Enterprise Data Quality Governance

By Srinivas Durvasula

Large organizations operating on diversified IT landscapes often experience the ill effects of poorly organized master data and inaccurate, redundant data flows. An inability to enforce and maintain data quality impacts critical functional areas across the enterprise such as call centers, service assurance, and revenue management. Lacking a complete 360 degree customer view results in ineffective customer issue resolution, campaigns designed for the wrong target audience or poor vendor consolidation strategies.

These issues can be attributed to poor data quality resulting from distributed data entry points and associated duplication of data in multiple systems, along with the lack of a single-source of master data.

In such a landscape, there is a growing need for businesses to adopt a strategic architecture to solve critical enterprise data management issues by managing master business data centrally and automatically publishing it to all transactional and analytical systems. Such a strategic data governance model will also allow businesses to consolidate and harmonize master data, develop a single, effective way to maintain and author "Master Data" and, finally, commission solutions to ensure the quality of data.

Such master data changes are often complex activities involving long project durations and high costs, and require organizations to engage in a focused data quality governance initiative to ensure that their enterprise data can be leveraged to support their critical business objectives.

A Structured Approach

Customer service management relies on information from sales automation and CRM systems to manage and track transactions, and for identifying new revenue opportunities. An accurate 360 degree view of the customer is necessary to quickly respond to inquiries and identify potential sales opportunities. Inconsistent master data contributes to poor campaign management, ineffective issue resolution and product/service line remediation failures.

But data quality needs are specific to the business objectives of individual organizations. Here is a four-step process to build the business case for addressing organizational data quality needs, which is a pre-requisite for devising a data quality implementation roadmap.

Assess Data Quality: Data quality within sales automation and CRM systems should be assessed to identify master data requirements associated with customer master information and product/service lines availed. This assessment will help identify risks posed by "AS-IS" data quality level to achieving business outcomes such as reducing customer attrition or growing revenue from an existing customer base.

Evaluate Data Quality Requirements: The next step is an evaluation of master data quality and its variance with key organization standards.

Business objectives should define the standard of the data quality solution. For example, new revenue through targeted campaign management will need granular analysis of demographic profile and customers needs to arrive at expected conversion ratio. However, in procurement spend analysis, the benefits of commodity and vendor consolidation and mapping may yield lower returns as the spend value diminishes.

Enhance the Data Governance Model: The analysis of customer data flow variance across the enterprise will expose complaints and improvement areas within certain product/service lines, while also providing mechanisms to optimize the data governance model. This will help in enhancing or developing data integration and gradual integration into Master Data Management (MDM) initiatives.

Develop a Data Quality Monitoring Framework: Ongoing master data monitoring requires a pre-defined mechanism for monitoring business processes associated with master data. Such monitoring will provide smooth support for reporting and alerts relating to changes to the customer/product/organizational master data repository.

Businesses that implement these steps can enjoy a number of key benefits such as the application of organization or industry standard master data such as D&B, UNSPSC, etc. for identification of key master data elements, or cost reduction due to improved process efficiencies such as improved customer and other 3^rd party interactions and effective compliance. Further benefits include enhanced data governance model based on pragmatic business considerations such as immediate goals of customer retention and long terms goals such as improving market share and a mechanism to monitor business process and information alignment with business objectives and goals.

Biography

Srinivas Durvasula is Senior Manager of Business Intelligence Practice at Virtusa Corporation. With about 12 years of experience in information technologies and manufacturing management information systems, Durvasula is responsible for business intelligence solution consulting and implementation, program management for development and incubation of business intelligence and data management technologies and solutions.