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Why does data quality matter, in practical CRM terms?

Data quality

What is poor data quality?  

It can include data that is not properly maintained, secured, or validated. A range of factors, such as human error, system malfunctions, challenges in data integration, and others, can lead to the issue of poor data quality. 
 
Data quality

What is the cost of poor data quality?

The cost of poor data quality can have significant impacts on businesses across various aspects, leading to financial losses, reduced productivity, and missed opportunities. Here are some of the key areas where poor data quality can incur costs:

Inaccurate Decision-Making: It can lead to inaccurate insights and analytics, resulting in flawed decision-making. This can cause businesses to invest in the wrong strategies, products, or markets, leading to financial losses.
Wasted Resources and Operational Inefficiencies: Incorrect or outdated data can lead to wasted resources as employees spend time rectifying errors or working with inaccurate information. This inefficiency can slow down processes and hinder productivity.
Missed Business Opportunities: Inaccurate or incomplete customer data can result in missed opportunities for sales, marketing, or customer engagement. It may prevent businesses from targeting the right audience or personalizing their approach effectively.
Damaged Customer Relationships: The quality of poor data can lead to customer dissatisfaction due to errors in billing, shipping, or service delivery. This can damage the business’s reputation and lead to customer churn.
Increased Marketing Costs: Inaccurate data can lead to ineffective marketing campaigns, wasting marketing budgets on targeting the wrong audience or sending messages to incorrect addresses.

Conclusion:

In summary, the repercussions of poor-quality data are substantial, manifesting in both considerable financial losses and a variety of other detrimental effects. Investing in data quality management and implementing best practices can help to mitigate these costs and ensure that your business is making decisions based on reliable, accurate data.

Data Quality
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