there a continuous and unobstructed way to get to the information?
2) Can the infrastructure match the user’s working pace? |
EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer. |
| Data is easy and quick to retrieve. | 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) availability of a data source or a system.
2) Accessibility expresses how much data are available or quickly retrievable. 3) The frequency of failures of a system, its fault tolerance. |
SCANNAPIECO, M. & CATARCI, T. 2002. Data quality under a computer science perspective. Archivi & Computer, 2, 1-15. |