Most companies understand the value of determining and institutionalizing a data analysis plan that helps improve the quality of the organization's data. Despite this, some companies don't include this in their budgets and are limited to making gradual investments in things like data cleansing and proofreading.. And this can cause problems.
The absence of comprehensive data quality control and management processes leads to repeated efforts and higher costs, or even worse, makes it impossible to deliver consistent information to an organization's community of business users.
What is needed is a practical approach to aligning the various data quality activities with each other., creating a data analysis plan organized that addresses the challenges of ensuring and maintaining high levels of quality.
next to need to attract internal sponsors who support the project, and the development of arguments needed to help investments In data quality, Let's see a list of five tasks and procedures that are critical to effective data quality management and that together make up an effective data analysis plan for improving data quality..
1. Document data quality requirements and establish rules for measuring quality.
In most cases, data quality levels are related to the usefulness or suitability of the information for the purposes needed by administrators.
Start with requirements collection: engage these decision makers to understand your business goals and ask them what their expectations are for data usability.
This information, combined with shared experiences on the business impact of data quality issues, can be translated into rules that serve to measure key dimensions of quality, such as consistency of data formats across data formats. different systems, data integrity, etc.
As part of this procedure, a central system should be created to document the requirements and associated rules that support the development of data validation mechanisms.
2. Browse new data to create a quality baseline
A repeatable procedure for statistical evaluation of data quality helps us increase the set of rules for measuring quality, checking the source systems to locate possible anomalies in the creation of new data.
Statistical and data profiling tools can scan values, columns and relationships in and between datasets, using frequency and association analysis to examine values, data formats and completeness, and identify outliers that may indicate errors.
At the same time, profiling tools can provide information to governance and data quality managers on aspects such as data types, the structure of the relational database and the relationships between primary and foreign keys in the databases. data. Results can be shared with managers to assist in the development of data quality validation rules that are included in the data analysis plan.
3. Implement semantic metadata management processes.
As the number and variety of data sources increases, increases the need to limit the risk of end users in different parts of an organization misinterpreting the meaning of common business terms and other data concepts.
There is a need to centralize the management of business-relevant metadata and engage data management executives and professionals to collaborate in setting corporate rules in a way that reduces situations where inconsistent interpretations lead to data usage issues.
4. Verify the validity of the data continuously
Automated services must be developed that validate data records against the quality standards that have been defined in our data analysis plan.
A strategic implementation enables standards and validation mechanisms to be shared through applications and deployments at various points in an organization's information flow, so that continuous data inspection and quality measurement can be carried out.
Results can be fed into a range of information systems, as an example, direct alerts and notifications sent to data stewards, to address high-priority data anomalies and defects, and data quality dashboards and dashboards with metrics added for a wider audience.
5. Stay on top of data quality issues
The data analysis plan should include the development of a platform for registration, monitoring and management of data quality incidents. Assessing compliance with data quality standards will not lead to improvements unless there are standard processes in place to examine and erase the root causes of data errors.. An incident management system can automate processes such as reporting and prioritizing data quality issues, alert stakeholders, assign data quality improvement tasks and track the progress of efforts. correction.
