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Data Quality and Data Governance: Drivers of Business Success

Updated: Sep 6, 2022

Increasingly, companies need to make use of data in order to take their business to the next level. Having a lot of data is important, but only by establishing a good Data Quality strategy, they will be able to make decisions that will lead to business success. In this article we tell you what data quality and data governance are and how to establish them in your business in a simple way.

Roser Avilés, head of Data Governance at Aqtiva Data Technologies S.L.

July 21, 2022

Black & White city with Aqtiva's logo

Technology is evolving by leaps and bounds and companies, which have been forced to renew themselves or die, have found in data the fuel they need to drive their business and therefore their digital economy.

More and more companies are relying on data to perform their daily tasks and, as a result, the amount of data handled on a daily basis is growing at an exorbitant rate. So much so that data is considered the oil of the 21st century and, although it seems that it has been with us all our lives, it is in recent years that 90% of the data that exists today has been generated.


Why is data so important? Companies exploit this information with the aim of knowing their customers better and taking their customer experience to the next level, creating successful campaigns, understanding and monitoring their business, etc. Therefore, it is a very valuable asset for these organizations, which, day after day, strive to have more and more data. However, what good is all this massive information if we cannot ensure its quality?

While it is true that organizations are trying to improve the exploitation of their data through the use of new technologies, which bring additional value to their business and place data as the main strategic asset (Data Governance), these efforts are of no use if the data they handle is not reliable. In fact, just like oil, if data is not handled correctly, it can be harmful to companies. The fact is that companies can only extract real value from the data exploited if it is reliable and of optimum quality. Only then will it be useful for decision making.

With Data Governance, companies can efficiently manage their data and increase its value. In this way, they speed up the company's decision-making and improve its overall performance. In this methodology, one of the main axes is quality, since it is what helps to ensure the security and usability of the data handled in the decision-making process.


To fully address this issue, an analysis must be performed for each of the 6 fundamental dimensions of data quality: Completeness, Accuracy, Integrity, Conformity, Consistency and Uniqueness.

6 fundamental dimensions of data quality: Completeness, Accuracy, Integrity, Conformity, Consistency and Uniqueness.

Data Quality depends directly on the data management strategy implemented in the organization, which, as mentioned above, is the responsibility of Data Governance, which, to be effective, must set data measurement indicators, establish who will be responsible for this data, define which policies, processes and governance bodies will be established and involved transversally in the organization, as well as ensure continuous improvement.


For this to be possible, it is necessary to start from a methodological basis based on three main pillars: Analysis, Definition and Planning.

The Analysis consists in prospecting the current state of the company's data, to determine the impact of erroneous data on the company's decision making, as well as the efforts to be made to mitigate it, either manually or by the organization's team of developers.

The Definition consists of establishing the basis for the quality plan to be adopted by the organization to work on the quality of its data.

Planning establishes a long-term roadmap, in line with the organization's plans, that allows the different business units to visualize and understand the importance of data quality.

Methodological basis of Data Quality based on three main pillars: Analysis, Definition and Planning.


Undoubtedly, the greatest benefit of having good data quality in our business is the competitive advantage it brings. Handling reliable and secure data allows companies to quickly overtake any competitor and to position themselves first in the race for business success.

Implementing a good Data Quality strategy may seem like a big challenge that scares even large corporations. But today, there are solutions that make this process easier. Aqtiva, for example, is a solution based on Big Data, Machine Learning and Predictive Analytics that, in a very simple way, ensures data quality from the start, right at the moment of ingestion and in real time. It is designed so that it can be used by anyone in the company, even the non-technical staff of the business.

Difficulty is no longer an excuse to avoid establishing a good Data Quality strategy in any business. We live in times in which we need to make use of data in order to evolve, but only if we ensure its quality will we be able to make a difference.


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