All companies have difficulties in undertaking comprehensive quality management of the data. Most use only a fraction of the information available in the organization to obtain information that is useful to them to drive the overall performance of their business. At the same time, they usually do not realize the Associated cost of mediocre data, inaccurate or inconsistent.
The significant amount of lost revenue due to the handling of poor quality information is forcing companies to change your data quality management strategy. The trend is to move from occasional data cleansing and optimization to focus it as one comprehensive quality management of the data carried out continuously. It is a procedure of continuous quality improvement, covering all levels of the organization.
Let's look at some of the best practices that can be used.
How to increase the efficiency of comprehensive data quality management
exists Four Powerful Ways to Move Toward a Comprehensive Approach to Data Quality Management. They are as follows:
1. Request or conduct a data quality assessment
The right way to start address the issues of comprehensive quality management of the data It is through the realization of a full analysis of the current situation of your data.
You need to know what kind of information contains errors or inconsistencies. Know if you have duplicate data or fields that are often missing. All of this may not be easy to identify and correct, since the data might already be in the backups, or it could also be data received from external sources such as:
- Providers.
- External applications.
- RRSS, like Facebook and Twitter.
2. Create a data quality firewall
Your organization's data is an asset that contains strategic information and should be treated as such. Like any other company asset, the data contained in the organization's information systems have economic value. The value of data increases and is correlated with the number of people who can make use of it.
If we allow inaccurate data to appear continuously, not only will it be more difficult to obtain valid business ideas for decision-making, but it will also end up corrupting the quality data.
A virtual data quality firewall detects and blocks incorrect data just as it enters our system, Act proactively to prevent poor quality data from contaminating company information sources..
A comprehensive data quality management solution must include a data quality firewall that dynamically identifies invalid or corrupted data.
3. Unify data management and business intelligence
The sheer volume of data flowing through company systems can make it especially difficult to maintain the highest data quality at all times.. The key to success is in identify and prioritize the type and volume of data we want to manage.
Business Intelligence (WITH A) enables institutions to determine which data sets are most likely to be used. These will be, therefore, in which priority should be focused on the quality management plan.
4. Create a working group in charge of managing data quality.
The main objective of creating a data quality working group is to mitigate the risks that can arise in the decision-making process that is carried out when analyzing and treating data of high business value.
Forming this group is essential in a holistic approach such as comprehensive data quality management.and should include both business users and IT department staff. The group will be responsible for determining data policies and standards., ensuring there is a mechanism in place to fix all data-related problems that may arise.
They should also facilitate and enforce the data quality improvement procedures that are implemented and should promote measures to prevent data-related problems before they occur..
Comprehensive quality management Successful data management starts with a strong, well-established data management strategy, based on the selection and implementation of a good data quality solution and the creation of data integrity teams, consisting of a combination of IT staff and business users.
