Metadata is the key for almost all forms of automation and augmentation in modern data utilization scenarios.
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Faced with an industry apps explosion, richer contextual awareness across our data universe — ET, OT, IT, as well as the explosively growing category of visual data — has never been more vital. It is however not the data alone that holds the keys to value, but our ability to understand and operationalize the data. This is why data about data — or metadata — is the new black.
Embedding AI at the core of new data management
Current state of metadata management by data management teams certainly leaves room for improvement.
Whilst the potential for augmentation and machine learning are not yet fully realized, expectations in the market are accelerating. Finding relationships in combinations of diverse data using AI techniques to deliver a rich layer of active metadata is increasingly understood as the foundation of modern data and analytics.
Digitalization leaders are turning their attention in particular to:
- Transfer of metadata ownership from the CIO to the Chief Data Officer (CDO), or similar role, to bring mission critical metadata closer to data consumers
- Enhancement of the scope of metadata through automation (ML) and through automated enrichment by semantic search capabilities and SME crowdsourcing
- Development of shared understanding across multiple domains for new ways to capture, activate and visualize metadata
Old MDM is dying
Metadata and master data management are certainly not new concepts. Very much the opposite. Ask anyone outside IT, and the primary association is likely with dreaded multi-year corporate IT projects, large cost overruns, plenty of “GRC jargon” — and most definitely — complete detachment from use cases and data consumer involvement.
Conventional MDM is failing to adapt to the new data consumer era that is driven business buyers, and catalysed by ongoing proliferation of citizen data engineers, citizen data scientist and citizen developers — all inside business functions. Below is an overview of metadata pain points highlighted by Gartner (2020).
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Start by asking: Does my data speak human?
Are you meeting the needs of your citizen data scientists, citizen data integrators and business/data analysts — end users who are not IT coding experts or experienced data engineers? Get in touch with a Cognite Data Fusion® product expert to learn how you can teach your data to speak human today.