When correcting this problem, la consecución de una databaseA database is an organized set of information that allows you to store, Manage and retrieve data efficiently. Used in various applications, from enterprise systems to online platforms, Databases can be relational or non-relational. Proper design is critical to optimizing performance and ensuring information integrity, thus facilitating informed decision-making in different contexts.... con datos fiables necesita una acción que optimice la información. To face this big data challenge, it is necessary to build an action plan with controls and alerts, avoiding errors such as duplication of records, quickly detect system failures, prevent or minimize human errors through automation.
In summary, it is about debugging and at the same time enriching the data based on the required data, as well as to systematize some guidelines, always with a constant review of their performance. Therefore, the achievement of our objective requires a series of methodologies and phases that carry out a standardization procedure, cleaning and improving data quality.
Exponential increase in information: a challenge for data quality
Within an adequate administration or coordination of the project, it is necessary to create controls that help in its administration, since Data quality initiatives are continuous and controlled improvement projects., especially in the context of Big Data, characterized by a Inefficient administration as a consequence of the exponential increase in information. from very different sources.
The reliability needs processes and techniques that seek to improve the quality of information, and they will be using data quality tools, how the six dimensions of data quality will be covered:
• Integrity: Relevant data must be included.
• Accordance: its format must be standard and readable.
• Consistency: Avoid conflicting information.
• Precision accuracy: Data must be accurate, resist comparison with reference sources.
• Duplication: duplicate data, same or similar.
• Integrity: relevant information must be usable.
Identifying defects in data based on these dimensions is the first step in driving data quality. After, having achieved this, it will be necessary to determine a plan for Apply the appropriate techniques that improve both the data and its administration.
Data quality action plan
Therefore, from a practical point of view, you should pay attention to the six dimensions of the data, but not only that. Additionally, proper tools and user training are required for cost-effective control..
Far from being an overwhelming task, Data quality initiatives simply require a successful focus of efforts within an action plan to successfully address the challenge.. These simple tips will help you create an effective action plan:
• Know what data we want: which implies knowing both which ones we need, for what purpose and which ones do we discard because they do not serve our purpose. This will allow to establish rules and determine objectives to administer the system with respect to format, storage mode, validation, etc.
• Look for data quality issues: study our starting data with a manual verification or an automatic analysis, where we have gaps, redundancies, if required enrich the data or other deficiencies. The key idea is to be clear about the aspects of its relevance (previous point) Y analyze the data in light of that need.
• Automation: automate processes with the right technology provides speed and improves information reliability. Incident control is needed, with double monitoring at the system level and human error, taking into account that the latter will be reduced with automation.
• Training in data quality techniques: pagTo achieve data quality, collaboration of all stakeholders of the organization involved is vital, only then will it be achieved the biggest possible improvement.
Implementing a data quality procedure will provide substantial improvement, but this must be controlled and have continuity. to consolidate results. Once the necessary standards have been achieved, measure data quality over time and keep it up to date. key processes, including a periodic review of initial objectives, it will be indispensable. Let's keep in mind that The data quality life cycle needs constant review, that will be facilitated by the application of clear and well-established procedures.
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