Data freshness
| Characteristic Name: | Data freshness |
| Dimension: | Currency |
| Description: | Data which is subjected to changes over the time should be fresh and up-to-date with respect to its intended use. |
| Granularity: | Element |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to lack of data freshness |
| The number of complaints received due to lack of data freshness |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Identify the natural factors which creates a particular data obsolete | (1) A seasonal change may impact the customer's food preferences. (2) Customers who are students may change their addresses frequently. |
| Considering the above factors plan for data refreshing activities by specify the frequency of refreshing the data elements and adhere to the plan. | (1) Customer contact information should be refreshed annually. |
| Identify the master data that may change over the time but may be used in longitudinal analysis. | (1) Name of customer in 2001 is ABC (PLC) Ltd, after a merger in 2006 its name is XYZ (PLC). This customer is an ongoing customer in the customer master file |
| For such master data maintain longitudinal versions with time a stamp in such a way they can be linked in longitudinal analysis | (1) 2001-2005: ABC (PLC) (2) 2006-20012: XYZ (PLC) |
Validation Metric:
| How mature is the process for ensuring data freshness |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| 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. |
| currency indicates how stale is the account balance presented to the user with respect to the real balance at the bank database. | V. Peralta, “Data Freshness and Data Accuracy: A State of The Art”, Instituto de Computacion, Facultad de Ingenieria, Universidad de la Republica, Uruguay, Tech. Rep. TR0613, 2006. |
| 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 rate of negative change to the data. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| Is the information upto-date and not obsolete? | EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer. |
| Data is accurate if it is up to date – anti example: “Current president of the USA: Bill Clinton”. | KIMBALL, R. & CASERTA, J. 2004. The data warehouse ETL toolkit: practical techniques for extracting. Cleaning, Conforming, and Delivering, Digitized Format, originally published. |
| Currency refers to the degree to which information is current with the world that it models. Currency can measure how up to date information is and whether is it correct despite possible time-related changes. Timeliness refers to the time. | LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub. |
| Currency refers to the degree to which information is current with the world that it models. Currency can measure how “up-to-date” information is, and whether it is correct despite possible time-related changes. Data currency may be measured as a function of the expected frequency rate at which different data elements are expected to be refreshed, as well as verifying that the data is up to date. For example, one might assert that the contact information for each customer must be current, indicating a requirement to maintain the most recent values associated with the individual’s contact data. | LOSHIN, D. 2006. Monitoring Data quality Performance using Data Quality Metrics. Informatica Corporation. |
| A datum value is up-to-date if it is correct in spite of a possible discrepancy caused by time related change to the correct values; a datum is outdate at time t if it is incorrect at t but was correct at some time preceding t. currency refers to a degree to which a datum in question is up-to-date. | REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc. |
Data volume
| Characteristic Name: | Data volume |
| Dimension: | Completeness |
| Description: | The volume of data is neither deficient nor overwhelming to perform an intended task |
| Granularity: | Information object |
| Implementation Type: | Process-based approach |
| Characteristic Type: | Usage |
Verification Metric:
| The number of tasks failed or under performed due to not meeting the right volume of data |
| The number of complaints received due to volume related issues |
GuidelinesExamplesDefinitons
The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation
| Guidelines: | Scenario: |
|---|---|
| Define the scope of data in terms of organisational coverage to perform a business activity | (1) At least 70% of the production units should submit data to calculate total production efficiency of the company |
| Define the scope of data in terms of activities relates to any business task | (1) Pages with more than thousand hits per day and above are considered for the analysis |
| Define the scope of data in terms of the population of data which is under concern | (1) At least 10% of the population of white blood cells in the culture should be collected as samples to calculate its growth |
| Define an appropriate amount of records in terms of lower limit and upper limit for any task | (1) At least six responses should be available to evaluate a tutor's skills and competency. |
Validation Metric:
| How mature is the process of defining and maintaining appropriate data volumes of data |
These are examples of how the characteristic might occur in a database.
| Example: | Source: |
|---|---|
| At the end of the first week of the Autumn term, data analysis was performed on the ‘First Emergency Contact Telephone Number’ data item in the Contact table. There are 300 students in the school and 294 out of a potential 300 records were populated, therefore 294/300 x 100 = 98% completeness has been achieved for this data item in the Contact table. | 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 availability and comprehensiveness of data compared to the total data universe or population of interest. | D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008. |
| Is the scope of information adequate? (not too much nor too little). | EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer. |
| Degree of presence of data in a given collection. | SCANNAPIECO, M. & CATARCI, T. 2002. Data quality under a computer science perspective. Archivi & Computer, 2, 1-15. |
| The quantity or volume of available data is appropriate | WANG, R. Y. & STRONG, D. M. 1996. Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 5-33. |