Master Data Management is inherently challenging. Technology alone will not solve the problem – most of the root causes issues are process and competency-oriented:
Master Data Management is inherently challenging. Technology alone will not solve the problem – most of the root causes issues are process and competency-oriented:
- Organisations typically have complex data quality issues with master data, especially with customer and address data from legacy systems
- There is often a high degree of overlap in master data, e.g. large organisations storing customer data across many systems in the enterprise
- Organisations typically lack a Data Mastering Model which defines primary masters, secondary masters and slaves of master data and therefore makes integration of master data complex
- It is often difficult to come to a common agreement on domain values that are stored across a number of systems, especially product data
- Poor information governance (stewardship, ownership, policies) around master data leads to complexity across the organisation
MDM solutions are often perceived by business and executive management as significant and costly purely due to infrastructure improvement efforts lacking well-defined tangible business benefits. In order to avoid this, organizations should make sure to align this work with other initiatives that improve business processes, business intelligence, reporting and analytics, help reduce administrative overhead caused by redundant data entry, and provide other demonstratable benefits.
Data Quality Improvement requires more than Technology
Even a very sophisticated MDM technology solution cannot resolve data quality issues if proper standards and governance procedures are not in place. MIKE2.0’s open source solution for Master Data Management works in conjunction with solutions for Data Investigation and Re-Engineering and Data Governance to address historical issues, prevent new on-going data quality issues from occurring when possible and provide an enterprise exception processing framework for efficient data processing management.
