Posts by author
Chris Shaw
6 Reasons Companies Ignore Data Quality Issues
Be it lean startups or multinational enterprises, true, proper maintenance of data quality gets swept under the rug. The initial efforts put in place early-on aren’t matched with best practices in the long run. Managers may be tempted to leverage existing CRM platforms tools to try and meet the data cleansing needs – not knowing that these solutions fall sorely short of any kind of proper record cleansing. While budgets are emptied in favor of customer acquisition and advertising spend, these half-hearted attempts at cleaning house leave valuable customer records collecting dust, hidden in plain sight.
Assessing Your Data Quality Needs
Before any data quality project, it is critical to go beyond the immediate issues of duplicate records or bad addresses and understand the fundamental business needs of the organization and how cleaner day will enable you to make better business decisions.
Creating Your Ideal Test Data
Every day we work with customers to begin the process of evaluating helpIT data quality software (along with other vendors they are looking at). That process can be daunting for a variety of reasons from identifying the right vendors to settling on an implementation strategy, but one of the big hurdles that occurs early on in the process is running an initial set of data through the application.
Why is Data Quality So Hard?
If you take a good look around the master data management (MDM) industry, data quality is the buzz word of the day. Blog posts, surveys, analyst briefings, white papers and testimonials are filled with commentary on the importance of good data quality. What is the importance?
