CIO OPINION language to all data users to ensure that all metadata can be universally understood and used . In understanding and taking advantage of this valuable metadata , CIOs will facilitate more effective teamwork and informed decision-making . This is now possible through using new AI technology in the form of a Semantic AI platform , which can provide a common metadata vocabulary for data users , from taxonomists to ontologists , data architects and developers within large enterprises ; while at the same time bringing this metadata closer to the business experts driving innovation forward , faster .
The evolution of metadata management
The practice of processing all this metadata – metadata management – has evolved , just as the type of metadata itself has changed . To most data architects , metadata management means defining and leveraging information about the data itself to achieve better data quality , governance , unity , reliability and security . However , in 2021 Gartner ’ s Market Guide for Active Metadata Management pronounced traditional metadata practices as ‘ insufficient ’ to meet the needs of the enterprise , due to the transformational capabilities of active metadata . sciences company , standardised metadata fields for clinical trials ( e . g . agent dose , agent administered time , disease recurrence type , etc .) can help ensure that data is consistent , shareable and compliant with regulations . This gives pharmaceutical team members greater transparency into data , facilitating better communication , deeper insights and faster results .
How Semantic AI can standardise data interpretation
To embrace the metadata evolution and ensure that their data is universally understood and used , organisations must create data harmonisation and offer a common language to all data users , which will facilitate effective teamwork and informed decisionmaking . This data harmonisation is achieved through new AI technology in the form of Semantic AI platforms .
A Semantic AI data platform unifies data with its metadata to establish a single data resource . It creates and manages active metadata , allowing data leaders to integrate , store , manage , govern , contextualise and surface data regardless of format , schema , or type . It also employs Semantic AI to synthesise , enrich , extract and harmonise all types of metadata .
What determines metadata to be ‘ active ’ or passive can change as it moves through the information supply chain . Essentially , passive metadata is information about data – system-applied dates , creator , source semantic metadata . It describes the meaning of data topics , product , geography , audience , concepts and relationships . Active and augmented metadata is intelligent and dynamic data , for instance facts , status , personal identifiable information ( PII ), data orchestration and ML analytics .
Metadata-driven insights will soon be table stakes for CIOs to make effective transformations and directly enhance collaboration and business consistency .
To date , approaches to take advantage of active metadata have included deploying Machine Learning ( ML ) and / or business-oriented AI algorithms to automate metadata management through an AI / ML-powered data platform which triggers automated actions or proactive recommendations for more informed decisions . However , this approach is no longer enough . Separate layers suggest custom integration between different vendor products which carries a consequential risk of versioning , compatibility and loss of capability .
Metadata should now be addressable as data from a single data platform without compromise . This means only optimal metadata management can eliminate the effect of data and knowledge silos , deliver data agility , enhance collaboration and accelerate insightful business decision-making . For example , at a life
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