English 
Vietnamese Both businesses and individuals generate and manage an overwhelming amount of data. So much so that even storage solutions like data marts or data warehouses are now unable to hold. Businesses today need something "deeper."
This highlights the increasing importance of a proper, bigger, more advanced, and centralised data management platform for efficient decision-making.
With an ever-increasing array of enterprise applications, APIs, devices, and users, having a big data platform to centralise the entirety of your data portfolio helps organisations start their journey from analytics to predictive AI applications.
Read more: Data Management vs Information Management - What You Need to Know
Contents
What is data fabric?
Data fabric is a comprehensive data integration and management solution comprising architecture, data management, integration software, and shared data, empowering organisations to effectively leverage their data assets.
Data fabric provides unified, consistent access to all users across the organisation in real time, helping them solve complex data problems, and acquire relevant information, regardless of where it is stored.
The result? Seamless access and data sharing in a centralised and secure environment for all data needs.
Read more: When Does Your Business Need a Data Management Platform?
Complementary components of a data fabric include application programming interfaces (APIs) and software development kits (SDKs) designed to provide front-end users with access to data and insights through familiar tools like business intelligence (BI), reporting, and visualisation software.
This brings together vast amounts of complex and diverse data from multiple sources, including data lakes and data warehouses. However, its utility extends beyond data collection and storage; it also includes built-in machine learning and analytics capabilities that allow for rapid and scalable data transformation and processing.
According to Gartner, data fabric applies continuous analytics to existing metadata assets, facilitating the identification and connection of data from disparate applications to establish business-relevant relationships.
Yet, while data fabric presents a wide view of data and enhanced data utilisation, its implementation and maintenance also pose several challenges that cannot be ignored. Developing a secure data fabric solution that seamlessly integrates with all relevant data sources and platforms demands specialised skills and expertise, often necessitating a substantial IT budget allocation.
With the advantages of a unified, real-time data view and facilitating the integration of data management processes across various sources like hybrid cloud environments, web applications, and edge devices, a data fabric can help businesses reduce data silos and expand data accessibility to a wider user base.
Data Fabric vs. Data Lakes vs. Data Warehouses
|
Data Fabric |
Data Lakes |
Data Warehouses |
|
|
Data architecture |
Unified platform for seamless data integration across IT infrastructure that can break down data silos. |
Repositories for raw, unstructured data from various sources that embrace a "store now, process later" approach, allowing for the storage of data in its original format until required for analysis or reporting purposes. |
Structured repositories designed to store transformed and structured data ready for analysis and reporting and extracted from transactional systems and quantitative metrics. |
|
Data access |
Real-time access to a comprehensive view of data. |
Flexible access to raw data, requiring further transformation. |
Structured data ready for analysis and reporting. |
|
Data transformation |
Features built-in machine learning and analytics for rapid data processing. |
On-the-fly reconfiguration due to raw data storage. |
Transformation is required for structured insights. |
|
Data governance |
Ensures appropriate data access and governance restrictions. |
Requires strict attention to data governance and metadata management. |
Adheres to the structured data model, facilitating efficient reporting to warrant access controls and regulatory compliance. |
|
Flexibility |
Offers flexibility for evolving business needs and scalability. |
Offers flexibility in storing and accessing raw data, supporting experimentation. |
Limited flexibility due to rigid structure and schema modification processes. |
|
Complexity |
Requires specialised skills and significant IT budget allocation. |
Management complexity due to attention to data governance and metadata. |
Schema modification processes can be laborious and slow as they require careful planning and management of data modeling and ETL processes. |
|
Suitability |
Ideal for organisations seeking unified, real-time data view and insights. |
Suited for businesses prioritising flexibility and cost-effectiveness. |
Best suited for structured insights for operational and analytical use cases. |
Read more: 5 Use Cases of Data Lakes that You Probably Did Not Know
Infor Data Fabric on Infor OS
Infor Data Fabric is a cloud-native data platform with a range of features, such as big data storage, query interfaces, real-time data delivery, and a suite of advanced data management tools, which are instrumental in structuring the extensive data generated by CloudSuite applications.
Below are the key components and functionalities of Infor Data Fabric:
- Converged data storage platform: Infor Data Fabric provides enterprises with a converged data storage platform, eliminating the need for upfront, complex, and costly data transformation. Enterprises can store their data as-is, preserving its integrity and ensuring efficient data management across diverse sources and formats.
- Data unification and clarity: With Infor Data Fabric, enterprises gain access to powerful tools for managing petabyte-level data repositories. These tools enable organisations to gain deep insights into the structure and content of their data, facilitating informed decision-making and strategic planning.
- Operational monitoring: Infor Data Fabric offers clear visibility into data generation, traffic patterns, and data consumer behaviors. Application administrators and content curators can monitor data flows in real-time, ensuring optimal performance and resource allocation.
- Data dictionary: Infor Data Fabric addresses the common challenge of semantic governance by providing a comprehensive data dictionary. This semantic governance framework ensures that data is accurately labeled, documented, and easily accessible, preventing delays and ensuring the success of big data projects.
- Secured confidence: Security is paramount in enterprise data management, and Infor Data Fabric leverages the robust security principles and capabilities of Infor OS. Data is secured at-rest and in-transit, ensuring confidentiality, integrity, and availability while adhering to regulatory compliance requirements.
- Knowledge virtualisation: Infor Data Fabric facilitates knowledge virtualization by delivering domain-specific content to applications and functional teams. By deeply understanding data models, relationships, and value creation opportunities, organisations can unlock the full potential of their data assets and drive innovation across disparate datasets.
Ready to reform your enterprise data management? Download the Infor OS brochure now to discover how Infor Data Fabric, seamlessly integrated with Infor OS, can empower your organisation to unleash the full potential of your data assets and expedite business growth.
