The pursuit of data quality can take many forms. The goal is the clean, de-duplicated, verified, complete data that gives your sales, marketing, supply, and finance teams the actionable insights they need to be most effective.
Customer data is a prime example of data that is prone to need quality improvements. Typically, the marketing team’s data challenges can be traced back to the organization’s multiple, siloed line-of-business applications, each of which has its own way of organizing and using data. Adding to the problem are the usual baseline big data headaches: data motion, variety, volume, and the need for verification.
From frequent address changes to variations resulting from mergers and acquisitions, or even seemingly benign shifts in the normal course of operations, all eventually lead to the obsolescence of customer data. The accurate and timely reflection of these changes varies greatly across the organization’s line-of-business applications. And, with data obsolescence comes the loss of trust in the data and, inevitably, its under-utilization.
Each of these root causes of data obsolescence supports a strong business case for data quality. Whether for sales and marketing, informed market segmentation and targeted messaging, or for supply chain risk mitigation and vendor stability monitoring, data quality is the foundation of effective enterprise management.