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Data Tools: Defining the Characteristics of Quality Data
When can data be considered ‘high quality’? Joseph Juran, an early promoter data-quality, defined “data to be of high quality if it fits the intended uses in operations, decision making and planning.” Data needs to consistently and accurately represent real world objects to which the data refers.
Thomas C Redman, ‘the data doc’ at Navesink Consulting Group, identified the following characteristics of high quality data:
| Free of defects | Relevant |
| accessible | comprehensive |
| accurate | easy to read |
| timely | easy to interpret |
| complete | consistent with other sources |
Data quality is becoming increasingly important as the number of big data and business analytics projects grows. Data quality also plays an important role in the area of data governance, application modernization projects and master data management.
In the area of data tools, Gartner identifies six areas in which data quality management tools can provide value:
- Parsing and standardization
- Generalized cleansing
- Matching
- Profiling
- Monitoring
- Enrichment
A recent report by Gartner on vendors in the Data Quality Management (DQM) tools sector found that Business Objects’ Firstlogic, DataFlux, IBM and Trillium are the market leaders.
