5 steps to achieve better data quality

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The quality of information determines the efficiency of processes, improved decision-making and customer satisfaction.

We invite you to learn how to get the most out of your data in this article.

What is data quality?
The world's largest and most well-known companies and organizations rely on data to make much more efficient decisions , speed up processes, and streamline many other activities.

Ensuring data quality ensures that it is accurate, complete, adequate and consistent with what it reflects, including other actions such as data validation, cleaning and standardization.

Data quality refers to a set of techniques, practices and oman phone number lead methods used to obtain data that is suitable, robust and objective for decision making. Specifically, the state of the data is measured with respect to terms of integrity, quality, relevance and consistency, among others.

This concept goes far beyond just checking and checking a box; it is about an ongoing commitment to preserve the reliability of the data obtained and ensure the objectives pursued.

Checking that the data is of quality is relevant for:

Informed decision making : operational and strategic decisions will be accurate, improving performance and presenting clear competitive advantages.
Operational efficiency : Processes are improved by reducing errors, downtime and rework resulting from the use of faulty data.
Regulatory compliance : the use of accurate data enables compliance with standards and regulations, avoiding legal sanctions or loss of reputation due to unethical practices.
Consequences of poor data quality
Just as they can bring great benefits , it is also known that if a company does not invest or care about the quality of the data it uses, it could face consequences such as:

Bad decisions : Bad decisions can affect the achievement of objectives, delay the company's growth and seriously damage its economy.
Loss of customer trust : Errors and negative outcomes can result in an unsatisfactory customer experience. As a result, a loss of loyalty and trust towards the brand may occur.
Additional costs : Using erroneous data can result in financial costs and the need to use additional resources to correct errors, which negatively impacts business profitability.
Steps to achieve better data quality
To achieve better data quality , organizations should follow these 5 steps:

1- Data profiling. Helps uncover data quality issues, risks, and general trends. Truly understanding what the data looks like will give a clear idea of where to focus improvement efforts.

2- Standardize and match data . Many data quality problems are solved through standardization and comparison processes. Standardizing data is an essential step when preparing data for analysis, so that all data being examined is in the same format. Data matching allows you to associate different records within different systems, and even integrate data in real time.

3- Data enrichment. The data has already been standardized and is known to be correctly formatted. It is then possible to enrich it by adding related data to obtain more complete information and a better understanding of it.

4- Monitoring . Data quality is not a “one-and-done” operation. It has to be an ongoing practice because data is constantly changing, and those changes must be monitored to ensure quality is maintained. When new quality issues are discovered, you can go back to previous steps to standardize, combine, and enrich that data and get it back on track.

5- Operationalization . The final step is to operationalize data quality – see data quality in action. By automating the controls and rules created in the previous steps and embedding them into your data pipelines, you can see significant efficiency gains and dramatically reduce the amount of bad data that requires interventions to fix.

By taking these steps, organizations can significantly improve the quality of their data. This, in turn, will lead to more consistent decisions, better business outcomes, and more efficient processes.

At Data IQ we can help you improve the quality of your data to speed up and increase the efficiency of your decisions. Let's talk!