Governance and Quality

One of the key challenges in analytics today is to co-ordinate between different stakeholders' needs for self service and to encourage better data stewardship practices.

Data Governance and Data Quality
Quality Control

Automate data quality checks including reconciliation and detection of duplicate records.

Data Acquisition

Provide visibility of data availability and automatically alert processing issues.

Publishing Control

Create mandatory metadata to capture before reports are published. Classify documents and reports for easier discovery.

Role Based Security

Assign responsibility to data stewards and report authors across departments and regions.

Data Stewardship

Enforce data governance policies such as classifying data sensitivity and capturing descriptions of key metrics.

Business Glossary

Deliver better context of reports with an integrated report and data catalog.

Overcome a major analytics adoption hurdle – lack of trust in the data.

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How Loome does it

Loome consists of a number of modules, each aimed at augmenting a specific part of your enterprise data governance and quality practice.

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Module Integrate Assist Monitor Publish
Data Catalogue and Business Glossary
Automated Reconciliation*
Single Customer View Processing*
Data Job Instrumentation
Audience Targeted Report Portal
Report Publishing Process
Report Issues Help Desk
SME Tagging
Report Usage
Federate Content Management
Data Validation and Screening*
Report Reliability Badging~
Interface Agreements~
Master Data Management~

~Coming Soon

*Server Only Feature

Want to find out more? Contact us for any information you need.