Data Governance Solutions

The Biggest Challenges With Data Governance And How MURIS Solves Them

Muris provides a consulting services that aims to understand the organization data problems and flows. As part of this process, we also help define a roadmap for your organization’s data governance strategy.

We have experience in all classic data governance issues:

  • Data quality issues, including master data management, information discovery and metadata management
  • Data lineage, including traceability and provenance
  • Data security and privacy, including anonymization of sensitive personal data
  • Data archiving and retention policies
  • Data analytics and reporting processes

We also have experience in emerging data governance issues: Data science and AI/ML, including open source software, commercial tools and vendor selection Cloud computing, including security and privacy compliance Data analytics and reporting processes Data governance maturity assessment

Data governance strategy development Data integration, including data federation and unified data access Data lineage, including traceability and provenance.

Data quality, lineage, security and privacy are all integral to data governance. However, there are other aspects of data governance that are often overlooked, such as reporting processes and analytics capabilities. In fact, many organizations suffer from not having a clear view of their data assets or how they can be used to make better business decisions. By not having a clear view of their data assets, organizations are missing out on opportunities to make better business decisions. The lack of reporting processes and analytics capabilities can lead to the following: Lack of visibility into data quality and lineage Lack of compliance with regulations (e.g., GDPR) Data integration challenges A fragmented view of data that makes it difficult for users to access the right data sets for their needs Lack of automation and self-service capabilities that make it difficult for users to access the right data sets for their needs The lack of a unified view of data can lead to costly mistakes. For example, employees may use inaccurate information from multiple sources to make important decisions that have a direct impact on an organization’s bottom line.