1. accurate information 2. organized information 3. timely information 4. verifiable information 5. accessible information 6. economical information 7. useful information
Data Integrity
There are four key characteristics which separate the data warehouse from other major operational systems:Subject Orientation: Data organized by subjectIntegration: Consistency of defining parametersNon-volatility: Stable data storage mediumTime-variance: Timeliness of data and access terms
Ungrouped data is data that is not grouped in a specific order. Grouped data is a set of data that has unique characteristics in common.
Quantitative data is quantity - how much. Qualitative data is quality - is it good? what is it like?
The five characteristics of high quality information are accuracy, completeness, consistency, uniqueness, and timeliness. The quality of information determines its usefulness.
high profit,good machines ,more labours,quality products
1. accurate information 2. organized information 3. timely information 4. verifiable information 5. accessible information 6. economical information 7. useful information
Field characteristics in a database are used to define the properties and constraints of a specific field, such as data type, length, and validation rules. They help ensure data integrity, accuracy, and consistency within the database by specifying how data should be stored and validated. These characteristics help to maintain the quality and reliability of the data stored in the database.
Elevate Your Marketing with High-Quality Data
That depends a lot on what sort of data you store in those MB, for example low-quality sound, high-quality sound, low-quality video, high-quality video.
data steucture characteristics
Farmers want their cattle to produce high quality meat and a high milk yield
A luxury hotel has the best amenities such as high quality shampoo, lotion and conditioner. One will always find a coffee pot and the bedding will also be high quality.
Data. Usually represented by different electrical characteristics like high/low, on/off. Data coming in is referred to as INPUT, data going out is OUTPUT.
There are a variety of data integration tools such as the garder's new magic quadrant, and other quadrants that focus on tools that provide high quality integration of data.
The second major factor to consider in data management is data quality. This involves ensuring that the data is accurate, complete, consistent, and relevant for its intended use. Poor data quality can lead to errors in analysis and decision-making, so it is crucial to implement processes to maintain high data quality standards.