Guidelines for implementing master data governance

Abstract

In order to increase business performance through modern data utilization methodologies, organizations implement several new information systems and applications. As these solutions usually replace and/or complement one or more existing solutions, the implementation success is to a great extent determined by the organizational data management capabilities. If data can't be migrated, harmonized or cleaned in a proper and systematic manner the implementation is unlikely to succeed. But it is not only these implementations that require capabilities to handle data; the existing information systems are often crippled by data quality problems directly caused by lacking data related policies and workflows. Although there has been a huge advancement in the valuation of data and data management during the last decade, we are yet to manifest a similar leap in the organizational data management capabilities. All organizations possess some set of data management capabilities. But it is often the governing actions, the efforts that ensure data management is evolving with a correct pace and direction, that are missing. 'Data governance' is the key for systematic development of sustainable data management solutions. It is the organization and implementation of policies, procedures, roles, and responsibilities which model and enforce decision rights, accountabilities, and rules of engagement. This thesis introduced the key concepts associated with data governance and its implementation through one of the primary approaches to data governance, the master data governance. Master data governance is a narrowed down version of a full-scale data governance framework that focuses solely on governance of master data.

The primary objective of this thesis was to assess and guide the master data governance efforts of minerals processing and flow control technology and service supplier Metso by carrying out a thorough descriptive literature review on the topic and executing a small-scale empirical research focused on gathering insights from the key personnel working with master data management and data governance. Several development suggestions were identified from the empirical data. Due to lack of existing governance processes, an establishment of a novel master data governance function was suggested. It was highly recommended that Metso should appoint a chief data officer that would have the enterprise level responsibility of the governance efforts. In preparation to the implementation, it was suggested that Metso should review and assess both the as-is and the to-be situations of the data management landscape utilizing the best-suited assessments and governance frameworks. On the basis of the preparation efforts Metso was suggested to craft the enterprise-level principles, the initial governance structure, and the initial operating model. Furthermore, it was proposed that Metso would establish new operative governance procedures focusing on systematic policy management and business oriented data quality metrics. The enterprise-level development actions were suggested to be initiated in service business segment which has already implemented some relevant development projects.