Enhanced governance and control 

Our software enables organisations to improve performance across a range of levels:



More effective use of information

Reduced risk of inadvertent mis-reporting

Lower costs associated with enterprise analytics 



Greater consistency of metrics across the enterprise

Enhanced productivity of data teams

Improved context for users



Supports complex data architectures

Sources metadata from disparate applications

Automation supports optimisation of analytics apps


Key Drivers and Use Cases

  • Automated metadata capture and active monitoring of data assets underpins the capabilities of Metonomy software in the support of enterprise analytics governance.

  • Complex data architectures require a range of controls to monitor and regulate data as it flows through an organisation to the point of use or consumption. Metadata management is a cornerstone to making data fabric or mesh viable.

  • Improved control is achieved through the identification of anomalies across disparate applications, tracking usage over time and determining the degree of similarity and potential duplication between elements.

  • The use of software in conjunction with processes, policies and people with specific skills provides a foundation for improved data governance.

  • Manual analysis of analytics applications is often time-consuming, frequently repetitive and always inefficient. Automation enables data teams to be more effective in optimising existing applications and more productive when migrating to new.

  • Enriching asset metadata with annotations relating to purpose, ownership or other categories and types provides context to output. Context is necessary for people to be able to understand the nature of the information they are accessing and to use it appropriately.


Efficiencies Gained

Automation provides efficiencies throughout the project lifecyle:



Assessment & Inception

  • Automated metadata capture from source applications.  Analysis based on usage and similarity / duplication to support proposed scope for delivery.

  • Information reviewed with stakeholders to refine scope, e.g. for each application: # of assets used in most recent 18 months (if available), # of assets that have < 90% similarity (or whatever the threshold to be sufficiently unique).  Factors such as business priority of assets (e.g. regulatory) and key business processes to be supported also need to be considered.

  • Identification of data sets that are needed to support in-scope dashboards, reports, analyses & alerts.

  • The process of defining scope will be evidence-based, the result of consultation with key stakeholders and undertaken more efficiently via automation than would be the case otherwise.




  • In-scope assets to be tagged and tracked, e.g. assign status and resource for as-is application.

  • Metadata acquired from analytics assets as they are developed in the target environment. Information is assgned to support delivery such as status and responsible resources.

  • To-be assets analysed for accuracy through development and test activities.

  • Captured metadata & applied annotations used to produce progress reports based on as-is and to-be asset status.

  • To-be analytics assets enriched with contextual metadata, e.g. purpose, owner, SME, verification.



Operations, Support & Maintenance

  • Annotations used to certify status of analytics assets, e.g. self-service dashboard approved by central IT team.

  • Analysis module used to search, explore and inspect assets for any issue reported.

  • Comparison and Usage modules employed to monitor asset inventory and identify & address emerging report sprawl.

  • Contextual metadata applied and maintained, e.g. ensure complete for new assets; owners & SMEs change over time.

  • Impact of proposed change on analytics assets assessed efficiently via automation.

Metadata Connectors

Metadata are acquired from applications via Connectors using the most appropriate method supported by the host vendor for the product, e.g. web services REST APIs, SOAP, SDKs or XML.  A range of connectors is available for product suites provided by software companies including Oracle BI, Microsoft, SAP, IBM, Qlik, Tableau and others.

We prioritise development of additional connectors based on the needs of our clients. 


VendorProductProduct Module
MicrosoftPower BI DashboardPower BI Files
MicrosoftPower BI Report ServerPower BI Application Elements
MicrosoftPower BI Report ServerPower BI Catalog Items
MicrosoftPower BI ServicePower BI Application Elements
MicrosoftPower BI ServiceActivity Events
MicrosoftSQL Server Integration ServicesPackages
MicrosoftSQL Server Reporting ServicesReports
MicrosoftSQL ServerDatabase Engine
OracleBusiness Intelligence Enterprise EditionBI ACL
OracleBusiness Intelligence Enterprise EditionBI Analysis
OracleBusiness Intelligence Enterprise EditionBI Dashboard Prompts
OracleBusiness Intelligence Enterprise EditionBI Dashboard
OracleBusiness Intelligence Enterprise EditionBI Publisher
OracleBusiness Intelligence Enterprise EditionBI Server
QlikQlik SenseQlik Sense Desktop
QlikQlik ViewQvw Documents
SAPBusinessObjectsData Foundation Layer
SAPBusinessObjectsWeb Intelligence Reports
SalesforceTableauTableau Desktop
SalesforceTableauTableau Server + Online
EclipseBIRT / ActuateReport