Content area
Full text
Data are a principal strategic enterprise asset. Like any other asset, data must be managed over the entire data life cycle, from initial capture to storage, processing, access, use, retention, and destruction. Data governance is a data management function that involves processes, policies, standards, accountabilities, and framework to ensure proper use and protection of data and effectively manage the data assets in an organization. This article focuses on understanding the critical success factors (CSFs) driving the implementation of data governance in organizations and presents the results of the survey conducted in different organizations. In total, 14 CSFs with 48 variables were identified from the literature and discussions with information management professionals. Survey results revealed that leadership and management commitment, practical and enforceable data governance policy, and incremental approach to data governance are the most important factors. Use of data governance tools is the least important factor in the successful implementation of data governance.
KEY WORDS
critical success factors (CSFs), data, data governance, data governance framework, data management, data quality
INTRODUCTION
Digitization of management systems, the introduction of the internet of things (IoT), electronic data capture, advances in technology, the low cost of digital storage, and the ability to electronically store large volumes of data for extended periods of time have led to organizations becoming more and more data rich. Organizations have vast amounts of digital data, stored in a multitude of heterogeneous data repositories (databases and file systems) across an organization, which is increasing at an alarming rate. In other words, there is an explosion of data. However, to leverage value from these data, effective management of data and data quality is needed.
The past three decades have seen the deployment of various data intensive initiatives, such as data warehousing, data integration, data migration, master data management, and others. For these data intensive initiatives to be successful, and for data to continue to deliver business value while different regulatory and compliance requirements are met, there needs to be an effective data governance framework in place. Without this framework, organizations are likely to end up with data quality issues, data security problems, or inconsistent data models that produce conflicting results, depending on the business unit or department that independently created them.
Simplistically, data governance involves...