In search of data integrity

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Here you will learn about the guarantees implicit in the concept of data integrity.
Data integrity refers to the accuracy and consistency (validity) of data stored in a database, data warehouse or similar, throughout its entire life cycle . It can be said that when we have data integrity, this data has a complete structure and all the characteristics that define it are correct.

Often, the principles of business data management assume the quality of the data and its suitability when making decisions based on its processing. However, far from acting correctly, this path speculates on the reliability of the data , which may not be as healthy and suitable as one thinks, to assume risks from strategic approaches in favor of the business.



Data Integrity and BI: The Strategic Value of Integration


Data integrity as a way to ensure the success of Business Intelligence
Today’s digital world has led to an explosion of data like never before, requiring Business Intelligence solutions to evolve. Many innovative companies rely on Business Intelligence to instantly assimilate, transform and fantuan database this endless flow of information to make perfectly timed business decisions .

But successfully managing a Business Intelligence solution to ensure your business thrives and maintains a competitive advantage in the future can be a daunting task.

A key component is data integrity and compliance with standards. The Business Intelligence solution must ensure data integrity and compliance. Failure to do so could result in multiple levels of cost.

This is why the Business Intelligence paradigm seeks to establish a clearly differentiated process in the maturation of existing data that involves:

Transform raw data into relevant and timely information.

Translate information into strategic and operational knowledge.

Exploiting this knowledge to understand where value exists within the business so that available capacity and resources can be focused.

Today, businesses remain standing and are highly competitive thanks to their correct decisions, but they also fail due to those judgments that are based solely on instinctive conclusions soaked in subjectivity , which are far from a logical use of the high implicit potential contained in their data.

Faced with imminent growth, both in the form and dynamics of today's global market, companies will have to fine-tune their strategies to process ever-increasing amounts of data that will come in different forms and from very diverse sources. All of this while ensuring data quality, which must be considered the basis of any process.