Data quality: what happens when data quality is poor?

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Far from being something of little importance, not paying special attention to guaranteeing data quality can cause serious problems for organizations, even leading to irreparable damage in terms of business.

When information quality is poor, customer relationships are damaged and analyses become inaccurate. As a result, decisions are not made correctly.

In this article, we delve into the impact of having poor quality data and detail what actions companies should take to improve data quality.

The dangers of poor data quality
Good quality data are accurate, adequate, consistent and complete. Poor quality data, on the other hand, are inaccurate, incomplete, inconsistent or irrelevant.

This group includes records with typographical errors, ambiguous, duplicate or missing values, inconsistent formats and obsolete information, among others.

There is no single cause for poor data. It can arise from bahrain phone number lead human error (either manual data entry or human bias), system integration issues, inadequate validation procedures, use of outdated technologies, lack of data governance policies , communication and collaboration failures, unvalidated sources, and fraudulent or malicious activities.

Beyond the reasons why an organization has poor quality data, the impact that its use has on the business is really considerable.

Loss of opportunities
What happens when a company's data on its potential customers is unreliable or duplicated? The company misses opportunities to identify sales opportunities.

The same thing happens when information on consumer trends is inaccurate. In this case, the ability to develop solutions that respond to market trends is reduced.

Without high-quality data on which to base their decisions, companies miss important opportunities to develop.

Lower operational efficiency and productivity
Many corporate processes require reliable data to function optimally. If data is incomplete or inaccurate, employees must detect and correct errors manually.

The time spent making these corrections means that they have to put aside the functions they normally perform, which leads to a decrease in the company's efficiency and profitability.

Loss of income
Inaccurate or incomplete data reduces the profitability of organizations, causing them to lose customers and business opportunities, while at the same time leading leaders to make poor business decisions.

In fact, according to Gartner, organizations lose around $15 million annually due to poor data quality .

Impoverished customer experience
Nowadays, personalization is one of the most valued aspects by customers when choosing a product or service to solve their needs.

Therefore, the most effective way – and perhaps the only way – to attract and retain them is to offer them a tailored experience.

If a company has unreliable or incorrect data, it will not be able to design solutions that adapt to the profile of its consumers and, therefore, will end up losing them.

Bad reputation
Poor quality data can also damage a company's reputation, both with customers, who feel disappointed and lose confidence in the company, and with employees.

Why? Because when records are inconsistent, inaccurate or incomplete, teams spend long hours analyzing information that is unlikely to be useful later.

Or they have to manually correct errors. This undoubtedly ruins the organization's image in the labor market and reduces the productivity of its employees.