Data quality is the quantitative and qualitative state in which your data is used for its intended purposes. This may revolve around operations and processes, decision making, and preparation.
Due to its critical nature in both day to day processes and ad-hoc tasks, management and employees must understand the factors that affect their data quality. If these factors are not considered and managed correctly, this could have a negative consequence on your valuable output.
- Personal Context
A study detailed the effect of the rate of human error on a variety of task– complicated non-routine tasks gathered a 1 in 10, routine tasks that need care resulted in a 1 in 100 error, simple everyday tasks were struck a 1 in 1,000, and the most straightforward possible task has resulted in 1 in 10,000 failure.
These factors take into consideration the personal characteristics of the data collector, which are (1) age, (2) relevance, and (3) motivation for their collection of data. Additionally, the collector’s ability to comprehend the processes of complicated machinery and equipment which is directly correlated to the competence of the collector. These characteristics of the data collector affect the data collection process and the data quality outcome.
- Organizational context
Research suggests that 50% of the problems experienced in a firm’s knowledge management processes are due to human resources and cultural factors, with only 25% being related to structural and technological fields.
Firms in themselves are groups of people who regularly share information and knowledge. Social capital in a firm is dependent on these knowledge management processes, which builds communities of routines. Such when a firm takes a unified approach to knowledge management will impact the collector’s performance during the data collecting process.
- Physical Context
Physical context is the impact of the physical aspects of the data collection process, which are the availability of equipment and technologies, cost associated with data collection, and the reliability of the systems integration in terms of data migration. These factors may impress on different points of the data collection process.
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