Recent research indicates that marketing and sales measurement data continues to be a problem for marketers, and likewise that improving data quality is both the biggest challenge to marketing data success and the most important objective of a marketing data strategy. So what’s the reason for inaccuracies? A study from Experian Data Quality [download page] offers some insights.
According to the survey of more than 1,400 respondents with visibility into their organization’s customer or prospect data management practices, human error remains the leading challenge contributing to a lack of contact data accuracy. However, people seem to be cleaning (pun intended) up their act: the 33% citing human error this year was down substantially from 56% last year.
By contrast, insufficient budgets seem to be a growing challenge, as the 30% noted this problem marked a rise from last year’s 19%. Also becoming a larger contributor to inaccuracies this year is the lack of internal communication between departments.
Separate results from the study do indicate a worsening trend in terms of central control of data quality. This year just 24% identified control as being held centrally by a single director, down from 31% last year. Instead, 56% say that despite some centralization, many departments still adopt their own strategy for data quality.
There are several reasons to maintain high quality data, per the report. The most common of those is is to increase efficiency (57%), though others see the opportunity to achieve cost savings (38%) and enhance customer or citizen satisfaction (37%).
The full report is available for download here.
About the Data: The study describes its methodology in part as follows:
“Produced by Loudhouse for Experian Data Quality in November 2016, the study polled more than 1,400 people across eight countries around the globe. A variety of roles from all areas of the organization were surveyed, including information technology, data management, marketing, customer service, sales, operations, and more. Respondents were chosen based on their visibility into their organization’s customer or prospect data management practices. Organizations that were surveyed came from a variety of industries including IT, telecommunications, manufacturing, retail, business services, financial services, healthcare, public sector, education, utilities, and more.”