The challenge of implementing data quality initiatives
Posted: Tue Jan 21, 2025 10:40 am
Discover the importance of good practices when implementing data quality initiatives.
Data is a strategic asset for almost all organizations, and that is precisely why data quality is essential for the success of the business. According to Experian's latest report, entitled "Building a Business Case for Data Quality ," there is still a long way to go in this regard.
According to the report, which discusses the findings of its global survey , while most organizations indicate that their data supports their business objectives, they estimate that, on average, one-third of their data is inaccurate.
Although respondents – around 400 professionals from around the world – consider data quality important, the survey reveals that they are often faced with poor data that can affect their ability to make strategic decisions.
In figures, only 2 percent of companies indicated that they fully trust their data, a percentage that contrasts with the 86 percent that consider that the implementation of a data quality initiative adds value to the organization.
At this point, the report concludes that it is a no-brainer gcash database poor data quality is a critical problem for businesses , and in turn the driver of data quality initiatives, in addition to regulatory compliance, business intelligence and improving customer experience.
Data quality as an essential part of MDM
Implementation issues
The value placed on data quality translates into an interest in carrying out a program that guarantees it permanently, but its implementation is complicated, he concludes.
On the one hand, the work detected that companies are investing in new technologies, but it also observed that the construction of a business model oriented towards data quality needs to be improved .
Good practices are therefore one of the main challenges for building this business model, ideally within a data-driven approach. Specifically, the study found several challenges still pending to achieve them.
These include the fact that in 80% of companies there are too many actors involved in their construction, the excessive time it takes to approve them, between 12 and 18 months, or, for example, the lack of a responsible person.
You may be interested in reading:
Data profiling, the first step in data quality
Communication based on facts
The quality of the data, the study continues, provides us with a better understanding of the data, providing reliable data, which will result in communication based on facts.
"This research supports what we've heard from our customers: business initiatives often take too long, and much of it is due to a lack of real data," said Thoms Schutz, senior vice president and general manager of Experian Data Quality.
Data is a strategic asset for almost all organizations, and that is precisely why data quality is essential for the success of the business. According to Experian's latest report, entitled "Building a Business Case for Data Quality ," there is still a long way to go in this regard.
According to the report, which discusses the findings of its global survey , while most organizations indicate that their data supports their business objectives, they estimate that, on average, one-third of their data is inaccurate.
Although respondents – around 400 professionals from around the world – consider data quality important, the survey reveals that they are often faced with poor data that can affect their ability to make strategic decisions.
In figures, only 2 percent of companies indicated that they fully trust their data, a percentage that contrasts with the 86 percent that consider that the implementation of a data quality initiative adds value to the organization.
At this point, the report concludes that it is a no-brainer gcash database poor data quality is a critical problem for businesses , and in turn the driver of data quality initiatives, in addition to regulatory compliance, business intelligence and improving customer experience.
Data quality as an essential part of MDM
Implementation issues
The value placed on data quality translates into an interest in carrying out a program that guarantees it permanently, but its implementation is complicated, he concludes.
On the one hand, the work detected that companies are investing in new technologies, but it also observed that the construction of a business model oriented towards data quality needs to be improved .
Good practices are therefore one of the main challenges for building this business model, ideally within a data-driven approach. Specifically, the study found several challenges still pending to achieve them.
These include the fact that in 80% of companies there are too many actors involved in their construction, the excessive time it takes to approve them, between 12 and 18 months, or, for example, the lack of a responsible person.
You may be interested in reading:
Data profiling, the first step in data quality
Communication based on facts
The quality of the data, the study continues, provides us with a better understanding of the data, providing reliable data, which will result in communication based on facts.
"This research supports what we've heard from our customers: business initiatives often take too long, and much of it is due to a lack of real data," said Thoms Schutz, senior vice president and general manager of Experian Data Quality.