Growth has always been the top priority for every company. Regardless of the type of industry, any company will find it difficult to achieve the target growth if the data quality is poor.
The following are the effects of having poor data quality towards the company:
According to a survey conducted by Gartner, an IT service management company, 40 percent of businesses will fail to achieve their business objectives due to poor data quality.
Bad data will result in errors in consumer targeting as well as incorrect data analysis which ultimately results in adverse decisions and ineffective strategies.
A research carried out by Experian Data Quality found that the average company loses 12% of its revenue due to poor data. When including all businesses in the United States, the losses reached $ 600 billion!
In addition, poor quality data such as duplicate data can cost companies up to millions dollars due to overruns in marketing costs just for unnecessary targets.
Apart from the loss of a certain amount of money, the company also suffered a loss in the form of wasted time. It has been estimated that each company has incurred a time loss of 550 hours per year on average.
Duplicate data can inflict a financial loss to a company in terms of missed sales opportunities. Imagine the time spent approaching the wrong prospect.
Sirius Group, an International Insurance Company, reveals that best practice in data quality can directly result in a 70% increase in revenue.
However, 10 percent of customer and prospect records contain critical data errors that include faulty demographic data, expired dispositions, and other basic weaknesses. On average, companies that fail to follow best practices in data management, data error rates can swell as high as 25%.
Poor data quality often causes problems in every company. These impacts include everything from mis-targeted marketing, unnecessary expenses, to ineffective decision making and strategies.
In this digitized era, it is time for every company to switch to digital checklists like Nimbly and maximize it to help companies process operational data professionally.
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