Four Ways Data Quality Solutions Can Better Your Business

http://blogs.pb.com/digital-insights/2015/03/03/4-ways-data-quality-solutions-can-better-business/

The article “Four Ways Data Quality Solutions Can Better Your Business” explains how better data management can help companies boost their business and better serve their customers. In order for businesses to succeed, and to serve their customers with the highest efficiency and effectiveness, it is essential for the data to be accurate and trusted. Businesses spend millions of dollars annually by relying on inaccurate or poor quality data, which causes unnecessary waste and slows down the decision-making and understanding of business problems.

So, effective data management can help companies:

1. Reduce Costs

For example, bad data can interfere with a company’s ability to keep track of their inventory and as a result, the store is out of stock when a customer comes in and thus, loses the sale.

2. Save Time

Accurate data reduces business-processing time and accelerates it. When data is inaccurate, companies waste valuable time and resources searching for that bad data, fixing it and remedying the situation.

3. Increase Response Rates

Data accuracy and timeliness increase returns on business postal or email campaigns and eliminate money and time waste sending mails to outdated addresses. Also, businesses may send the wrong message to a customer and lose that customer for good. For example, if a new customer’s information is not collected accurately, and the information is sent to the wrong individual, the customer never receives the information and is unable to respond.

4. Boost Customer Satisfaction

Bad data quality can lead to critical errors, which can damage a company’s reputation and scare current and future customers away. For example, bad data could cause confusion about a delivery date, and the item arrives after the customer needs it.

1. What negative impact a dirty data has on business processes? Do you have any real life example when bad/dirty data caused business loss or even failure?
2.  How business can eliminate a dirty data?
3. Who is responsible for data quality in an organization?