How to Overcome Data Fragmentation: A Practical Guide to Enterprise Data Governance

Today’s large group enterprises have increasingly detailed internal divisions—procurement, services, marketing, sales, development, support, logistics, finance, human resources—each constantly generating massive amounts of data in various formats, including structured and unstructured data stored in IT systems and electronic documents.

Consequently, managers face growing confusion: where does this data come from, can we trust it, what relationships exist between data sets, and who can understand it?

The root cause is data being stored in a scattered manner. As enterprises evolve, they build numerous internal IT support systems such as ERP, CRM, and financial management systems, which leads to fragmented data storage.

To analyze data, aggregation is required, but the decentralization of production systems results in inconsistent standards, models, and low data quality, making data governance the most urgent need for enterprises.

This article presents an implementation plan for enterprise data governance, detailing aspects that enterprises care about, including data standard management, metadata management, data quality management, data integration management, data asset management, and data security management.