What Managers Need to Know About Data Fabric Architecture
If you have never heard of it before, you will certainly not know why data fabric architectures are relevant in this data-centric world. So, why do we need it? The primary reason is to harness the power of integrated, secured, protected data, irrespective of source or location. And this has become extremely important with the exponential data deluge that characterizes any kind of online interaction today. For instance, in 2010, the global data volume stood at two zettabytes, but it is projected to reach 181 zettabytes by 2025 (1). As organizations, businesses, and even governments become increasingly data-driven, managing and extracting meaningful insights from such vast amounts of data presents significant challenges.
This is where data fabric architectures come into play. They facilitate seamless access, integration, and analysis of data, regardless of where or how it is stored. But what exactly is data fabric architecture? To what attributes does it owe its novel status? More importantly, how can business leaders harness its herculean potential?
What is Data Fabric Architecture?
Data fabric architectures present a proactive and highly sophisticated approach to data management. In essence, it showcases a composable structure, offering a panoramic view of data by enabling real-time access to distributed data across various platforms. Unlike traditional centralized storage systems, data fabric does not rely on a single data warehouse or data lake to manage information. Instead, it handles diverse data types, whether structured or unstructured. Furthermore, it does not demand the data to be in any specific format before it can be accessed.
A key feature of data fabric is its decentralized nature. It allows organizations to manage and integrate distributed data more efficiently. By employing metadata-driven and event-based design, data fabric automates much of the data integration process. This means that once the metadata is captured, the system uses it to understand data patterns, lineage, and relationships. In turn, this enables cross-operability between different platforms and systems. Moreover, unlike traditional systems that require a centralized storage layer to manage data, data fabric offers greater flexibility and scalability. Additionally, it supports a variety of data management styles, from analytics to operational workflows, making it an adaptable solution across industries.
1. From Data Warehouse to Data Fabric Architectures: The Evolution in Data Management
Data management has evolved through several stages, with each new system addressing the limitations of the previous one.
Enterprise data warehouses were the first step in offering centralized storage for structured data. However, this approach often struggled with unstructured data. To address the limitations of data warehouses, data lakes emerged which allowed the storage of structured, semi-structured, and unstructured data in its raw form. However, in the absence of modeling data upfront, lakes ended up creating unusable and context-devoid data, often referred to as “data swamps”.To overcome these challenges, data lakehouses were developed. This new system combined the strengths of both technologies. For instance, it supported a wide variety of data types while enabling analytics and data science applications. However, lakehouses still face challenges in handling the complexities of decentralized environments, especially with the growing use of multi-cloud and hybrid cloud systems.
This has now led us to data fabric architectures. Unlike previous systems, data fabric embraces decentralized data management. Consequently, it enabled organizations to connect and integrate data across diverse environments—whether on-premise, in the cloud, or across multiple clouds. It provides real-time access to trusted data, breaks down data silos, and automates IT-intensive processes. Hence, it proves more scalable and adaptable to modern business environments.
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2. The Benefits of Data Fabric
As Sonia Mezzetta—the programme executive director of IBM’s data fabric product management team—states in her seminal work titled Principles of Data Fabric, data fabric architectures present four primary benefits:
A. Eliminates Data Silos: Breaks down the barriers between different data environments, whether cloud, multi-cloud, hybrid, or on-premise. Thus, offering a connected view of distributed data enables actionable insights from across all these environments.
B. Promotes Data Democratization: Makes it easier for non-technical users to access and leverage data through self-service data access. Consequently, this shorter path to business value reduces friction. Furthermore, it speeds up the decision-making process and enables organizations to react quickly to market changes.
C. Ensures Data Security and Reliability: Employs automated data governance and knowledge management, ensuring that data is both secure and trusted.
D. Supports Both Business and Technical Users: Provides intuitive tools for business users to discover and understand data while simultaneously addressing the complex needs of technical users. As a result, it supports various data processing techniques, such as batch, real-time, ETL/ELT, data virtualization, and streaming.
What is the Structure of Data Fabric Architecture?
The structure of data fabric architectures includes several essential components that work in harmony to manage and distribute data across various environments:
- The Data Governance and Security Layer ensures proper oversight and protection of data, managing compliance and security measures across the entire system
- The Data Integration Layer at this stage, data from various sources, both structured and unstructured, is brought together, and connections between different data types are identified
- The Data Refinement Layer filters and refines the data, ensuring that only relevant and valuable information is made available for extraction
- The Data Coordination Layer handles data transformation, integration, and cleansing, preparing it for further usage in the organization
- The Data Exploration Layer finds openings for connecting diverse data sources (i.e., linking data from inventory systems with customer databases, facilitating new insights, etc.)
- The Data Access and Permission Layer ensures authorized users can access necessary data while maintaining regulatory compliance
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What is an Example of Data Fabric?
To better understand how data fabric works, let’s consider the example of an imaginary online retail company. Let’s assume this company operates in multiple regions, each storing its data in different formats and platforms—some on-premise, others in the cloud.
First, the company’s sales data, inventory levels, customer service logs, and website activity are distributed across these various environments. Now, this diverse information is efficiently brought together by the data fabric system. In turn, this enables the company to view its operations in real time.
The connections between these diverse datasets is established with the help of metadata. Consequently, it ensures that the data from customer service logs can be linked to purchase history irrespective of location.
The business analysts of the company in question would utilize self-service tools to analyze this assorted data. Here, the data fabric platform automatically cleans and prepares the data for this. Furthermore, it offers real-time insights into sales trends, inventory demands, and customer service performance.
Finally, with built-in governance protocols, the company ensures that only authorized employees can access sensitive customer information. Thus, it safeguards its data while maintaining full compliance with data privacy regulations.
How Can Managers Use Data Fabric for Data Management?
As organizations grapple with ever-growing data complexities, managers must leverage cutting-edge solutions to optimize data handling and decision-making. Data fabric architecture offers a seamless way to manage data across different environments. This technology allows managers to streamline operations, enhance data accessibility, and make informed, data-driven decisions in real-time.
The Professional Certificate Programme in Advanced Data Analytics for Managers, offered by IIM Kozhikode and brought to you by Emeritus, provides a unique module to equip managers with the skills to master a diverse range of data management solutions and lead data-driven initiatives successfully.
Programme Highlights:
- Comprehensive Learning: Gain a deep understanding of advanced data analytics techniques to manage, visualize, and analyze data
- Hands-on Training: Practical exposure to real-world case studies and business scenarios
- Capstone Project: An opportunity to implement learnings and derive actionable insights from complex data sets
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By integrating data fabric into their management strategy, managers can effectively break down data silos, streamline processes, and enhance business intelligence. This programme will enable them to harness data for better operational efficiency and strategic planning. It is the expertise you need to scale up business operations—and your career.
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