It is no secret that poor data management can have devastating consequences, ranging from revenue loss to decreased customer satisfaction. These negative effects are numerous and significant.
In previous articles, we discussed some of the common mistakes organizations face when they lack structured, clear data and a solid strategy. These can be summarized as follows:
- Revenue loss: Companies can lose between 15% and 20% of their annual revenue due to poor data quality. This happens because of errors in customer segmentation, ineffective marketing campaigns, and failures in customer retention.
- Incorrect decision-making: Decision-making based on incorrect, outdated, or incomplete data can lead to strategies that are misaligned with market realities or customer needs. This can result in wasted investments and missed opportunities.
- Reduced customer satisfaction: Incorrect data can lead to poor customer experiences, such as wrong shipments, lack of personalization in offers, and issues with customer service. As a result, this can decrease customer loyalty and negatively affect the company’s reputation.
- Increased operational costs: Correcting errors and managing duplicated or incorrect data require additional time and resources, which raises operational costs and reduces company efficiency.
A real-life example of this occurred with a telecommunications company that faced serious issues due to duplicated customer records and incorrect contact information.
The main problem was that the company had multiple records for the same customer due to data entry errors and the lack of a centralized data management system, leading to significant confusion in billing and customer service.
Because customers received multiple bills for the same service, it caused frustration, increasing calls to customer service, in addition to a loss of trust in the service provider. This resulted in decreased loyalty and an estimated loss of millions of dollars in annual revenue.
In response to this crisis, the company implemented a centralized data management system and used data cleansing tools to remove duplicates and correct errors. This significantly improved data accuracy and reduced operational costs associated with managing errors.
Thus, poor data management can have a profound negative impact on businesses, affecting their revenue, customer satisfaction, and operational efficiency. That is why it is crucial for organizations to invest in proper systems and processes to ensure the quality and accuracy of their data. Only then can they fully leverage the value of their data and avoid the negative consequences of poor management.
Reference:
Data Ladder El impacto de la mala calidad de los datos: Riesgos, retos y soluciones. Tomado de El impacto de la mala calidad de los datos: Riesgos, retos y soluciones – Data Ladder
Puro Marketing. El impacto negativo de la sobrecarga de información y la mala gestión de los datos en las empresas. Tomado de: El impacto negativo de la sobrecarga de información y la mala gestión de datos en las empresas
