Data quality: practices for smart management

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It is no longer enough to simply collect large amounts of information to achieve successful decision-making. Today, data quality is central to having a positive impact on the business.

Importance of data quality
The market and the business world are changing. Today, data has become an organization's most valuable asset . And more and more companies understand that decisions based on erroneous or partial information can have a detrimental impact on the business.

That is why the premise of digital evolution must be accompanied by reliable data. That is, data that represents the reality of what is happening in the company, consumers and the market.

From this introduction we can conclude that afghanistan phone number lead data quality is increasingly relevant and worrying in achieving business success.

In fact, a report from the MIT Sloan School of Management in Cambridge mentions that a significant portion of the companies surveyed are concerned about this issue and that incorrect data can cost up to 15-25% of total revenue.

The reality is that today, many organizations face countless challenges, from an exponentially growing volume of data, varying sources, to new data types and structures that appear every day.

Without high-quality data, the dashboards and information analysis that enable decision making will be incomplete, outdated or simply incorrect.

Furthermore, the cost of doing nothing increases over time and the level of data waste is maximized.

How to effectively advance data quality?
To move towards data quality , several determining factors must be addressed. We share with you 5 main dimensions to consider.

Integrity
For data to be valuable, it must be sufficiently complete, which is why it is key to integrate all the data that make up the company, the teams or areas according to the needs and analysis objectives.

Accuracy
Data must be accurate, reliable and/or certified by some type of data governance body. This ensures the quality and accuracy of the information obtained.

Update
Data records must be recent, that is, up-to-date to be relevant to the intended use.

Consistency
This point involves data that is in the same format and is maintained between versions or instances and updates. That is, the data is not distorted as it passes through the different stages of the life cycle, from the transaction that originated it, through data collection, movements between platforms, transformation, aggregation, etc.

Accessibility
Assets must be easily retrievable by the people who need to access them (without compromising compliance requirements).

6 Key Practices to Achieve Data Quality
Data quality is possible when a comprehensive, proactive and collaborative approach is designed and executed within the organization.

All teams and systems must be committed to this approach and prioritize continuous improvement of information. It is key to have data policies that prevent the entry of incorrect or erroneous information.

Although it may seem impossible, it is not. To achieve this, it is essential to follow this roadmap with 6 fundamental practices.