Data has always been at the core of a business, which explains the importance of data and analytics as core business functions that often need to be addressed due to a lack of strategic decisions. This factor gives rise to a new technology of stitching data using data fabrics and data mesh, enabling reuse and augmenting data integration services and data pipelines to deliver integration data.
Further, data fabric can be combined with data management, integration, and core services staged across multiple deployments and technologies.
This article will comprehend the value of data fabric architecture in the modern business environment and some key pillars that data and analytics leaders must know before developing modern data management practices.
The Evolution of Modern Data Fabric Architecture
Data management agility has become a vital priority for IT organizations in this increasingly complex environment. Therefore, to reduce human errors and overall expenses, data and analytics (DA) leaders need to shift their focus from traditional data management practices and move towards modern and innovative AI-driven data integration solutions.
In the modern world, data fabric is not just a combination of traditional and contemporary technologies but an innovative design concept to ease the human workload. With new and upcoming technologies such as embedded machine learning (ML), semantic knowledge graphs, deep learning, and metadata management, DA leaders can develop data fabric designs that will optimize data management by automating repetitive tasks.
Key Pillars of a Data Fabric Architecture
Implementing an efficient data fabric architecture needs various technological components such as data integration, data catalog, data curation, metadata analysis, and augmented data orchestration. Working on the key pillars below, DA leaders can create an efficient data fabric design to optimize data management platforms.
Collect and Analyze All Forms of Metadata
To develop a dynamic data fabric design, DA leaders need to ensure that the contextual information is well connected to the metadata, enabling the data fabric to identify, analyze, and connect to all kinds of business mechanisms, such as operational, business processes, social, and technical.
Convert Passive Metadata to Active Metadata
IT enterprises need to activate metadata to share data without any challenges. Therefore, the data fabric must continuously analyze available metadata for the KPIs and statistics and build a graph model. When graphically depicted, DA leaders can easily understand their unique challenges and work on making relevant solutions.
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