Quality data is important to any organization. Businesses, agencies and other kinds of organizational structures rely on data to be up-to-date, accurate and relevant to the mission of the organization.
Organizations that do not maintain strong quality in their data transactions typically learn that many problems can arise due to poor maintenance of data quality.
Issues such as:
*Bad decision making
*Lack of uniformed decision making
*Error prone records
*Costly errors
As a solution to poor quality data, organizations should strive to develop good database design and data models. These should contain strong security methods built into them and network resources. While these are great first steps, they are not enough to ensure good data quality.
One of the best ways organizations can promote good data quality is to carefully plan integration of the information systems right from the beginning. As a part of this development, decision makers should consider including all user levels to be involved in the process.
The benefit to this is it allows both management and system developers get a full picture of the organization's business requirements and needs. The more complete the development process is, the better off data quality is over the long term. In addition, development should also consider potential expansion and future business growth.
As the organization's members begin the development of an information system(s), these are some of the points that should be considered in the development process:
*Accuracy of taxonomy
*Precisely defined records
*Precision in how data is recorded and the methodology used
*Precise documentation, including updates, changes, additions or deletions
*A well-defined method of transmitting data
After the system is established, the organization should perform data quality audits on a regular basis. In addition, data cleansing should be conducted periodically to allow for better data consistency. Engaging in these activities will increase data integrity and overall quality.
Additionally, employees should be trained to eye over data before final submission into database, and not get careless. Since typos can be easily made, getting into the habit of proofing data entry will only add to better data quality.
Poor data quality, which includes bad or incorrect data will result in errors. An information system database plagued with inaccuracies has a much higher probability poor business decisions being made. Managers and employees who base their decisions on the information in error-laden databases are going to find themselves making decisions on wrong or inaccurate data.
Ultimately poor decisions can result in major financial loss for the organization, including but not limited to a recall of products. Striving hard to create good data quality in an organization is an important goal to work towards. Poor quality can result in problematic issues for the organization, stakeholders, consumers and the general public. It is in everyone's best interests when an organization pays attention to data qualit
SOURCE:
http://www.helium.com/items/1711157-managerial-issues-how-organizations-can-promote-data-quality
Organizations that do not maintain strong quality in their data transactions typically learn that many problems can arise due to poor maintenance of data quality.
Issues such as:
*Bad decision making
*Lack of uniformed decision making
*Error prone records
*Costly errors
As a solution to poor quality data, organizations should strive to develop good database design and data models. These should contain strong security methods built into them and network resources. While these are great first steps, they are not enough to ensure good data quality.
One of the best ways organizations can promote good data quality is to carefully plan integration of the information systems right from the beginning. As a part of this development, decision makers should consider including all user levels to be involved in the process.
The benefit to this is it allows both management and system developers get a full picture of the organization's business requirements and needs. The more complete the development process is, the better off data quality is over the long term. In addition, development should also consider potential expansion and future business growth.
As the organization's members begin the development of an information system(s), these are some of the points that should be considered in the development process:
*Accuracy of taxonomy
*Precisely defined records
*Precision in how data is recorded and the methodology used
*Precise documentation, including updates, changes, additions or deletions
*A well-defined method of transmitting data
After the system is established, the organization should perform data quality audits on a regular basis. In addition, data cleansing should be conducted periodically to allow for better data consistency. Engaging in these activities will increase data integrity and overall quality.
Additionally, employees should be trained to eye over data before final submission into database, and not get careless. Since typos can be easily made, getting into the habit of proofing data entry will only add to better data quality.
Poor data quality, which includes bad or incorrect data will result in errors. An information system database plagued with inaccuracies has a much higher probability poor business decisions being made. Managers and employees who base their decisions on the information in error-laden databases are going to find themselves making decisions on wrong or inaccurate data.
Ultimately poor decisions can result in major financial loss for the organization, including but not limited to a recall of products. Striving hard to create good data quality in an organization is an important goal to work towards. Poor quality can result in problematic issues for the organization, stakeholders, consumers and the general public. It is in everyone's best interests when an organization pays attention to data qualit
SOURCE:
http://www.helium.com/items/1711157-managerial-issues-how-organizations-can-promote-data-quality
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