In the purchase to pay process, poor quality vendor data causes missing purchase contracts or pricing information for important materials leading to delays in the procuring vital goods. This can result in production material short-ages, use of non approved procurement channels and ultimately higher costs of goods sold.
In the order to cash process, incomplete or inaccurate customer records, incorrect credit limits, or inaccurate pricing information result in lower overall customer service, lost or delayed revenue, increased service costs and a huge effort by the customer service team to resolve without losing customers. Furthermore, inaccurate data result in incorrect management reporting meaning that vital business decisions are made based upon incorrect information.
Data is deemed of high quality if it correctly represents the real world to which it refers and meets business-driven metrics. Data quality is essential, but the level of quality and the related investment depends on an understanding of business and investment priorities. High-quality data must be complete, timely, accurate, consistent, relevant and reliable. Initiatives that only address portions of the data quality strategy are ineffective and costly in the long term and tend not to be aligned with overall business priorities. What is required is an ongoing program of improvements in all aspect of the enterprise ranging from data entry standards and measures to technical data validation routines to how business organization and structure changes are implemented.
The systematic analysis of data, or data profiling, gathers actionable and measurable information about its quality. Information gathered from data profiling activities is used to assess the overall health of the data and determine the direction of data quality initiatives.
Data cleansing is a continuous process that requires corrective actions throughout the data life-cycle. Data cleansing activities must have adequate and dedicated resources from both the business and technical support organizations. Business resources are critical to provide context and insight into potential data anomalies.
Data compliance consists of the ongoing processes to ensure adherence of data to both enterprise business rules, and to legal and regulatory requirements. Data compliance includes four areas, controls, audit, regulatory compliance and legal compliance.
Expedien has deep experience working with global organizations to help develop solutions that effectively manage data quality from all points of the business.
We evaluate the landscape and bring in the appropriate resources to develop and refine our solutions to address the specific industry and organization needs of our clients.
Through our understanding of the importance of balancing business priorities, we create a realistic approach to ensuring that data is available, accurate, consistent, reliable and secure, ultimately enabling better business management and organizational performance.
Our Data Quality Assessment™ evaluates the “well-being” of your master data. Data Quality Assessment helps you uncover the data issues and enables you to identify how your data is compromising your business processes, your bottom line and your goals. The report provides a clear, real time view of your company’s data.Download