Data obscurity is data that exists in a state of being unknown, unintelligible, and opaque. With obscurity comes the loss of trust in the data, its under-utilization, and lack of value that is required to make informed business decisions.
A single record of truth for your business decisioning needs doesn’t have to be elusive; nor the broken promise of your Data Officer or CIO who committed to deliver the trusted information you expect – without obscurity.
From frequent company moves to ownership changes resulting from mergers and acquisitions, to even seemingly benign shifts in the normal course of operations, a vast array of events eventually lead to customer data becoming more obscure. The accurate and timely reflection of these changes can vary greatly across the organization’s line of business applications.
Each root cause of data obscurity supports a strong business case for data clarity that hinges on a data quality foundation built with processes, governance, and active oversight.
Removing data obscurity is further complicated by classic Big Data challenges that create an unwieldy variety, velocity, and volume of information moving around your enterprise. For example, your marketing team’s data challenges when performing customer segmentation analysis can likely be traced back to your organization’s multiple, siloed CRM, marketing automation, and other data storage systems, each of which has its own way of organizing and using data.
Customer data is a painful example of data that is prone to obscurity
The sheer weight and speed of data input to your organization from sales interactions and marketing campaigns can be overwhelming. Poor data governance further clouds the lens by which you view your data.
Each root cause of data obscurity supports a strong business case for data clarity that hinges on a data quality foundation built with processes, governance, and active oversight. Whether for informed customer 360 analysis, market segmentation, supply chain risk mitigation, or vendor stability monitoring, data clarity is fundamental for effective enterprise data management.
There are many paths to take as you begin your journey to data clarity
It starts with a simple but mission-critical process of identity resolution: a process to match your customers to a trusted, structured data source and assignment of a unique, persistent identifier for each company you do business with.
Identity resolution always begins with another data “v”: verification – and more and more enterprises are looking to pre-mastered, pre-verified referential third-party data to confirm business partner identities. Your data stewards, the people who gather, analyze, and curate your data, play a vital role in preparing your source data for identity resolution, governing the valuable frame of reference for your decision making.
Fortunately, there is a spectrum of solutions that can bring you closer to your data clarity goals. These can range from the ideal of an enterprise-wide master data management platform and the data stewardship practices that enable it, to more tactical, scalable ‘start small’ data stewardship practices and automation services that enable more timely identity resolution.
The data quality solution provided by one of Dun & Bradstreet’s newest partner solutions – Matchbook Services – is an example of a ‘start-small-and-grow’ approach to address the data quality needs and challenges many organizations face. By matching your data to Dun & Bradstreet’s pre-mastered commercial content on over 265 million businesses globally, you can master your business records based on confidence criteria you define.
It’s no surprise that data quality remains a key aspiration and critical need for organizations. But achieving and maintaining data quality requires processes, governance, and active oversight. The combination of trusted third-party pre-mastered data with the tools and technology that best fit your organization will set you on the right path.
Obscurity squashed, clarity achieved.
Source: Eric Sonntag | D&B