Poor Data Quality
One large issue with data is data quality. How many times have marketers pulled a list of emails out of their CRM, only to find that many of the email addresses bounced? Or how many times have corporate sales people tried connecting with leads who were no longer at their former companies because CRM data was outdated?
Data quality is comprised of dimensions: timeliness, integrity, conformity, uniqueness, consistency, completeness, and accuracy. Any one of these dimensions gone wrong can effect the accuracy of data analysis, visualization, and ultimately its usability for predictive analytics or machine learning.
At Aptude, our data engineering team specializes in creating and implementing best practices for data quality for this very reason: if you have data but it’s not good, you might as well not have the data at all.